This PDF file contains the front matter associated with SPIE Proceedings Volume 11408, including the Title Page, Copyright information, and Table of Contents.

The Linear Sampling Method is an imaging technique that reconstructs target shape via a series of beamforming operations without using linear scattering assumptions. The norm of the solution is typically used to determine which pixels are inside the target support. There has not been much study of how to use the phase of the solution to aid in target identification. In this study, we explore using the solution phase to classify targets according to their electrical properties via a machine learning approach. We implement a support vector machine, apply it to imagery from simulated target data, and quantify classification accuracy.

To satisfy the increase in demand for radio frequency (RF) spectrum, recent Federal Communications Commission (FCC) policies permit spectrum sharing between radar and Long-Term Evolution (LTE) communication systems. New cognitive radar systems are promoting spectrum sharing capabilities to mitigate the risk of mutual interference. To develop and test these systems, a realistic communications RF interference (RFI) environment is necessary. This paper describes a system, currently under development, to generate continuous dynamic 4G/LTE RFI for use by radar system designers. The system employs a Vector Signal Transceiver to emulate RFI with various frequency, time-complexity, and power parameters. Many 4G/LTE frames are pre-generated, then, according to the specified parameters, the random frame sequences are generated in real-time. This produces continuous, dynamic, and realistic LTE emissions for a controlled test environment. This work presents implementation details of the LTE emulation system.

We demonstrate an algorithm for determining low entropy fully polarimetric synthetic aperture radar (POLSAR) coherence matrices using inverse syntetic aperture radar (ISAR) data. Low entropy information is not accessible using the standard method of averaging pixels. Spatially averaging locally around a SAR pixel is not equivalent to time averaging necessary for low entropy measurement. A low entropy POLSAR measurement indicates the dominance of a single scatterer. Low entropy ocean scattering, primarily from Bragg scattering, will be discussed.

Wireless sensor network transmission scheduling refers to determining how and when the sensors in the network report their measurements. Intelligent scheduling could by used to lower the necessary number of transmissions, and reduce network congestion and extend battery life. In recent years, extending battery life of the sensors is a research problem that has received much attention in literature. This can be achieved through both hardware and software means. In this paper, we present a transmission scheduling approach for wireless sensor networks that has the purpose of monitoring and reporting measurements of some spatio-temporal process. This is achieved through the development of a geometric approach to an individual node's coverage model that is a function of the estimation accuracy in a region near the node. Then, for an arbitrary number of nodes, we will show how this model could be used to reduce how often a sensor needs to reports its measurements to a fusion center.

This paper considers optimum sparse arrays for target detection and tracking. Sparse array optimization simplifies the receiver design and reduces both the hardware and computational cost. In this paper, we consider two-dimensional sparse array design for transmit beamforming in MIMO radar operation. We pursue a bi-objective design function that jointly focuses the maximum energy towards the target locations while ensuring minimum cross-correlations from the radar returns, enabling adaptive target tracking. This involves a joint design of sensor locations as well as the transmitted waveforms. We compare the performance of the proposed sparse array design with other commonly used sparse arrays.

MicroDoppler classification of human motions has been performed thus far without considering the Human ethogram. The ethogram is a catalog of possible activities and the way they are connected. For example, sitting and falling cannot be followed by walking without first standing. The same argument applies to previous motions, e.g., sitting can be only preceded by standing. From a motion classification perspective, the ethogram can be categorized into translation and in-place motions. Whereas the former mainly describe crawling and gait articulations, the latter are primarily associated with motions that do not exhibit considerable changes in range. In this paper, we exploit the human ethogram to guide and improve classification of activities of daily living. Using an FMCW radar with range and Doppler resolution capabilities, we compare the performance of the ethogram-based classifications with the case where all motion classes are considered all the time. The thrust of this comparison is not to advocate one type of human motion classifier over the other, but rather to show the impact of incorporating the ethogram sequence of human motion on classification performance.

This paper investigates the prospect of classifying different types of rotary wing drones using radar. The proposed method is based on the hypothesis that the rotor blades of different sizes and shapes will exhibit distinct Doppler features. When sampled unambiguously, these features can be properly extracted and then can be used for classification. We investigate various continuous wave (CW) spectrogram features of different drones obtained with a low phase noise, coherent radar operating at 94 GHz. Two quadcopters of different sizes (DJI Phantom Standard 3 and Joyance JT5L-404) and a hexacopter (DJI S900) have been used during the experimental trial for data collection. For classification training, we first show the limitation of the feature extraction based method. We then propose a convolutional neural network (CNN) based approach in which the classification training is done by using micro-Doppler spectrogram images. We have created an extensive dataset of spectrogram images for classification training, which have been fed to the existing GoogLeNet model. The trained model then has been tested with unseen and unlabelled data for performance verification. Validation accuracy of above 99% is achieved along with very accurate testing results, demonstrating the potential of using neural networks for multiple drone classification.

Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature to address this problem, such as unsupervised pre-training and transfer learning from optical imagery or synthetic RF data. This work investigates an alternative approach to training which involves exploitation of “datasets of opportunity" micro-Doppler datasets collected using other RF sensors, which may be of a different frequency, bandwidth or waveform - for the purposes of training. Specifically, this work compares in detail the cross-frequency training degradation incurred for several different training approaches and deep neural network (DNN) architectures. Results show a 70% drop in classification accuracy when the RF sensors for pre-training, fine-tuning, and testing are different, and a 15% degradation when only the pre-training data is different, but the fine-tuning and test data are from the same sensor. By using generative adversarial networks (GANs), a large amount of synthetic data is generated for pre-training. Results show that cross-frequency performance degradation is reduced by 50% when kinematically-sifted GAN-synthesized signatures are used in pre-training.

Radar has been recently employed for automatic hand gesture recognition for touchless interactive intelligent devices. Compared to hand gesture, arm gesture recognition can be more suitable for contact-less man-machine interaction with longer range separation. The larger radar cross-section of the arms, vis-a-vis hands, permits more remote interactive positions in an indoor setting. Further, the ability of using hand gestures for device control can sometimes be limited by cognitive impairments such the Parkinson disease. In this case, arm motions can be more robust to strong hand tremor and shaking. In this paper, we discriminate between dynamic arm motions using a Doppler radar sensor. The method considered is motivated by the clear contiguity of the arm signal back-scattering in the time-frequency domain. We use information gleaned from the arm micro-Doppler (MD) signature as the sole features and proceed to classify arm motions using the Nearest Neighbor (NN) classifier. In particular, we analyze the role of the maximum instantaneous Doppler frequencies and their distribution on classification performance. The proposed classification method favorably compares with other methods based on principal component analysis (PCA) and convolutional neural networks (CNN).

This paper explores a concept recently proposed for underwater-to-air communications using a combination of acoustic and electromagnetics modalities. The transmitter is an underwater acoustic projector that is used to vibrate the surface of the water whose vibrations are modulated by the communications message. The airborne sensor is a millimeter-wave radar hovering over the surface of the water which senses the water surface vibrations. The phase of the coherent radar signal is used to determine the displacement of the water surface to the sub-millimeter level. From this information, the communications message can be demodulated. Prior research and our calculations suggest that acoustic frequencies in the range 100–300 Hz are ideal in terms of throughput and signal-to-noise ratio. In our measurements, a loudspeaker covered in copper tape is used as a surrogate water surface responding to the acoustic stimulus. A W-band Doppler radar operating in the 94-GHz frequency range using analog in-phase/quadrature (I/Q) demodulation is used as the radar sensor. Early results are quite promising; while playing sinusoids at 100 and 300 Hz, the signal can clearly be observed in the radar output.

A broad review of publications relevant to nonlinear radar is conducted. The principle-of-operation of nonlinear radar is summarized and applications for this technology are listed. Targets addressed by this type of radar follow a power-series model, and from this model a nonlinear radar range equation is derived. An extensive survey of publicly-available literature, including specifications for systems already tested, guides the design of harmonic radar for finding electronics. The authors have combined a stepped-frequency architecture with harmonic radar to create a system which is capable of imaging and tracking nonlinear targets with very high clutter rejection.

During the design and development of multi-functional airborne hazard detection and avoidance radars, as well as radar navigation functions, we usually need a precise and reliable simulation evaluation. However, the existing solutions are usually highly proprietary for specific developers. In previous studies, we developed PASIM as one possible framework to unify the multi-functional radar developments. In this study, more novel enhancements and applications of PASIM are introduced based on the needs of communities, and the software tools are updated specifically for airborne radars. These updates include: (1) Enhancement and evaluation of airborne radar ground clutter modules, which supports different terrain or water surface types. (2) Combination of measured data as part of simulations. In this case, we used NASA’s pulsed-Doppler weather radar data as “meta-truth” and created simulation examples of a new generation of Sense and Avoid (SAA) simulation operation based on them. (3) Incorporation of realistic target impulse responses, RF channel modeling and processing chain. (4) Incorporation of automatic radar mode optimization, ground clutter mitigation solution and algorithms. (5) Enhanced data quality evaluation for both air-target tracking and weather surveillance. A generic airborne pulsed-Doppler radar with reasonable system parameters are used for our studies as an example and the design/evaluation procedure and results are presented.

A method for synthesizing any radar signal via post-processing is proposed theoretically and demonstrated experimentally for both pulsed and linear frequency modulated signals. The method does not require transmitting the investigated signal, nor does it require any hardware reconfiguration (such as fully programmable gate arrays), in contrast with ordinary software defined radars. Instead, the method is based on transmitting the ‘stepped frequency continuous wave' signal with a device such as a network analyzer. By obtaining the frequency response in the desired bandwidth (S-parameters), signal-specific digital filters can be applied in order to obtain the response of any other signal. By transforming the filtered frequency response into the time domain, the ordinary processing of such signals can take place in the digital domain. The advantages of different signals can therefore be used by a single optimized chip, simply by swapping its software.

Binary phase noise waveform is difficult to detect due to its thrum nail ambiguity function, has high transmission efficiency due to its constant magnitude, and is simple for implementation. Because of these advantages, binary phase noise waveform is widely used in noise radar. Many techniques have been developed to design binary phase noise waveform including Bernoulli trial, logistic mapping function, neural network optimization, genetic algorithm, kicked rotor chaos, polyphase perturbing, and particle swarm optimization techniques. In this paper, we first discuss the characteristics that good binary phase noise waveform needs to have. Then we introduce Kolmogorov-Smirnov (KS) two sample test in nonparametric statistics and derive balanced random walk. From balanced random walk theory, we propose balanced random based approach for noise waveform design and demonstrate that the noise waveforms generated by balanced random walk approach have zero direct components. We implement the proposed approach for noise waveform design and show that the proposed method is over 10% better on maximum sidelobes, 30% better on sidelobe energy than Bernoulli trial noise waveform design and selection.

Chaotic FM signals are an attractive choice for secure communications and bistatic radar imaging in a shared electromagnetic spectrum. In this work, we propose an approach that incorporates a system architecture with a chaotic system with three state variables, one of which is selected for transmission with an embedded audio message. The sum of the chaotic state variable and audio message feeds a voltage-controlled oscillator that generates a wideband FM signal useful for radar target imaging. Using a receiver equipped with a chaotic synchronization stage, we show that the message is effectively extracted if it is embedded in any of a family of FM signals generated via dissipative chaotic flows. Furthermore, our results suggest that this family of FM signals provides the bandwidth necessary to resolve target components in range and Doppler as determined by their bistatic radar ambiguity surface.

Target detection can depend on many factors, such as waveform, system geometry, and transmit power. In order to study factors such as system geometry, both a monostatic and a bistatic radar system were simulated. The targets of interest were assumed to be moving vehicles in a ground-based simulation. After creating system models and performing the simulations, the next step was to evaluate the effectiveness of the post processing algorithm. This algorithm was developed to extract more information about the characteristics of the target. Next, the algorithm was tested in a bistatic geometry utilizing the Long-Term Evolution (LTE) Down Link signal as the illuminator of opportunity. The multiple test cases are used to verify the robustness of the signal processing. Overall, through simulations, it was concluded that the advanced signal processing techniques were effective in extracting more information about the targets' characteristics.

Distributed Sensor Networks are often implemented to overcome some of the challenges presented by a single monostatic sensor system. In this paper we consider the possibility of geographically-distributed quantum radar nodes for improved target detection. Our theoretical design assumes N quantum sensor nodes with transmit-detect capabilities. One of these nodes is chosen to an “entangler node”, which generates entangled photon pairs and uses quantum channels to transmit, swap or teleport the photons to nodes A and B. One of the nodes retains the photon as an idler while the other transmits its photon as the signal. When this process is repeated across several nodes, there is a clear advantage of having different bearing angles to the target among the N nodes. Furthermore, the position and orientation of these sensor nodes could be actively optimized to maximize information about the state of the target. In addition, because the photon frequencies can be chosen to be independent of N, the system generates virtual modes that increase the performance of a single quantum sensor. The proposed design could be generalized to maintain the equivalent of a distributed quantum register among the N nodes so that the sharing of classical information from the detection among the nodes can permit state estimation to be performed in a completely uniform and consistent way. More specifically, each node receives a signal photon which is compared to its idler photon to produce something that relates to the state of the target but is completely non-informative to the individual nodes. It is only when the quantum information from the nodes is received at a secure central node that a full estimate comprising all information about the target from all nodes can be constructed. As we will discuss, the system not only is more information-efficient but also provides a certain level of security because any classical information leakage at a node (i.e., compromised by an adversary) will not actually reveal anything about the state of the target. Therefore, a set of geographically distributed quantum sensors can be treated as a single logical quantum radar device.

Analysis of quantum-based methods for improved gravimetric sensing has demonstrated that photon entanglement can provide an additional source of target-state information beyond what is possible using purely classical sensing techniques. In this paper we propose a quantum-based system for large-scale space-based detection of small near-earth objects (NEOs). The objective of the system is to measure extremely small deviations in the background gravitational field within a defined surveillance region to identify potentially dangerous NEO intrusions as early as possible. The system is composed of a set of widely-separated line-of-sight emitter-receiver pairs that exchange entangled photons so that the signature of a moving object can be discerned from subtle gravitation-induced spin effects. The key advantage of the system is that detection does not require direct illumination of the target. A potentially more important practical advantage is that the system can be implemented using relatively simple interferometric measurements.

Recently, much of the quantum radar/lidar research is focused on correlating single photon detection events with no delay line on the idler path. In other words, measuring the idler immediately, and correlating these events with later received photon events from the returning signal. This research approach has raised some questions due to the fact that all measurements done are classical, yet researchers are still observing sensor improvement in comparison to classical techniques. This therefore implies that the benefits from quantum radar/lidar using these techniques should be able to be explained entirely classically. This paper explores this concept by asserting that the correlation between the signals used in quantum remote sensing is largely due to the fact that the signal and idler photons are created simultaneously (which is only possible from an entangled source). We show, using very simple computer simulations, that having a single photon correlated (binary) waveform leads to correlation SNR advantages only in the low photon level regime, agreeing with previous literature.

Within the last decade, the field of quantum remote sensing has garnered a lot of interest from the radar and communication community. Many papers on this topic have compared the performance of a classical system versus a quantum system. However, the concept of a system using both classical and quantum components in conjunction has not been explored thoroughly. This paper documents the design and simulation of a quantum + classical cooperative remote sensing design in the optical regime. The arrangement uses quantum correlations created by entangled photons in addition to conventional classical waveform correlations. We show that the composite quantum + classical system exhibits increased performance compared to a pure classical system alone.

In this paper, we derive the electric field covariance matrix of the signal and idler beams from an entangled source for applications involving quantum radar. We also derive the corresponding covariance matrix for a classical matched filtering remote sensing system and compare to the quantum result. We use this comparison to derive an expression for the quantum enhancement factor as a function of the mean photon number per mode, N_s. This result is significant because it allows one to exactly calculate the predicted quantum enhancement as a function of transmit power, rather than only having an upper bound. Additionally, we look into previous analog correlation techniques using an optical parametric amplifier (OPA) and show that immediately detecting the idler produces the same cross correlation terms. However, the actual measurements needed to harness these correlations is enhanced when one immediately detects the idler because it minimizes the added noise caused by the additional length of the idler path in the conventional method. Finally, our results also show that one does not need to count photons to harness these correlations, but rather, perform electric field measurements.

The HRGB method is an alternative to Fourier and back projection techniques for image reconstruction from k-space or I&Q SAR datasets. The method is highly parallelizable, and does not rely on interpolation. The resulting images can thus be computed quickly, with accuracy exceeding back projection techniques. The HRGB method has been shown to be particularly suitable to trapezoidal/keystone SAR grids. The present paper discusses the HRGB method as applied to polar-grid SAR image reconstruction, such as for spotlight SAR and ViSAR. The discussion includes steps for 3D wavefront curvature compensation.

One of the primary challenges in helicopter flight is landing in a degraded visual environment (DVE). In a DVE, the situational awareness of the pilot is inhibited by natural phenomena such as rain, snow, fog, etc., or aircraft induced phenomena such as brownout or whiteout. Typically, the pilot is assisted by feedback from a lidar system, but if the particles are dense enough, the lidar is unable to provide useful information about the terrain and other obstacles. Nonetheless, radar can be used to create useful imagery of the surrounding area, albeit at a reduced resolution, as it has the ability to penetrate precipitation, dust, and other obscurants. In this paper, a forward-looking synthetic aperture radar (FLSAR) concept, which can form three-dimensional (3-D) imagery from a 1-D array, is proposed. A frequency domain imaging algorithm, the polar format algorithm (PFA), is investigated for its applicability to the FLSAR geometry. We show that a wavefront curvature correction (WCC) procedure is required to compensate for the far-field approximation made in the PFA which is not valid at the frequencies and operational ranges under consideration. A filter transfer function for WCC for the FLSAR geometry is derived. Finally, the effectiveness of the derived filter transfer function is demonstrated in simulated 2-D imagery.

Synthetic aperture radar (SAR) imaging of a moving target has been a subject of interest because of its high-resolution target imaging and tracking capability. However, SAR was primarily designed for imaging stationary targets. Therefore, traditional SAR imaging algorithms suffer from low detection accuracy, inaccurate parameter estimation, and displace/defocus imaging due to the uncertainty of the target’s motion. The motivation for this paper is to employ a tracking approach to track and estimate the target parameters accurately from the data returned by the SAR to resolve moving targets imaging issues. In this paper, we present a novel approach of combining the SAR range-Doppler Algorithm (RDA) with the probabilistic multi-hypothesis tracking (PMHT) algorithm in solving the problem of moving target imaging with periodically missing data in SAR. The algorithm is implemented on the raw SAR data, rather than on the processed SAR image, to reduce memory consumption and entail lower complexity. Results from simulated SAR data demonstrate the effectiveness of the proposed approach by generating a well-focused SAR image.

Mobile ad hoc networks (MANETs) consisting of nodes with single omnidirectional antennas and limited transmit power can support wireless connectivity over long distances through coherent operation. Although designed for short-range networked communication, the individual radio nodes in a MANET can be coordinated to form a sparse aperiodic array with a high power directional gain. By targeting a distant receiver with the array beam synthesized by a synchronized transmission, the MANET can ensure greater signal quality at the receiver. The sparse nature of the array coupled with the mobility-driven randomization of node positions makes the array gain somewhat uncontrollable outside of the main beam. Our work investigates methods of shaping the pattern of a coherent distributed transmit array using open-loop beamforming. A null-forming algorithm based on a partitioning of the network nodes into two sub-arrays is presented. Simulation results demonstrate the attenuation of the array factor in the desired null direction. A stochastic optimization step is also introduced.

A Photonic MOSFET includes a low series resistance semiconductor laser or LED diode in the drain region [1], and a photon sensor (Avalanche Photo Diode) near the channel / drain region [2]. MOSFET, Lasers, and APDs are integrated as one transistor. When a gate voltage and a drain voltage are applied, both the laser and MOSFET are on. Light from the laser is absorbed by the APD to achieve higher output drain current and speed, with the avalanche breakdown current. When the MOSFET is off, the laser is also turned off. In this paper we will focus on the RF performance of sub-10nm Photonic CMOS technology, including information of series resistance and capacitance, cut-off frequency, avalanche breakdown voltage, and other critical DC / AC parameters. The goal is to improve the performance of power RF ASICs for radar electronics. Based on these data, we will also discuss other potential applications, such as Photon Counting Image Sensors, and Regulator for pulsed and CW high power lasers.

Moving target indication (MTI) is the first component of a tracking radar's signal process. Being the fundamental component of a tracking radar, the reliability of the MTI process to detect a moving target must be effectively characterized in order to achieve the goal of characterizing the reliability of a tracking radar. Operational reliability metric is used to quantify the ability of an MTI radar to detect a moving target in various scenarios. The radar scenario investigated in this paper includes the detection of a small aircraft by a ground based MTI radar. MTI processing is applied to the received signal in order to remove the effects of the stationary ground clutter. An operational reliability analysis of the filtered data is performed to show the reliability of the MTI radar. The reliability analysis is repeated for different target altitude scenarios.

Control of orbital angular momentum (OAM) offers the potential for increases in control, sensitivity, and security for high-performance microwave systems. OAM is characterized by an integer OAM mode where zero represents the case of a plane wave. Microwaves with a nonzero OAM mode propagate with a helical wavefront. Orthogonal OAM modes can be used to carry distinct information at the same frequency and polarization, increasing the data rate. The OAM waveform may also increase radar detection capability for certain shaped objects. OAM can be induced by broadcasting a plane wave through a spatial phase plate (SPP) dielectric which introduces an azimuthally dependent phase delay. However, SPPs are frequency-specific, which presents an obstacle for harnessing OAM in frequency-modulated communication systems and wide-bandwidth radar. In this study, we develop a circular phased array to synthesize the desired vortex-shaped wavefront. This approach offers a critical advantage: the phases of all antenna elements are easily programmable under different frequencies. As a result, transmission and reception of the OAM beam can be controlled with great flexibility, making it operable over a wide frequency spectrum, which leverages OAM radar functionality and performance. In this paper, we will investigate a wide-bandwidth radar with OAM mode-control and signal processing.

Using a mutual coupling based calibration technique, a platform agnostic algorithm is developed to perform an initial alignment on phased array radar systems without the use of external equipment (far-field sources, near-field chambers and probes) or the need to implement additional internal calibration networks to already existing systems. By using coupling pairs from mutual coupling measurements, an overdetermined system of equations is created and solved. The solutions of the overdetermined system are the least-square error estimated complex gains of each element channel. A pre-phasing routine is implemented on the element channels to avoid possible phase ambiguity issues in the results since fully digital radar systems may contain phases from all possible angles (-π:π). Another improvement implemented to the algorithm is an iterative solution that can reduce the errors of the estimated results and also further aids in avoiding phased wrapped results. For diagnostic purposes, the algorithm can determine if an element has failed in either receive or transmit mode and then perform the initial alignment while ignoring the failing elements to avoid introducing errors from these failed elements. The algorithm is compatible with dual polarized systems.

Data processing, calibration, and quality evaluation are critical elements for successful airborne radar missions. For a downward-looking airborne radar, the usage of ground as a calibration target has been discussed before but not completely analyzed for precipitation measurement missions at Ka-band. In this study, the team performed data analysis and calibration modeling for the Millimeter-Wave Airborne Radar for Learning and Education (MARBLE), which was developed as a recent undergraduate team effort beginning in 2016. Millimeter-wave radar missions for MARBLE include precipitation measurement and terrain remote sensing through vertical profiling. To achieve these mission goals, the team used multiple time- and spectrum-domain processing methods on the ground return data collected from 2018 NASA ER- 2 engineering calibration flights. Some of the algorithms include spectrum analysis with various CPI arrangement and multi-lag processing to enhance signal-to-noise ratio (SNR). Doppler calibration based on aircraft platform motion and orientation is also considered. Useful results are obtained from ground power calibration as well as Doppler estimation. In addition, multiple ground calibration tests with actual weather results are incorporated to supplement the airborne measurements after some hardware checking and improvement. Based on the reasonable outcomes from the calibration measurements, a new high-altitude flight campaign for precipitation measurement is being planned for 2020.

Autonomous vehicles have always been a field of considerable research interest. Past research have demonstrated achievements assuring that self-driving cars are, in fact the future of mobility. Self-driving cars have been made possible by sensor fusion technique, which incorporates sensors, including camera and radar. Cameras have the best resolution. Nevertheless, their ability to sense may be affected in extreme weather or night conditions. Radars are not affected by these conditions but lack the resolution when compared with radar. Most of the automotive radars are Frequency Modulated Continuous Wave (FMCW) radars whose range resolution depends on the bandwidth of the FMCW chirp, and spatial resolution depends upon the number of the receiving antennas. Having a higher number of receiving antenna elements will improve the angular resolution. Instead of increasing physical receiving antennas, it is possible to generate virtual receiving antennas by adding transmitting antennas, commonly known as the Multiple Input Multiple Output (MIMO) technique. MIMO requires orthogonal signals in multiple transmitting antennas. Commercial automotive radars have implemented the capability of MIMO using Time Division Multiplexing (TDM) and Binary Phase Modulation (BPM) in 2Tx and 4Rx systems. Although the angular resolution is improved, the maximum unambiguous velocity is reduced by half. This paper proposes the Frequency Division Multiplexing (FDM) Technique to achieve orthogonality. A full radar system has been simulated in MATLAB environment, which shows the possibility of using FDM in automotive radars without compromising the maximum unambiguous velocity. Frequency modulated signal with different starting frequencies for two Tx antenna is used to create 8 Rx virtual channels. FDM usually requires an increment in sampling frequency of Analog to Digital Converter (ADC). In this paper, the two starting frequencies are chosen, such that the requirement of higher sampling rate has been eliminated.

In recent decades, the fever issue in radar research is how to reduce radar cross section (RCS) of specific targets like fighters. In regard to the other aspect, to increase the detection probability of air targets is also a highlighting feature for military surveillance radars. In this work, RCS of a typical aircraft target for a monostatic radar system in very high frequency (VHF: 30-300 MHz) band and for a bistatic one in X band (8-12 GHz) are investigated. According to the electromagnetic theory, since the RCS of the aircraft in VHF band is within Mie region; the resonant phenomenon of the RCS is observed so the RCS is enhanced and the polarization effect is also described. For the bistatic radar operating in X band, there is also an apparent enlargement of the bistatic RCS at certain incident angles. Two numerical electromagnetism techniques — integrated equation (IE) solver based on moment of method (MoM) and shooting and bouncing ray (SBR) solver based on ray model, are respectively used to calculate RCSs in VHF and X bands for efficacy and accuracy. A guideline to choose an appropriate simulation method in a certain frequency regime is proposed to increase the calculation efficiency and maintain the fidelity of evaluation. The simulated results reveal that these two radar systems indeed have the advantage of detecting stealthy or small targets.

During the last decades the radio detecting and ranging (RADAR) technology underwent an evolution transiting from the linear-frequency-modulated (LFM) systems developed in the 1970s, up to the orthogonal frequency-division multiplexing (OFDM) systems developed in the early 2000s. In mid 2010s, systems were proposed that combined the radar principle with optical solutions developed for imaging and ranging tasks following a hyperspectral embedded systems approach. The idea was to profit on the one side from the possibility offered by RADAR systems to work in harsh environments using emitted radio waves and detect mainly metal objects placed far away (hundreds of meters or even kilometers) from the detection system with positioning spatial resolutions in tens of centimeters, even if there are non-metallic barriers such as e.g. walls in between; and expand this possibility by using optical systems (e.g. light detecting and ranging –LIDAR- systems), using visible light active illumination, capable of generating 2D and 3D images of objects placed at much smaller distances from the detector, but allowing for much higher spatial resolutions (in the millimeter range). To reduce the atmospheric absorption of the emitted active illumination and increase the emitted optical power allowed for these systems that can correctly function even in harsh environments, we propose shifting the active illumination wavelengths from the visible range to the near infra-red (NIR) range, e.g. to 1550 nm. Lacking affordable image sensors fabricated in InGaAs technology, capable of detecting NIR radiation, in this paper we propose a hyperspectral imaging system using a very low power consuming single commercially available InGaAs photodiode to generate 2D images using the single-pixel imaging (SPI) approach based on compressive sensing (CS) and an array of NIR light emitting LEDs, combined with an 80 GHz millimeter band RADAR. The system is conceived to deliver a maximum radar range of 150 m with a maximum spatial resolution of ≤ 5 cm and a RADAR cross-section (RCS) of 10 – 50 m², combined with an optical system capable of generating 24 fps video streams based on SPI generated images yielding a maximum ranging depth of 10 m with a spatial resolution of < 1 cm. The proposed system will be used in unmanned ground vehicle (UGV) applications enabling decision making in continuous time. The power consumption, dimensions and weight of the hyperspectral ranging system will be adjusted to the UGV targeted applications.

The U.S. Army has recently developed a single-operator, low frequency, non-motorized experimental radar system called “Templar”. This system is intended to collect data at geometries approximating those encountered by small, unmanned aerial systems (sUAS). As a result, it is smaller than typical vehicle-mounted systems, enabling it to be pulled by the operator and more easily image areas that are not accessible to motorized, vehicle-mounted systems. Since it is intended to operate at lower frequencies (500 MHz – 1500 MHz), its transmit and receive antennas are larger than those of many airborne systems. Hence, an appropriate physical structure—including a mast and a cart to carry necessary electronics—were required elements of the system design. In what follows we describe the system in more detail, and we discuss the initial data collections performed to verify system performance, noting certain problems encountered. Finally, we examine imagery from this initial data set and present plans for future data collection and processing.

In the era of big data, it appears imperative that future battle management systems be able to identify, decipher, and prioritize actionable information. This calls for fusing information and synchronizing operations across multiple domains and multiple sensor modalities. Fusing data from a diverse range of sensors across multiple domains is critical for improved situational awareness to enhance warfighters’ effectiveness. The basis for analyzing multiple field radar system data in real time remains a challenging yet promising threshold for military operational intelligence. Multiple domain sensor systems used to gather field intelligence requires gathering different types of information processing at required speeds that fall short of human reaction time and cognition. To press the advancement of field intelligence, the analysis, fusion and optimization of multi-domain systems, sensor data analysis is explored using probabilistic machine learning and supplemented heuristic signal processing to provide a basis for multi-system data integration, analysis and sensor suite selection.