This PDF file contains the front matter associated with SPIE Proceedings Volume 10188 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Passive coherent location (PCL) is a radar technique, in which the system uses reflections from opportunistic illumination sources in the environment for detection and tracking. Typically, PCL uses civilian communication transmitters not ideally suited for radar. The physical geometry of PCL is developed on the basis of bistatic radar without control of the transmitter antenna or waveform design. This poses the problem that often the receiver is designed with two antennas and channels, one for reference and one for surveillance. The surveillance channel is also contaminated with the direct signal and thus direct signal suppression (DSS) techniques must be used. This paper proposes an analytical solution based around hardware for DSS which is compared to other methods available in the literature. The methods are tested in varying bistatic geometries and with varying target radar cross section (RCS) and signal-to-noise ratio (SNR).

Passive Coherent Location (PCL) is a developing radar technique, in which the system processes reflections from opportunistic illumination sources in the environment for detection and tracking. Many developments and improvements of PCL implement pseudo-monostatic and bistatic radar configurations; however, with the proliferation of commercial communication systems, the spectrally dense environment suggests the use of a heterogenous multistatic PCL system. This paper develops error minimization criteria to adjust and optimize available resources to a wideband PCL receiver. The method introduces the concept of self ambiguity as an error metric and implements this as a criterion to test varying PCL scenarios with differing transmitter modulation waveforms. The paper compares this to available techniques and the global minimum error available.

Current airborne radar systems segment the available time-on-target during each beam dwell into multiple Coherent Processing Intervals (CPIs) in order to eliminate range eclipsing, solve for unambiguous range, and increase the detection performance against larger Radar Cross Section (RCS) targets. As a consequence, these radars do not realize the full Signal-to-Noise Ratio (SNR) increase and detection performance improvement that is possible. Continuous High Pulse Repetition Frequency (HPRF) waveforms and processing enables the coherent integration of all available radar data over the full time-on-target. This can greatly increase the SNR for air targets at long range and/or with weak radar returns and significantly improve the detection performance against such targets. TSC worked with its partner KeyW to implement a Continuous HPRF waveform in their Sahara radar testbed and obtained measured radar data on both a ground vehicle target and an airborne target of opportunity. This experimental data was processed by TSC to validate the expected benefits of Continuous HPRF waveforms.

Change detection using very high resolution SAR images is an important source of information for reconnaissance applications. Modern SAR sensors are capable of acquiring many images in short periods of time, which creates the need for a reliable automatic change detection method. In this paper, we will describe a new automatic change detection approach that combines very high resolution SAR images with prior knowledge about the imaged scene. In this case, the prior knowledge about the scene will come from vector maps, which can be obtained from a Geographic Information System (GIS). These vector maps will allow us to determine which regions are of interest for the change detection, and what kind of changes/objects can be expected there. The algorithm described in this paper will be applied to a time series of high resolution TerraSAR-X images of a port with military shipyards, and used to automatically detect ship activity and extract information about the detected ships. In this case, the vector maps were obtained from a Geographic Information System (GIS) containing map data from OpenStreetMap

Experiences are shared from a recent radar measurement and characterization effort. A regimented data collection procedure ensures repeatability and provides an expedited alternative to typical narrowband capabilities. Commercially-available instrumentation is repurposed to support wideband data collections spanning a contiguous range of frequencies from 700 MHz to 40 GHz. Utilizing a 4-port network analyzer, both monostatic and quasi-monostatic measurements are achievable. Polarization is varied by way of a custom-designed antenna mount that allows for the mechanical reorientation of the antennas. Computational electromagnetic modeling is briefly introduced and serves in validating the legitimacy of the collection capability. Data products presented will include high-range resolution profiles and inverse synthetic aperture radar (ISAR) imagery.

Due to challenges in the visual interpretation of radar signatures or in the subsequent information extraction, a fusion with other data sources can be beneficial. The most accurate basis for a fusion of any kind of remote sensing data is the mapping of the acquired 2D image space onto the true 3D geometry of the scenery. In the case of radar images this is a challenging task because the coordinate system is based on the measured range which causes ambiguous regions due to layover effects. This paper describes a method that accurately maps the detailed 3D information of a scene to the slantrange-based coordinate system of imaging radars. Due to this mapping all the contributing geometrical parts of one resolution cell can be determined in 3D space. The proposed method is highly efficient, because computationally expensive operations can be directly performed on graphics card hardware. The described approach builds a perfect basis for sophisticated methods to extract data from multiple complimentary sensors like from radar and optical images, especially because true 3D information from whole cities will be available in the near future. The performance of the developed methods will be demonstrated with high resolution radar data acquired by the space-borne SAR-sensor TerraSAR-X.

In this paper we present a new through-wall (TW) FMCW radar system. The architecture of the radar enables both high sensitivity and range resolutions of <1.5 m. Moreover, the radar employs moving target indication (MTI) signal processing to remove the problematic primary wall reflection, allowing higher signal-to- noise and signal-to-interference ratios, which can be traded-off for increased operational stand-off. The TW radar operates at 5.8 GHz with a 200 MHz bandwidth. Its dual-frequency design minimises interference from signal leakage, and permits a baseband output after deramping which is digitized using an inexpensive 24-bit off-the-shelf sound card. The system is therefore an order of magnitude lower in cost than competitor ultrawideband (UWB) TW systems. The high sensitivity afforded by this wide dynamic range has allowed us to develop a wall removal technique whereby high-order digital filters provide a flexible means of MTI filtering based on the phases of the returned echoes. Experimental data demonstrates through-wall detection of individuals and groups of people in various scenarios. Target positions were located to within ±1.25 m in range, allowing us distinguish between two closely separated targets. Furthermore, at 8.5 m standoff, our wall removal technique can recover target responses that would have otherwise been masked by the primary wall reflection, thus increasing the stand-off capability of the radar. Using phase processing, our experimental data also reveals a clear difference in the micro-Doppler signatures across various types of everyday actions

Through-the-Wall (TW) radar sensors are gaining increasing interest for security, surveillance and search and rescue applications. Additionally, the integration of Multiple-Input, Multiple-Output (MIMO) techniques with phased array radar is allowing higher performance at lower cost. In this paper we present a 4-by-4 TW MIMO phased array imaging radar operating at 2.4 GHz with 200 MHz bandwidth. To achieve high imaging resolution in a cost-effective manner, the 4 Tx and 4 Rx elements are used to synthesize a uniform linear array (ULA) of 16 virtual elements. Furthermore, the transmitter is based on a single-channel 4-element time-multiplexed switched array. In transmission, the radar utilizes frequency modulated continuous wave (FMCW) waveforms that undergo de-ramping on receive to allow digitization at relatively low sampling rates, which then simplifies the imaging process. This architecture has been designed for the short-range TW scenarios envisaged, and permits sufficient time to switch between antenna elements. The paper first outlines the system characteristics before describing the key signal processing and imaging algorithms which are based on traditional Fast Fourier Transform (FFT) processing. These techniques are implemented in LabVIEW software. Finally, we report results from an experimental campaign that investigated the imaging capabilities of the system and demonstrated the detection of personnel targets. Moreover, we show that multiple targets within a room with greater than approximately 1 meter separation can be distinguished from one another.

The effectiveness of various dynamic calibration targets emulating human respiration are analyzed. Potential advantages of these devices relate to easier calibration methods for human detection testing in through-wall and through-rubbles situations. The three devices examined possess spherical polyhedral geometries. Spherical characteristics were implemented due to the unique qualities spheres possess in regards to calibration purposes. The ability to use a device that is aspect independent is favorable during the calibration process. Rather than using a traditional, static calibration sphere, a dynamic, sphere-like device offers the ability to resemble breathing movements of the human body. This motion opens the door for numerous types of Doppler testing that is impossible in a static calibration device. Monostatic RCS simulations at 3 GHz are documented for each geometry. The results provide a visual way of representing the effectiveness of each design as a dynamic calibration target for human detection purposes.

Radar based detection of human targets behind walls or in dense urban environments is an important technical challenge with many practical applications in security, defense, and disaster recovery. Radar reflections from a human can be orders of magnitude weaker than those from objects encountered in urban settings such as walls, cars, or possibly rubble after a disaster. Furthermore, these objects can act as secondary reflectors and produce multipath returns from a person. To mitigate these issues, processing of radar return data needs to be optimized for recognizing human motion features such as walking, running, or breathing. This paper presents a theoretical analysis on the modulation effects human motion has on the radar waveform and how high levels of multipath can distort these motion effects. From this analysis, an algorithm is designed and optimized for tracking human motion in heavily clutter environments. The tracking results will be used as the fundamental detection/classification tool to discriminate human targets from others by identifying human motion traits such as predictable walking patterns and periodicity in breathing rates. The theoretical formulations will be tested against simulation and measured data collected using a low power, portable see-through-the-wall radar system that could be practically deployed in real-world scenarios. Lastly, the performance of the algorithm is evaluated in a series of experiments where both a single person and multiple people are moving in an indoor, cluttered environment.

In this paper, we discuss the effect that photon polarization has on the quantum radar cross section (QRCS) during the special case scenario of when the target is enveloped in either a uniform electric field or magnetic field and all of its atomic electric/magnetic dipole moments become aligned (target polarization). This target polarization causes the coupling between the photon and the matter to change and alter the scattering characteristics of the target. Most notably, it causes scattering to be very near zero at a specified angle. We also investigate the relationship between electric and magnetic types of coupling and find that the electric contribution dominates the QRCS response.

In previous research we designed an interferometric quantum seismograph that uses entangled photon states to enhance sensitivity in an optomechanic device. However, a spatially-distributed array of such sensors, with each sensor measuring only nm-vibrations, may not provide sufficient sensitivity for the prediction of major earthquakes because it fails to exploit potentially critical phase information. We conjecture that relative phase information can explain the anecdotal observations that animals such as lemurs exhibit sensitivity to impending earthquakes earlier than can be done confidently with traditional seismic technology. More specifically, we propose that lemurs use their limbs as ground motion sensors and that relative phase differences are fused in the brain in a manner similar to a phased-array or synthetic-aperture radar. In this paper we will describe a lemur-inspired quantum sensor network for early warning of earthquakes. The system uses 4 interferometric quantum seismographs (e.g., analogous to a lemurs limbs) and then conducts phase and data fusion of the seismic information. Although we discuss a quantum-based technology, the principles described can also be applied to classical sensor arrays

The study of plate tectonic motion is important to generate theoretical models of the structure and dynamics of the Earth. In turn, understanding tectonic motion provides insight to develop sophisticated models that can be used for earthquake early warning systems and for nuclear forensics. Tectonic geodesy uses the position of a network of points on the surface of earth to determine the motion of tectonic plates and the deformation of the earths crust. GPS and interferometric synthetic aperture radar are commonly used techniques used in tectonic geodesy. In this paper we will describe the feasibility of interferometric synthetic aperture quantum radar and its theoretical performance for tectonic geodesy.

Synthetic aperture radar (SAR) uses sensor motion to generate finer spatial resolution of a given target area. In this paper we explore the theoretical potential of quantum synthetic aperture quantum radar (QSAR). We provide theoretical analysis and simulation results which suggest that QSAR can provide improved detection performance over classical SAR in the high-noise low-brightness regime.

The precise navigation of underwater vehicles is a difficult task due to the challenges imposed by the variable oceanic environment. It is particularly difficult if the underwater vehicle is trying to navigate under the Arctic ice shelf. Indeed, in this scenario traditional navigation devices such as GPS, compasses and gyrocompasses are unavailable or unreliable. In addition, the shape and thickness of the ice shelf is variable throughout the year. Current Arctic underwater navigation systems include sonar arrays to detect the proximity to the ice. However, these systems are undesirable in a wartime environment, as the sound gives away the position of the underwater vehicle. In this paper we briefly describe the theoretical design of a quantum imaging system that could allow the safe and stealthy navigation of underwater Arctic vehicles.

In this paper we discuss and examine approaches for detecting large objects in low-light maritime environments with a goal of improving the detection of large targets within a region of interest. More specifically, a passive ghost imaging system is proposed for using caustics illumination patterns to reconstruct a target image from correlations with intensities captured by a bucket detector.

A network based on quantum information has been developed to improve sensing and communications capabilities. Quantum teleportation offers features for communicating information not found in classical procedures. It is fundamental to the quantum network approach. A version of quantum teleportation based on hyper-entanglement is used to bring about these improvements. Recently invented methods of improving sensing and communication via quantum information based on hyper-entanglement are discussed. These techniques offer huge improvements in the SNR, signal to interference ratio, and time-on-target of various sensors including RADAR and LADAR. Hyper-entanglement refers to quantum entanglement in more than one degree of freedom, e.g. polarization, energy-time, orbital angular momentum (OAM), etc. The quantum network makes use of quantum memory located in each node of the network, thus the network forms a quantum repeater. The quantum repeater facilitates the use of quantum teleportation, and superdense coding. Superdense coding refers to the ability to incorporate more than one classical bit into each transmitted qubit. The network of sensors and/or communication devices has an enhanced resistance to interference sources. The repeater has the potential for greatly reducing loss in communications and sensor systems related to the effect of the atmosphere on fragile quantum states. Measures of effectiveness (MOEs) are discussed that show the utility of the network for improving sensing and communications in the presence of loss and noise. The quantum repeater will reduce overall size, weight, power and cost (SWAPC) of fielded components of systems.

The U.S. Army Research Laboratory (ARL) recently designed and tested a new prototype radar, the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar system, based on a stepped-frequency architecture to address issues associated with our previous impulse-based radars. This is a low-frequency ultra-wideband (UWB) radar with frequencies spanning from 300 to 2000 MHz. Mounted on a vehicle, the radar can be configured in either sidelooking or forward-looking synthetic aperture radar (SAR) mode. We recently conducted our first experiment at Yuma Proving Grounds (YPG). This paper summarizes the radar configurations, parameters, and SAR geometry. The radar data and other noise sources, to include the self-interference signals and radio-frequency interference (RFI) noise sources, are presented and characterized in both the raw (pre-focus) and SAR imagery domains. This paper also describes our signal processing techniques for extracting noise from radar data, as well as the SAR imaging algorithms for forming SAR imagery in both forward- and side-looking modes. Finally, this paper demonstrates our spectral recovery technique and results for a radar operating in a spectrally restricted environment.

A miniature half cross dipole antenna for defense and aerospace RFID applications in UHF band is presented. The dipole printed line arms are half crossed shape on top of dielectric substrate backed by reactive impedance surface. The antenna fed by a coaxial cable at the gap separating the dipole arms. Our design is intended to work at 2.42 GHz for RFID readers. The radiation pattern obtained has HPBW of 112, return loss of 22.24 dB and 90 MHz bandwidth.

Fully-polarimetric X-band (9.6 GHz center frequency) VideoSAR with 0.125-meter ground resolution flew collections before, during, and after the fifth Source Physics Experiment (SPE-5) underground chemical explosion. We generate and exploit synthetic aperture RADAR (SAR) and VideoSAR products to characterize surface effects caused by the underground explosion. To our knowledge, this has never been done. Exploited VideoSAR products are “movies” of coherence maps, phase-difference maps, and magnitude imagery. These movies show two-dimensional, time-varying surface movement. However, objects located on the SPE pad created unwanted, vibrating signatures during the event which made registration and coherent processing more difficult. Nevertheless, there is evidence that dynamic changes are captured by VideoSAR during the event. VideoSAR provides a unique, coherent, time-varying measure of surface expression of an underground chemical explosion.

With the rapid proliferation of small unmanned aerial systems (UAS) in the national airspace, small operational drones are being sometimes considered as a security threat for critical infrastructures, such as sports stadiums, military facilities, and airports. There have been many civilian counter-drone solutions and products reported, including radar and electromagnetic counter measures. For the current electromagnetic solutions, they are usually limited to particular type of detection and counter-measure scheme, which is usually effective for the specific type of drones. Also, control and communication link technologies used in even RC drones nowadays are more sophisticated, making them more difficult to detect, decode and counter. Facing these challenges, our team proposes a “software-defined” solution based on noise and LPI radar. For the detection, wideband-noise radar has the resolution performance to discriminate possible micro-Doppler features of the drone versus biological scatterers. It also has the benefit of more adaptive to different types of drones, and covertly detecting for security application. For counter-measures, random noise can be combined with “random sweeping” jamming scheme, to achieve the optimal balance between peak power allowed and the effective jamming probabilities. Some theoretical analysis of the proposed solution is provided in this study, a design case study is developed, and initial laboratory experiments, as well as outdoor tests are conducted to validate the basic concepts and theories. The study demonstrates the basic feasibilities of the Drone Detection and Mitigation Radar (DDMR) concept, while there are still much work needs to be done for a complete and field-worthy technology development.

The coherence matrix from the scattering matrix of a single look polarimetric SAR pixel will have an entropy of zero with the main eigenvalue being equal to the span and the other two eigenvalues being equal to zero. Each scattering matrix element from terrain scatter is a coherent sum from a large number of scatterers in a resolution cell. Entropy/alpha decomposition is only possible where the coherency matrix elements are determined from ensemble covariances. This is the case for multilook polarimetric SAR data where covariances from the exact same collection of scatterers are averaged using separate extraction filters in the SAR doppler direction. We report interesting observations from analysis of multilook SIR-C data at L and C bands from different oceans around the globe. We present a strategy for segmenting single look polarimetric TerraSAR ocean data using an algorithm we have previously developed with the averaging of the resulting like pixels used to generate coherence matrices. We give a brief discussion of desert surfaces.

The phase noise problem or Rayleigh problem occurs in all aspects of radar. It is an effect that a radar engineer or physicist always has to take into account as part of a design or in attempt to characterize the physics of a problem such as reverberation. Normally, the mathematical difficulties of phase noise characterization are avoided by assuming the phase noise probability distribution function (PDF) is uniformly distributed, and the Central Limit Theorem (CLT) is invoked to argue that the superposition of relatively few random components obey the CLT and hence the superposition can be treated as a normal distribution. By formalizing the characterization of phase noise (see Gray and Alouani) for an individual random variable, the summation of identically distributed random variables is the product of multiple characteristic functions (CF). The product of the CFs for phase noise has a CF that can be analyzed to understand the limitations CLT when applied to phase noise. We mirror Kolmogorov's original proof as discussed in Papoulis to show the CLT can break down for receivers that gather limited amounts of data as well as the circumstances under which it can fail for certain phase noise distributions. We then discuss the consequences of this for matched filter design as well the implications for some physics problems.

The new challenges originated from Digital Array Radar (DAR) demands a new generation of reconfigurable backend processor in the system. The new FPGA devices can support much higher speed, more bandwidth and processing capabilities for the need of digital Line Replaceable Unit (LRU). This study focuses on using the latest Altera and Xilinx devices in an adaptive beamforming processor. The field reprogrammable RF devices from Analog Devices are used as analog front end transceivers. Different from other existing Software-Defined Radio transceivers on the market, this processor is designed for distributed adaptive beamforming in a networked environment. The following aspects of the novel radar processor will be presented: (1) A new system-on-chip architecture based on Altera’s devices and adaptive processing module, especially for the adaptive beamforming and pulse compression, will be introduced, (2) Successful implementation of generation 2 serial RapidIO data links on FPGA, which supports VITA-49 radio packet format for large distributed DAR processing. (3) Demonstration of the feasibility and capabilities of the processor in a Micro-TCA based, SRIO switching backplane to support multichannel beamforming in real-time. (4) Application of this processor in ongoing radar system development projects, including OU’s dual-polarized digital array radar, the planned new cylindrical array radars, and future airborne radars.

Binary phased codes have many applications in communication and radar systems. These applications require binary phased codes to have low sidelobes in order to reduce interferences and false detection. Barker codes are the ones that satisfy these requirements and they have lowest maximum sidelobes. However, Barker codes have very limited code lengths (equal or less than 13) while many applications including low probability of intercept radar, and spread spectrum communication, require much higher code lengths. The conventional techniques of finding binary phased codes in literatures include exhaust search, neural network, and evolutionary methods, and they all require very expensive computation for large code lengths. Therefore these techniques are limited to find binary phased codes with small code lengths (less than 100). In this paper, by analyzing Barker code, linear chirp, and P3 phases, we propose a new approach to find binary codes. Experiments show that the proposed method is able to find long low sidelobe binary phased codes (code length >500) with reasonable computational cost.

Since the inception of coherent waveforms, it has been realized that the effect of the motion of a non-point like object can induce structure in the return spectrum of the waveform (Gray and Addison, Gray et-al). There are many ways to think about this, but a newer one is to think of the law of motion induced onto the scattered waveform as being "generated" by an operator to produce the law of motion (Gray and Parks). One can use this observation to design a receiver's matched filter response specific to these type of operators that generate a law of motion. We use Poisson bracket method for generating a Taylor series solution to a functional equation to illustrate how to do this. In addition, one can also do the same for three dimensional periodic motion using the rotation matrices. We illustrate this by developing the matched filter response for a rotating object in terms of the rotation matrix.

A 24 GHz medium-range human detecting sensor, using the Doppler Radar Physiological Sensing (DRPS) technique, which can also detect unmanned aerial vehicles (UAVs or drones), is currently under development for potential rescue and anti-drone applications. DRPS systems are specifically designed to remotely monitor small movements of non-metallic human tissues such as cardiopulmonary activity and respiration. Once optimized, the unique capabilities of DRPS could be used to detect UAVs. Initial measurements have shown that DRPS technology is able to detect moving and stationary humans, as well as largely non-metallic multi-rotor drone helicopters. Further data processing will incorporate pattern recognition to detect multiple signatures (motor vibration and hovering patterns) of UAVs.

This paper discusses the micro-Doppler signatures of small UAVs obtained from a millimeter-wave radar system. At first, simulation results are shown to demonstrate the theoretical concept. It is illustrated that whilst the propeller rotation rate of the small UAVs is quite high, millimeter-wave radar systems are capable of capturing the full micro-Doppler spread. Measurements of small UAVs have been performed with both CW and FMCW radars operating at 94 GHz. The CW radar was used for obtaining micro-Doppler signatures of individual propellers. The field test data of a flying small UAV was collected with the FMCW radar and was processed to extract micro-Doppler signatures. The high fidelity results clearly reveal features such as blade flashes and propeller rotation modulation lines which can be used to classify targets. This work confirms that millimeter-wave radar is suitable for the detection and classification of small UAVs at usefully long ranges.

The appearance of small UAVs on the commercial market poses a real threat to both civilian safety and to military operations. In open terrain a radar can detect and track even small UAVs at long distances. In an urban environment with limited line-of-sight and strong static and non-static background, this capability can be severely reduced. The radar cross section of these UAVs are normally small compared to the background. However, the rotors of the UAVs produce a characteristic micro-Doppler signature that can be exploited for detection and classification. In this paper, we investigate in an experimental set-up whether it is possible in the radar non-line-of-sight to retrieve the micro-Doppler signature of the UAV rotors. This is done by exploring up to three multipath bounces in the measured signal. The measurements were made with a semi-monostatic single receiver-transmitter radar system operating at X-band in a pulsed single frequency mode. The radar response of the UAV, with plastic and metallic rotors, was measured at several positions inside a 4 m wide corridor with metallic walls. In this paper, data from one line-of-sight and two non-line-ofsight positions are presented. Results show that we are able to detect the micro-Doppler of the rotors and to retrieve the number of revolutions per minute, for both rotor types. Free space Finite-Difference Time-Domain calculations have also been performed on a CAD-model of the UAV rotor to determine the optimal choice of polarization and the short-time Fourier transform filter length.

Remote detection of human aquatic activity can be applied not only to ocean surveillance but also to rescue operations. When a human is illuminated by electromagnetic waves, a Doppler signal is generated from his or her moving parts. Indeed, bodily movements are what make humans’ micro-Doppler signatures unique, offering a chance to classify human motions. Certain studies have analyzed and attempted to recognize human aquatic activity, but the topic has yet to be extensively studied. In the present research, we simulate the micro-Doppler signatures of a swimming person in an attempt to investigate those signatures’ characteristics. We model human arms as point scatterers while assuming a simple arm motion. By means of such a simulation, we can obtain spectrograms from a swimming person, then extend our measurement to multiple participants. Measurements are taken from five aquatic activities featuring five participants, comprising freestyle, backstroke, and breaststroke, pulling a boat, and rowing. As suggested by the simulation study, the spectrograms for the five activities show different micro-Doppler signatures; hence, we propose to classify them using a deep convolutional neural network (DCNN). In particular, we suggest the use of a transfer-learned DCNN, which is based on a DCNN pretrained by a large-scale RGB image dataset that is, ImageNet. The classification accuracy is calculated using fivefold cross-validation on our dataset. We find that a DCNN trained through transfer learning achieves the highest accuracy while also providing a significant performance boost over the conventional classification method.

Longer radio frequency waves better penetrating through high scattered media than millimeter waves, but imaging resolution limited by diffraction at longer wavelength. Same time frequency and amplitudes of diffracted waves (frequency domain measurement) provides information of object. Phase shift of diffracted waves (phase front in time domain) consists information about shape of object and can be applied for reconstruction of object shape or even image by recording of multi-frequency digital hologram. Spectrum signature or refracted waves allows identify the object content. Application of monopulse method with overlap closely spaced antenna patterns provides high accuracy measurement of amplitude, phase, and direction to signal source. Digitizing of received signals separately in each antenna relative to processor time provides phase/frequency independence. Fly eye non-scanning multi-frequency radar system provides simultaneous continuous observation of multiple targets and wide possibilities for stepped frequency, simultaneous frequency, chaotic frequency sweeping waveform (CFS), polarization modulation for reliable object detection. Proposed c-band fly eye radar demonstrated human detection through 40 cm concrete brick wall with human and wall material spectrum signatures and can be applied for through wall human detection, landmines, improvised explosive devices detection, underground or camouflaged object imaging.

We present the design and characterization of a ground-based, zenith-pointing, 94 GHz FMCW Doppler radar demonstrator for cloud profiling. The radar uses an all solid-state and relatively simple homodyne architecture and two, low sidelobe 0.5 m diameter Fresnel zone plate antennas to reduce system costs. The low-phase noise, coherent radar employs a direct digital synthesis (DDS) chip for highly linear chirp generation. The design will be able to leverage ongoing future improvements in mm-wave low noise and power amplifier technology to maximize sensitivity. Once the radar is installed in a rooftop location, the processor will perform real-time range-Doppler measurements with averaging, to yield target velocity spectra as a function of altitude

Space debris nowadays is one of the main threats for satellite systems especially in low earth orbit (LEO). More than 700,000 debris objects with potential to destroy or damage a satellite are estimated. The effects of an impact often are not identifiable directly from ground. High-resolution radar images are helpful in analyzing a possible damage. Therefor DLR is currently developing a radar system called IoSiS (Imaging of Satellites in Space), being based on an existing steering antenna structure and our multi-purpose high-performance radar system GigaRad for experimental investigations. GigaRad is a multi-channel system operating at X band and using a bandwidth of up to 4.4 GHz in the IoSiS configuration, providing fully separated transmit (TX) and receive (RX) channels, and separated antennas. For the observation of small satellites or space debris a highpower traveling-wave-tube amplifier (TWTA) is mounted close to the TX antenna feed. For the experimental phase IoSiS uses a 9 m TX and a 1 m RX antenna mounted on a common steerable positioner. High-resolution radar images are obtained by using Inverse Synthetic Aperture Radar (ISAR) techniques. The guided tracking of known objects during overpass allows here wide azimuth observation angles. Thus high azimuth resolution comparable to the range resolution can be achieved. This paper outlines technical main characteristics of the IoSiS radar system including the basic setup of the antenna, the radar instrument with the RF error correction, and the measurement strategy. Also a short description about a simulation tool for the whole instrument and expected images is shown.

We present a prototype bistatic compact radar range operating at 160 GHz and capable of collecting fullypolarimetric radar cross-section and electromagnetic scattering measurements in a true far-field facility. The bistatic ISAR system incorporates two 90-inch focal length, 27-inch-diameter diamond-turned mirrors fed by 160 GHz transmit and receive horns to establish the compact range. The prototype radar range with its modest sized quiet zone serves as a precursor to a fully developed compact radar range incorporating a larger quiet zone capable of collecting X-band bistatic RCS data and 3D imagery using 1/16th scale objects. The millimeter-wave transmitter provides 20 GHz of swept bandwidth in the single linear (Horizontal/Vertical) polarization while the millimeter-wave receiver, that is sensitive to linear Horizontal and Vertical polarization, possesses a 7 dB noise figure. We present the design of the compact radar range and report on test results collected to validate the system’s performance.

Proposed novel imaging technique will provide all weather high-resolution imaging and recognition capability for RF/Microwave signals with good penetration through highly scattered media: fog, snow, dust, smoke, even foliage, camouflage, walls and ground. Image resolution in proposed imaging system is not limited by diffraction and will be determined by processor and sampling frequency. Proposed imaging system can simultaneously cover wide field of view, detect multiple targets and can be multi-frequency, multi-function. Directional antennas in imaging system can be close positioned and installed in cell phone size handheld device, on small aircraft or distributed around protected border or object. Non-scanning monopulse system allows dramatically decrease in transmitting power and at the same time provides increased imaging range by integrating 2-3 orders more signals than regular scanning imaging systems.

The U.S. Army Research Laboratory has developed the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar, which is capable of imaging concealed/buried targets using forward- and side-looking configurations. The SAFIRE radar is vehicle-mounted and operates from 300 MHz–2 GHz; the step size can be adjusted in multiples of 1 MHz. It is also spectrally agile and capable of excising frequency bands, which makes it ideal for operation in congested and/or contested radio frequency (RF) environments. Furthermore, the SAFIRE radar receiver has a super-heterodyne architecture, which was designed so that intermodulation products caused by interfering signals could be easily filtered from the desired received signal. The SAFIRE system also includes electro-optical (EO) and infrared (IR) cameras, which can be fused with radar data and displayed in a stereoscopic augmented reality user interface. In this paper, recent upgrades to the SAFIRE system are discussed and results from the SAFIRE’s initial field tests are presented.

The Radar and Communications Laboratory (RCL) at The Pennsylvania State University is at the forefront of radar technology and is engaged in cutting edge research in all aspects of radar, including modeling and simulation studies of novel radar paradigms, design and development of new types of radar architectures, and extensive field measurements in realistic scenarios. This paper summarizes the research at The Pennsylvania State University’s Radar and Communications Laboratory and relevant collaborative research with several groups over the past 15 years in the field of radar and related technologies, including communications, radio frequency identification (RFID), and spectrum sensing.

Radar research has been synonymous with the University of Kansas (KU) for over half a century. As part of this special session organized to highlight significant radar programs in academia, this paper surveys recent and ongoing work at KU. This work encompasses a wide breadth of sensing applications including the remote sensing of ice sheets, autonomous navigation methods for unmanned aerial vehicles (UAVs), novel laser radar capabilities, detection of highenergy cosmic rays using bistatic radar, different forms of waveform diversity such as MIMO radar and pulse agility, and various radar-embedded communication methods. The results of these efforts impact our understanding of the changing nature of the environment, address the proliferation of unmanned systems in the US airspace, realize new sensing modalities enabled by the joint consideration of electromagnetics and signal processing, and greater facilitate radar operation in an increasingly congested and contested spectrum.

Clutter and radio frequency interference (RFI) are prevalent issues in the field of radar and are specifically of interest to of cognitive radar. Here, methods for applying and testing the utility of cognitive radar for clutter and RFI mitigation are explored. Using the adaptable transmit capability, environmental database, and general “awareness” of a cognitive radar system (i.e. spectrum sensing, geographical location, etc.), a matched waveform is synthesized that improves the signal-to-clutter ratio (SCR), assuming at least an estimate of the target response and the environmental clutter response are known a prior i. RFI may also be mitigated by sensing the RF spectrum and adapting the transmit center frequency and bandwidth using methods that optimize bandwidth and signal-to-interference plus noise ratio (SINR) (i.e. the spectrum sensing, multi-objective (SS-MO) algorithm). The improvement is shown by a decrease in the noise floor. The above methods’ effectiveness are examined via a test-bed developed around a software defined radio (SDR). Testing and the general use of commercial off the shelf (COTS) devices are desirable for their cost effectiveness, general ease of use, as well as technical and community support, but these devices provide design challenges in order to be effective. The universal software radio peripheral (USRP) X310 SDR is a relatively cheap and portable device that has all the system components of a basic cognitive radar. Design challenges of the SDR include phase coherency between channels, bandwidth limitations, dynamic range, and speed of computation and data communication / recording.

Dynamic spectrum access (DSA) refers to the adaptive utilization of today’s busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

For nonlinear radar, the transmit power required to measure a detectable response from a target is relatively high, and generating that high power is achieved at the cost of linearity. This paper applies the distortion mitigation technique Linearization by Time-Multiplexed Spectrum (LITMUS) to intermodulation radar, a type of nonlinear radar which receives spectral content produced by the mixing of multiple frequencies at a nonlinear target. By implementing LITMUS, an experimental detection system for an intermodulation radar achieves a signal-to-noise ratio up to 20 dB for a total transmit power of approximately 80 mW and nonlinear targets placed at a standoff distance of 2 meters.

Nonlinear radar has proven to be a viable means of detecting devices that contain electrical nonlinearities. Electrical nonlinearities are present in dissimilar metals, metal to oxide junctions, semiconductors and more. This paper presents a linear and nonlinear synthetic aperture radar (SAR) system capable of imaging linear and nonlinear targets. The system creates images using data collected from a fixed 16 channel receiver with a single transmitter. A custom 16:1 switching network was developed to collect the SAR data from a 16 antenna receive array. SAR images presented show a nonlinear target placed directly on the ground and imaged in multiple range and cross-range locations. Data is also presented showing the clutter rejection properties of nonlinear radar. Images show that the harmonic radar is able to ignore the strong linear response from a corner reflector, while retaining the nonlinear response from a target.

It is often advantageous to modify, or warp, radar waveforms, particularly with respect to group-delay and spectral dilation. These warping adjustments may facilitate real-time motion compensation of waveforms in radar systems, especially when those waveforms are generated by a digital parametric waveform generator. Relevant waveforms to this paper include Frequency Modulated (FM) waveforms, such as the Linear-FM (LFM) chirp, Non-Linear FM (NLFM) chirp, and other general FM waveforms. We present techniques for making fine adjustments to dynamically warp general FM waveforms.

We desire a metric with which to evaluate the “goodness” of various image compression schemes in recreating an original Synthetic Aperture Radar image. Herein we propose a “coherence” measure that results in a single quality number for such an evaluation.

Spurious energy in received radar data is unanticipated and undesired signal relevant to radar target signatures, usually a consequence of nonideal component and circuit behavior, perhaps due to I/Q imbalance, nonlinear component behavior, additive interference (e.g. cross-talk, etc.), or other sources. The manifestation of the spurious energy in a range-Doppler map or image can often be influenced by appropriate pulse-to-pulse phase modulation. Comparing multiple images having been processed with the same data but different signal paths and modulations allows identifying undesired spurs and then cropping or apodizing them.

Synthetic Aperture Radar (SAR) is a proven, effective tool to monitor and map changing topography in rapidly expanding wildfire disaster situations. The broad area coverage provided by SAR can be used to detail areas of fire damage while high-resolution imagery can be used to determine fire movement and possibly provide warning to areas in the fire’s path. General Atomics Aeronautical Systems, Inc. (GA-ASI) recently collected data with an airborne Lynx SAR, operating in the Ku Band, in conjunction with a FLIR Systems Star Safire 380-HD Electrical-optical/Infrared (EO/IR) camera system on Southern California’s Blue Cut Fire. The Blue Cut Fire began in the Cajon Pass East of Los Angeles and burned a total of 36,000 acres⁴ . GA-ASI was able to overfly the fire, map the area, and detail the current location, as well as additional areas where the fire was spreading. Image Analysts were able to review the collected data and provide valuable information regarding location and possible fire direction. Analysts also conducted fire damage assessments to determine which structures were lost or damaged in the Fire. The real-time analysis of SAR and EO/IR data collections has the potential to increase effectiveness of firefighting crews attempting to contain a dynamic wildfire significantly

We propose an approach based on artificial Neural Networks (NN) to classify wideband radar jammer signals for more efficient use of countermeasures. A robust NN is be used to correctly differentiate Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). We compare the performance of the NN using the samples of the power spectrum versus the autocorrelation. Prior experiments showed that frequency-domain moments of the FM signal itself are better descriptors than time-domain moments. Using simulated wideband FM radar signals, we compute a set of N autocorrelation and spectra and feed them to the NN which has ten hidden layers. For training purposes, the autocorrelations or spectra sets are divided into three groups, 75% for training, 15% for validating and 15% for testing. For the power spectra set, we observe that a Signal to Noise Ratio (SNR) of 5dB allows the network to approach an average of 5% percent Probability of Error (PE). Training with the autocorrelation set yields comparable results. For an SNR of 5dB, the average PE reached an average of 0.3%. In both instances, the NN reaches zero percent PE at an SNR of 10dB.

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Analysis of satellite-acquired synthetic aperture radar (SAR) data provides a way to rapidly survey road conditions over large areas. This capability could be useful for identifying road segments that potentially require repair or at least onsite inspection of their condition due to changes in vehicular traffic associated with change in land use. We conducted a feasibility study focused on urban roads near the Southwest Research Institute (SwRI) campus in San Antonio, Texas. The roads near SwRI were affected by heavy truck traffic, they were easily inspected, and the age and construction of the pavement was known. TerraSAR-X (TSX) SpotLight (ST) satellite data were used to correlate radar backscattering response to pavement age and condition. Our preliminary results indicate that TSX radar imagery can be useful for detecting changes in pavement type, damage to pavement, such as cracking and scaling, and, occasionally, severe rutting. In addition, multitemporal interferometric analysis showed patches of settlement along two roads south of the SwRI campus. Further development of an automated approach to detect degradation of roads could allow transportation departments to prioritize inspection and repair efforts. The techniques also could be used to detect surreptitious heavy truck traffic in areas where direct inspection is not possible.

A new, versatile, UHF/L band, ultrawideband (UWB), vehicle-mounted radar system developed at the U.S. Army Research Laboratory (ARL) has recently been exercised at an arid U.S. test site. The unique switching scheme implemented to record data from all receive channels is described, along with the current calibration procedure. Radar and global positioning system (GPS) data collected in both forwardand side-looking configurations are processed, and synthetic aperture radar (SAR) images are formed. Results are presented for various target emplacement scenarios.

Weak Value Measurements (WVMs) with pre- and post-selected quantum mechanical ensembles were proposed by Aharonov, Albert, and Vaidman in 1988 and have found numerous applications in both theoretical and applied physics. In the field of precision metrology, WVM techniques have been demonstrated and proven valuable as a means to shift, amplify, and detect signals and to make precise measurements of small effects in both quantum and classical systems, including: particle spin, the Spin-Hall effect of light, optical beam deflections, frequency shifts, field gradients, and many others. In principal, WVM amplification techniques are also possible in radar and could be a valuable tool for precision measurements. However, relatively limited research has been done in this area. This article presents a quantum-inspired model of radar range and range-rate measurements of arbitrary strength, including standard and pre- and post-selected measurements. The model is used to extend WVM amplification theory to radar, with the receive filter performing the post-selection role. It is shown that the description of range and range-rate measurements based on the quantum-mechanical measurement model and formalism produces the same results as the conventional approach used in radar based on signal processing and filtering of the reflected signal at the radar receiver. Numerical simulation results using simple point scatterrer configurations are presented, applying the quantum-inspired model of radar range and range-rate measurements that occur in the weak measurement regime. Potential applications and benefits of the quantum inspired approach to radar measurements are presented, including improved range and Doppler measurement resolution.

Multiband cognitive radar systems, operating in a variety of frequency bands and combining the different channels into a joint system, can provide significant flexibility and capability to detect and track hostile targets. This paper proposes a multi-passband complex filter (MPCF) architecture and the related circuit design for a multiband cognitive radar system. By operating under the 5.8GHz UNII band, the sensing part detects the current usage of frequency bands from 5.15GHz to 5.825GHz and provides the information of unused channels. The multiband cognitive radar system uses the whole unused channels and eliminates the used channels by using an on-chip MPCF in order to be coexistent with the Wi-Fi standard. The MPCF filters out the unwanted channels and leave the wanted channels. It dynamically changes the bandwidth of frequency from 20MHz to 80MHz using the 0.18μm CMOS technology. The MPCF is composed of the combination of 5th-order Chebyshev low-pass filters and high-pass filters, and the overall inband ripple of the MPCF is 1.2dB. The consuming current is 21.7mA at 1.8V power supply and the 20MHz bandwidth noise is 55.5nV. The total harmonic distortion (THD) is 45dB at 25MHz and the adjacent channel rejection is 24dB. The result of the MPCF guarantees the performance requirements of the multiband cognitive radar system.

Coastal soils offer a number of challenges in electromagnetic remote sensing applications. They are highly saline owing to their constant contact with salt water resulting in high values for the real and imaginary parts of their permittivity. Due to this fact, it is desirable to model these properties and determine how they will affect the detection and location of targets buried in coastal soil environments. We examined the propagation of a plane wave with three different incidence angles on a cubic perfect electric conductor (PEC) target contained within an semi-infinite dielectric material with the same properties as the soil. This response was then compared to that of a baseline target with no dielectric surrounding it and a dielectric mimicking dry sandy soil. The results show that the signal is both highly reflected at the surface of the wet coastal soil, and significantly attenuated as it propagates through the wet soil dielectric. The results of our modeling and simulation studies over a wide range of conditions (e.g. frequency, soil salinity, burial depth, etc.) are presented and trade-offs examined in order to develop a cognitive radar system for enhancing target detection and clutter suppression.

In this study, an automatic take-off and landing system (ATOLS) based on radar guidance was developed to provide day/night, all weather, automatic takeoff and landing for unmanned aerial vehicles (UAVs). The ATOLS contains a ground-based tracking radar subsystem and an airborne transponder subsystem. This X-band tracking radar can provide precise position information for UAV-control operations (transponder mode) and fire-control systems (skin mode). It provides 360 degrees of azimuth coverage and therefore can be employed for navigation applications. Its maximum tracking range is about 17 km and accuracy of altitude measurement is about 1 ft with a 50-ft decision height above ground level. To substantiate the proposed ATOLS system, a differential global positioning system (DGPS) was also developed. When a UAV at a low-elevation angle is detected and tracked by a tracking radar, multipath propagation often leads to the degradation of tracking accuracy or even cause the radar to break track. As a result, it becomes a potential risk to flight safety of the ATOLS guidance and control of UAVs. To overcome this technical difficulty, this paper proposes a solution based on optimization of radar parameters to mitigate the interference from multipath signals. The feasibility of proposed method has been experimentally proven through the flight trials of UAVs. Compared to the conventional low-elevation tracking techniques, the proposed one employs the radar signal processing, and does not consume additional hardware and resources.

The bearing angle could be estimated based on the high resolution image obtained by Multiple-Input Multiple-Output (MIMO) synthetic aperture radar (SAR). By extending the previous work of SAR images simulator for 3D target model, two estimation methods are proposed for calculating the bearing angle according to the provided SAR images. Without loss of generality, the SAR images are derived through the raytracing aided simulator of a TDMA MIMO SAR system with 13 transmitting antennas and 8 receiving antennas. Assuming the true bearing angles range from -10° to 10°, the estimation results along with the error analysis are presented after the discussion about the estimation methods. The root mean square error (RMSE) value varies with different threshold settings for erasing the undesired pixels, and an RMSE less than 1.6° could be achieved in most circumstances.