This PDF file contains the front matter associated with SPIE Proceedings Volume 11003, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Passive bistatic and multistatic radar rely on illuminators of opportunity to provide the transmitted signal. The illuminator of opportunity selected for our study is Frequency Modulation (FM) radio broadcasters. FM radio was chosen for its widespread availability and high power output which allows for long range measurements. The simulation performed includes three transmitters in order to allow effective triangulation and location of the targets. The receiver emulates an airborne receiver to simulate a surveillance aircraft. The signals from each transmitter is received and processed separately to obtain bistatic range information. Then, using data fusion techniques, these signals are combined in order to obtain a better spatial location of the targets relative to the receiver. The main limitations of this system can be traced back to using FM radio as the illuminator of opportunity, because the bandwidth limits the range resolution. Overall, the system can effectively detect and track airborne moving targets using three FM radio stations as transmitters.

The task of detecting and locating landmines, unexploded ordnances (UXO) and improvised explosive devices (IEDs) is still a major challenge up to the present day. Problems such as the distance between the hazardous area and the measurement system as well as the differentiation between the target of interest and the surrounding soil are of importance in the development of the sensor system. Various types of radar based systems have been developed over the last decades to solve these problems. Compared to other methods ground penetrating synthetic aperture radar (SAR) has the ability to scan large areas from a safe standoff distance in a relatively short time. In this paper, two different imaging radar systems are compared. The first one is a ground-based SAR (GB-SAR) developed at German Aerospace Center (DLR). The other system is an unmanned aerial vehicle-based SAR (UAV-SAR) from the University of Ulm. The presented data originates from a joint campaign using the same measurement scenarios.

This paper investigates the possible configurations of a ground penetrating radar (GPR) system installed on a small UAV platform and used for buried target imaging. After discussing the advantages of this type of platform as compared to existing designs, we proceed to analyzing the imaging performance of the proposed system. This is done first by simulating the point spread function of the system, then by using realistic radar models involving a buried antitank landmine. The study has the following objectives: finding the best sensing geometry for this type of targets; optimizing the frequency band, polarization, and synthetic aperture length, position and sampling rate for best imaging performance; and developing SAR image formation algorithms for this GPR system. Both 2-D and 3-D GPR imaging systems are considered in this work, emphasizing the advantages of certain sensing modalities over the others. Image attributes such as resolution, grating lobes, signal strength, ground bounce separation and surface clutter sensitivity are used in the performance comparison.

Explosive hazards pose a threat to both civilians and warfighters in areas of current and past conflict. The U.S. Army Combat Capabilities Development Command (CCDC) has been exploring the use of an unmanned aerial vehicle (UAV)-mounted ultra-wideband (UWB) radar to image and detect obscured explosive hazards. In a stripmap modality, a synthetic aperture radar system travels in a straight line and takes measurements perpendicular to the platform’s direction of travel. The large angular diversity provided by the platform motion yields a fine cross-range resolution of the imaged scene. This problem space is being simulated in MATLAB to determine the feasibility of buried target detection and to identify the optimal parameters of operation on a UAV. Parameters such as platform height, incident angle, and bandwidth are investigated. It is shown that performance at different platform heights is determined by the dependence of the signal-to-noise ratio (SNR) on elevation. Furthermore, a minimum platform height is required to meet the minimum requirements of the time-bandwidth product for pulsed waveforms. An optimal transmit angle can be found by maximizing the target-to-clutter ratio (TCR). The target radar cross section (RCS) is taken from finite-difference time-domain (FDTD) models of targets of interest, and the clutter is simulated using the small perturbation method (SPM) for distributed clutter. Finally, the required resolution and bandwidth of the system are presented.

There is a significant need for portable, low-cost, and multi-functional ground and airborne radars. In some applications, the goal is to integrate multiple functions within a limited and constrained frequency band. For other applications, however, the challenge is how to support multiple missions that have been associated with separated frequency bands. This study considers the feasibility of two possible solutions for multifunction radar: the multi-band, agile frequency diversity radar, and a broad-band, common radar aperture. For either of the solutions, the goal is to find the optimal architectures and enabling technologies that support the integration of the following mission requirements: (1) automatic precise landing support, (2) counter-threat detection (such as counter-drone) in ground and airborne operations, (3) 3Daltitude- finding, (4) portable local weather surveillance. We investigated the basic requirements of each of these missions and summarized the trade analysis results. Next, we investigated the current enabling technologies for two proposed aperture options, especially regarding the design of radiating elements. Multiple antenna element designs were studied, which show promise for meeting multi-band or broadband aperture needs. A simulation verification based on the Phased Array System Simulator (PASIM) technology, which was jointly developed with MathWorks, is introduced. The simulation serves as a method to evaluate the quality of performance (QoP) of different radar missions that can be achieved with specific architectures. We investigated a mixed-signal transceiver and GPU-enabled backend software system that allows for the parallel execution of multiple radar missions through the same aperture.

Some performance characteristics of ordered-statistics CFAR (OS-CFAR), such as probability of false alarm (P_FA) and probability of detection (P_D), are controlled by many parameters. Some of these parameters can be considered decision parameters, since they are user defined to achieve a fixed P_FA, and an optimized P_D. However, other parameters that control these probabilities have the tendency to fluctuate based on the radar environment and operating conditions. These environmental variables can sometimes be difficult to predict and may affect performance. In this paper, a robust decision making method is presented, which selects decision parameters that provide robust performance even in the presence of these variations. The relevant environmental variables investigated in this paper are the number of interfering targets within the detection window and the signal-to-noise ratio (SNR). The Forward Automatic Order Selection Ordered Statistics Detector (FAOSOSD) is used to provide an estimate for the number of interfering targets, and the accuracy of this estimate is observed as a function of SNR. The proposed method defines a performance metric and observes its mean and variance over the uncertain parameter SNR. A trade-off behavior is shown between this mean and variance, and using information elasticity analysis, a decision is selected.

The calibration and inspection of various antennas related to navigation, aviation and flight operations has been a big challenge for agencies such as FAA and DoD. These antennas include both ground and airborne components. Antenna systems at ground infrastructure include navigational aide systems such as VOR/LOC, TACAN/DME, and Glide Slope, and include the ground-based surveillance radars. The antennas mounted on the aircraft include various aviation probe antennas and airborne radars. The flight inspection mission requires precise measurement of signal power at locations around any facility. Calibration of airborne radar antenna mounted on aircraft is also needed for precise radar functions. The difficulties, however, lie in the fact that the aircraft body and the environment have significant impacts on the signal measurement quality, which is usually difficult to characterize. This work focuses on how the airframe affects the typical aviation antenna measurements, and a possible way to “normalize” such impacts to gain the desired “effective” radiation patterns. We mainly reply on computational electromagnetic (CEM) tools to establish the physical scattering model of the aircraft with respect to different simplified antenna models, and then validate the radiation patterns through actual flight test data collections. Initial comparisons between the simulations and flight measurements reveal some interesting behaviors of radiation patterns on the aircraft installations, further issue of electromagnetic compatibility in the complex aircraft operations, and the potential of using unmanned aerial systems (UAS) to automate the measurement procedure in the future.

It is a common assumption that more effective decisions can be made in radar sensor processing if more “information" is available. However, this is not always the case since more information may not necessarily translate to better sensor performance due to a phenomenon known as information overload. What determines the effectiveness of a decision is rarely only dependent on a single performance metric. Instead, there are often multiple performance metrics involved. This necessitates the use of cost functions for modeling a loss in decision effectiveness due to deviations from the optimal values of the performance metrics. These optimal values are also often determined in a fuzzy/subjective manner by an individual using language. Two different types of statements are looked at and modeled, namely, soft and hard constraining statements. These models, called language-based cost functions (LBCFs), are derived and their properties are explored. These can then be easily combined, via a summation, to obtain a smooth objective function. The objective function is then used to find the utility of the radar system. By altering the systems parameter values the amount of information introduced into the system can be varied and the point at which the utility of the radar system decreases due to excess information can be found. This is then regarded as the information overload point or the point at which more information causes a decrease in the utility of the system. An example of a radar system with imposed constraints is provided and its resulting information overload point is found by utilizing LBCFs.

Sandia National Laboratories' Radar Intelligence, Surveillance and Reconnaissance (ISR) systems enable a new product paradigm in radar capabilities and modalities. With the ability to shrink sensor size, increase resolution, raise image quality, and advance real–time on–board processing, Sandia has been dedicated to producing the continuous cycle of next generation systems for nearly three decades. Sandia specializes in the full system design of exquisite Synthetic Aperture Radar (SAR), Ground Moving Target Indicator (GMTI), target recognition and other sensor systems for the Department of Defense, Other Government Agencies and industry partners.

Three successive experiments are conducted to determine the viability of using harmonic radar to detect electronic targets-of-interest. For the first experiment, the radar transmit signal is directly injected into the front-end of each target to determine whether or not the target is sufficiently nonlinear and will re-radiate harmonics that may be used for its detection. For the second experiment, the radar transmit signal is applied indirectly, in the form of field energy, to the target inside of a transverse-electromagnetic cell, to determine whether or not the target will generate its own field energy that may be captured by a radar receiver. The third experiment, constructed using a wideband radar antenna, confirms that each target is detectable remotely. In this paper, the experiments are demonstrated using four handheld radios and ultra-high frequencies (UHF). Modifying the experiments to accommodate different targets and lower/higher frequencies is straightforward. A list of best-practices for minimizing system-generated distortion (while adapting the authors’ experiments to new applications) is provided.

Harmonic radar is used in solving the problem of detecting and localizing electronic devices within a given measurement scene. The underlying measurement principle utilizes the frequency conversion, mixing and harmonics generation of the harmonic radars transmit signal in systems with nonlinear transfer functions. The nonlinearities in the signal transfer behavior of the targets of interest are due to electronic circuit elements and predominantly semiconductor components. Comparable to classic radar imaging techniques, it is possible to generate maps that give knowledge about the presence and the location of electronic devices within the observed scene. This work presents the current research done at the German Aerospace Center (DLR) in the field of harmonic radar. The development of a dual-mode (classic and harmonic radar) measurement system is shown. In addition, we present a signal processing algorithm for harmonic radar imaging based on wideband, pre-steered, frequency-smoothed robust capon direction-of-arrival estimation. The developed algorithm is analyzed and compared to classic imaging techniques using synthetic data. Concluding measurement results show the capabilities of the experimental measurement setup and validate the performance of the devised imaging algorithm and system demonstrator.

Various parameters can affect the ground moving target indicator (GMTI) radar performance. One such parameter that may often be overlooked is that of the unintended polarization of the antenna, e.g., the cross-polarization response. This paper discusses the issue of cross-polarization on clutter attenuation performance for GMTI radars.

A large number of scatterers contribute to the total energy received from a resolution cell of a polarimetric synthetic aperture radar(POLSAR). The polarimetric response for each resolution cell is obtained by simultaneously measuring both the amplitude and phase of the scattered field using orthogonal channels. The POLSAR signature from ocean backscatter is primarily produced by the capillary and small gravity waves produced by the local wind. Measurement of low entropy cells from resonant ocean Bragg scatter requires proper multilook processing of the SAR phase histories. Low entropy information is not accessible using the standard averaging of pixels. Scattering entropy allows for the consideration of depolarization. Empirical observations are presented from four-look SIR-C L and C band data from a large set of global ocean surface data. The data trend of alpha versus incidence angle for the L Band four-look SIR-C scenes closely follows some behaviour predicted by an extended Bragg model. A survey is presented for a collection of terrestrial surfaces including agricultural areas, forests, desert terrain, and volcanic surfaces. Analysis of the normalized coherence matrices from four look SIR-C imagery is discussed.

In previous work, we have discussed the use of operators to solve some signal analysis problems that are at the heart of some signal processing and analysis problems. In this paper, we extend the formalism to include noise by the introduction of a noise operator as an alternative way to think about the various signal in noise problems in radar and sonar. In addition, we show how non-linear combinations of signal with noise can be analyzed as noise operators acting on signals can expressed as combinations of operators with random parameters with a given distribution acting on the signal. Then, we show that the noise reverberation or clutter problem can be thought of as a parameter randomized operator acting on signals in either the radar and sonar domain. Then, we note that this formulation lends itself to finding unexpected consequences of non-linear transformations of random variables. Finally, we note a radar problem first addressed by Cartwright and Littlewood in WWII of identifying a spectrum being produced by a non-linearity in a circuit versus noise can be attacked by these methods.

Noise radars have many advantages over conventional radars and receive great attentions recently. The waveforms and their characteristics of a noise radar are the crucial factors that impact the performance of this radar. Noise waveforms are usually generated by random number generators or logistic mapping. In this paper, we propose the kicked rotor quantum chaos perturbation (KRQCP) approach. First, we investigate the characteristics of noise waveforms generated by kicked rotor quantum chaos and demonstrate that like random number generation approach and logistic mapping approach, noise waveforms generated by kicked rotor quantum chaos (KRQC) also show the fractal relation between sidelobe values and code lengths. Then, we propose using KRQC noises to perturb quadratic phases and develop the KRQCP noise waveform generation method. We demonstrate that the performance for the noise waveforms generated by the new method is considerably higher than traditional random number generator and logistic mapping approaches.

The emergence of cognitive radar has led to a considerable amount of research into optimizing a radar's waveform to achieve better performance for a desired task. Specifically, optimizing a waveform's energy spectrum to take advantage of interference, noise, and a target's scattering characteristics has led to a number of approaches for design. Noise radar has emerged as a desirable candidate for radar operation in a tactical environment due to its low probability of interception and detection (LPI/LPD) characteristics, along with the ease of wideband waveform generation. This paper attempts to take a step towards the development of a cognitive noise radar by combining these two areas of research. This is studied by looking at adaptive spectral shaping of noise radar pulses to achieve performance gains in the system. Techniques traditionally applied to stochastic process modeling are utilized to derive a pulse shaping filter that takes advantage of the at spectrum of a white noise pulse to shape it to the desired spectral mask.

In comparing system performance for ground moving target indicator (GMTI) radar systems, various metrics are used. It is highly desirable that the metric be simple and powerful. Ideally it is a single number, or a plot. It is often the case that a single number is not sufficient to describe the radar performance under all operational conditions. In spite of this, it is still common to attempt to use a simple metric, such as the minimum detectable velocity (MDV). This paper discusses the concept of minimum detectable velocity with the goal of showing what this metric attempts to communicate, and what may not be properly communicated by this metric without careful attention. Basic parameters that affect the minimum detectable velocity are presented.

The vibratory response of buildings and machines carries key information that can be exploited to infer their operating conditions and to diagnose failures. Moreover, since vibration signatures observed from the exterior surfaces of structures are intrinsically linked to the type of machinery operating inside of them, the ability to monitor vibrations remotely can facilitate the detection and identification of the machinery. Recently, synthetic aperture radar (SAR) has proven to be a versatile tool capable of performing vibrometry and high-precision vibration-estimation algorithms have been developed for reconstructing surface vibration waveforms from SAR images. However, these algorithms tend to be computationally demanding and, in addition, require knowledge of the exact location of the object a priori. This renders their use as unpractical for exploratory applications. This paper focuses on the detection of vibrating objects by exploiting the phase modulation that a vibration causes in the received slow-time SAR data. Two different vibration detection schemes are investigated. The first scheme is data-driven and utilizes features extracted with the help of the discrete fractional Fourier transform (DFrFT) to feed a random-forest detector. The second scheme is model-based, and uses a probabilistic model of the slow-time SAR signal, the Karhunen-Loeve expansion, and a likelihood-ratio detector. The proposed detection algorithms are tested using both simulated and real SAR data. Our results show that both detection schemes can be used to achieve high-performance vibrating-object detectors.

Micro-Doppler radars have been used to accurately detect and classify human activities in various scenarios. This paper discusses the development of an X-Band micro-Doppler radar in detecting abnormal signatures embedded in micro-Doppler responses from activities such as walking, jogging, and jumping. To synthesize the condition of an abnormal gait, five test subjects were asked to perform the activities while wearing shoes, being barefoot, and wearing shoes accompanied by a heel lift insert in one shoe. Abnormal gait detection was performed using micro-Doppler responses of the various activities measured over two different look angles. The Short-Time Fourier Transform (STFT) was used to analyze the micro-Doppler characteristics along with Cadence Frequency Diagrams (CFD). Feature extraction was performed on the micro-Doppler responses for acquiring unique hand-crafted features commonly used in literature for gait analysis by micro- Doppler radar measurements. A secondary analysis utilized dimensionality reduction through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to extract unique signatures and perform abnormal gait classification. An in-depth evaluation was performed on the different feature sets by the Weight K-Nearest-Neighbor (WKNN) and Support Vector Machine (SVM) classifier algorithms that determined the feasibility of discriminating between individuals performing the activities. The research allows for future determination of abnormal motion in specific activities via micro-Doppler response and machine learning that can further emphasize the ability of micro-Doppler radar to perform abnormal gait classification.

Through-wall/through-barrier motion sensing systems are becoming increasingly important tools to find people hidden behind barriers and under rubble. The sensing performance of these systems is best determined with dynamic calibration targets that mimic human breathing and respiration. Potential advantages of these targets are in their reproducibility and accuracy in support of calibration methods for human detection testing in through-wall and through-rubbles situations. Essential ingredients of an ideal calibration target are: (1) its RCS should be as close to that of a human, (2) it must be easy to fabricate and deploy, (3) it must present essentially a constant RCS value at all aspect angles, and (4) it must be durable with respect to laboratory and field test conditions. An ideal calibration target is a sphere. However, we have determined that it is possible to mimic a sphere using a polyhedron with a limited number of faces. We are in the process of constructing an expandable metallic polyhedron target of appropriate size and number of faces for experimental measurements under various through-wall and through-rubble operational scenarios. This paper presents simulation results on the dynamic RCS variations of the calibration target as it expands and contracts to simulate human breathing.

This paper discusses the various millimeter-wave radar micro-Doppler features of consumer drones and birds which can be fed to a classifier for target discrimination. The proposed feature extraction methods have been developed by considering the micro-Doppler signature characteristics of in-flight targets obtained with a frequency modulated continuous wave (FMCW) radar. Three different drones (DJI Phantom 3 Standard, DJI Inspire 1 and DJI S900) and four birds of different sizes (Northern Hawk Owl, Harris Hawk, Indian Eagle Owl and Tawny Eagle) have been used for the feature extraction and classification. The data for all the targets was obtained with a fixed beam W-band (94 GHz) FMCW radar. The extracted features have been fed to two different classifiers for training (linear discriminant and support vector machine (SVM)). It is shown that the classifiers using these features can clearly distinguish between a drone and a bird with 100% prediction accuracy and are able to differentiate between various sizes of drones with more than 90% accuracy. The results demonstrate that the proposed algorithm is a very suitable candidate as an automatic target recognition technique for a practical FMCW radar based drone detection system.

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. A "horizontal key" of data elements is calculated – preferably in parallel – for each of an arbitrary set of x-coordinates in image-space. Imagespace "color" data are then calculated – preferably in parallel – at an arbitrary set of image coordinates. The method is greatly simplified for SAR I&Q samples on trapezoidal/keystone grids. The resulting images can be computed quickly, with accuracy exceeding back projection techniques.

While convolutional neural networks (CNNs) are powerful tools in machine learning, their construction is far from a science. In addition, instantiations of CNNs are highly memory expensive and typically require large training sets. Wavelet scattering networks (WSNs) could provide a simple means of testing quantization schemes for CNNs, without the added complexity of adjustable parameters. Using the MSTAR database, the performance of a WSN in combination with several quantization schemes is examined.

With the rapid development of artificial intelligence technology and the emergence of a large number of innovative theories, the concept of deep learning is widely used in object detection, speech recognition, language translation and other fields. One of the important practices is target recognition in SAR images. Although it shows certain effectiveness in some researches, when using deep learning algorithm, there are still many problems that have not yet been solved. For example, the convergence of algorithms has not shown intuitively, although the high precision of experimental results can be obtained. There are many reasons that lead to the results divergence, such as the size of the database, the type of model, and the algorithm used in the experiment. This paper aims at analyzing the factors that influence the convergence of the results from the perspective of the CNN algorithm. The goal can be achieved by means of constraint of convergence condition. Firstly, by controlling the amount of data in the database, the influence of the size of the database will be determined. Secondly, the radius of convergence will be analyzed, based on which, the scope of application will be found. Combining the above two factors, a corresponding method can be given in the final paper, which gives rise to the convergence of the result. Finally, the correctness of the above theories will be explained by conducting experiments using the MSTAR database.

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations in order to understand relevant object phenomena. It has been shown in a previous work that deep learning may be used to increase the efficiency of creating such datasets by providing estimations comparable to simulation results. In this work, we further investigate the utility of deep learning for electromagnetic simulation prediction by adding to the existing training and testing dataset while also incorporating additional material properties. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to further investigate the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Additionally, performance is compared for different data pre-processing techniques focused on data reduction.

Operational reliability has been shown to be a useful measure of the effectiveness of a radar's detection algorithm in a specific operational scenario. Operational reliability transforms traditional detection theory parameters into a metric that can be combined with component reliability, thus setting the foundation for the formulation of a radar's total reliability. Component reliability can be divided into two main areas of study: lifetime prediction and degradation characterization. The former focuses on the component's mean-time-to- failure (MTTF), while the latter investigates the slow decrease of a component's performance throughout its lifetime until the component eventually fails. In this paper, we explore the degradation of common radio frequency (RF) components that are used in a radar system. The degradation of the components is then used in conjunction with the radar's operational reliability to develop a model for the overall reliability of a radar system.

Advancements in wireless technology have led to an increased demand in the enhancement of wireless security, especially in indoor environments as GPS and cellular services degrade in performance. Recent developments in wireless security for indoor environments have focused mainly on developing radio frequency fingerprinting approaches through machine learning for device classification or localization. The work performed and discussed herein describes a developed system that can simultaneously perform device classification and localization in indoor environments using designed vector sensing antenna and artificial intelligence concepts. The devices evaluated are considered to be non-cooperative emitters that convey wideband code division multiple access (WCDMA) information found in universal mobile telecommunication systems (UMTS). However, the designed approaches can be extended to other protocols such as Global System for Mobile communications (GSM), Long-Term Evolution (LTE), and Code Division Multiple Access (CDMA). Device classification is performed in line-of-sight (LoS) scenarios with a developed vector sensor based on statistical features are extracted from the received power spectra and evaluated by two machine learning models, i.e. support vector machine (SVM) and weighted-K-nearest neighbor (WKNN). The final analysis experimentally validates the localization of the UMTS devices in an indoor environment by means of a comparison between dimensionally reduced features extracted from a short-time Fourier transform matrix along with three-dimensional received signal strength features, all acquired by the designed vector sensor antenna. Extension to other wireless protocols is assessed by evaluation of narrowband GSM signals for localization whilst being compared to the localization performance of the wideband UMTS non-cooperative emitters via WKNN.

Advances in space-based geospatial remote sensing technologies, especially interferometric synthetic aperture radar (InSAR), have enabled precise monitoring of land surface deformations due to geologic and human processes such as earthquakes, volcanoes, and mining. The success of infrastructure monitoring using InSAR techniques is sometimes limited because a distinguishable radar target is not located at the specific point of interest. To overcome this limitation corner reflectors (CRs) are typically employed to provide detectable, measurable targets at the desired locations. However, CRs are cumbersome three-dimensional structures that are difficult to install and align on infrastructure in an inconspicuous manner that also preserve the aesthetic and structural integrity of the installation site. In this research we developed and tested a two-dimensional Van Atta (VA) array retro-reflector to replace the conventional CR in InSAR remote sensing applications. The two-dimensional retro-reflective array can be discreetly flush mounted onto new or existing infrastructure, thereby preserving the aesthetics of the installation site, and will be less vulnerable to vandalism or damage from the elements (rain, snow, wind). A novel antenna element composed of micro-strip patches and Substrate Integrated Waveguides (SIW) was developed and used in the VA array design. Electromagnetic modeling and simulation using the FEKO code was performed to streamline the design process. To investigate array performance versus Radar Cross Section (RCS), a reconfigurable array, based on an elemental (16 x 16 element) array panel, was employed. Increasing or decreasing the number of panels used to assemble the array increased or decreased the RCS. We designed and fabricated nine VA arrays which were assembled into various multi-panel VA geometries. RCS and retroreflective beam pattern were measured in the lab to confirm that they met the design specifications. Field validation was performed on the VA arrays, in addition to a reference corner reflector, custom designed for this project. Multiple high resolution TerraSAR-X satellite images have been acquired, processed, and analyzed using existing COTS software and routines developed at SwRI. Results indicate that the Van Atta arrays are a viable alternative to CRs and can be potentially employed in a variety of applications including military, hazard reduction and climate change.

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. Furthermore investigations on unknown space objects or satellites can be performed. 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 multipurpose 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 high-power 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 1.8 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 orbit pass 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. It shows the main error sources and solutions as well as the calibration effort to generate the first centimeter-resolution radar image observed with IoSIS.

In a previous paper we discussed some potential applications of the Aharonov Ansatz to antenna theory. In this paper we illustrate the method based on the two scattering theoretic operators that formalize what we wish to extract from a return signal. The delay operator and the time warping operator can be used to define the properties of a phased array antennas in terms of steering command (delay operator) its beam shaping by altering the phase delay (warping operator) along an individual aperture. In these terms, the synthesis problem to produce an ideal radiation problem in by using a phased array antenna amounts to solving an inverse problem: taking a desired pattern in the far field and determining what combination of operators acting in combination will produce that pattern. Likewise, if we want to produce an effect in the near field from a given model of an antenna, then we are solving the direct problem of determine how a given radiation pattern induces a current distribution.

Fiber-reinforced polymer (FRP) composites are a class of materials consisting of polymer matrices infused with high-strength fibers, such as glass, aramid, or carbon. The uses of FRPs, such as carbon fiber reinforced polymers (CFRP), are widespread throughout many military and civil applications due to their high-strength properties and lightweight nature. During the lifespan of a structure reinforced with an FRP, many scenarios arise which threaten the integrity of the material, which may cause minor air gaps within the layers of the material. Microwave non-destructive testing serves as a potentially non-contact one-sided approach for detection of internal or superficial defects. An issue that arises is that the sample under test, in many cases, does not maintain a uniform shape, making assessment of the material's state difficult. This is a result of the microwave response being more heavily weighted on the shape of the material, rather than potential defects. To accommodate for this, elaborate systems must be designed to minimize the likelihood that non-visible defects may be overlooked. Ultrawideband (UWB) noiselet waveforms, along with the anisotropic nature of CFRP, allow for the reduction of this likelihood. In this research effort, we include the derivation of the noiselet waveform, give a description of the UWB system used for testing, and present results of tests conducted on a CFRP sample back with an aluminum sheet, which has an air gap fabricated within the layers.

This paper explores the effect that impact damage has on the microwave reflection coefficient of unidirectional carbon-fiber reinforced polymer (CFRP) circuit analog absorbers (CAA). Such damage can negatively impact the electromagnetic properties of radar absorbing materials (RAM) and degrade their performance. Three unidirectional CFRP samples comprised of a unidirectional CFRP lamina and foam spacer are fabricated using a wet-layup technique. The samples under test (SUTs) are then individually subject to controlled impact tests. A custom damage apparatus drops and guides a variable weight impactor from a known height towards the center of the SUT. After each damage phase, the complex relative permittivity, surface impedance, and the reflectivity when the samples are placed in an absorber configuration are measured from 4-18 GHz using a free- space measurement system. The change in these properties due to the impact are investigated as a function of frequency, angle, and damage.

This work evaluates the performance of a cognitive radar system which predicts and avoids radio frequency interference (RFI) through an alternating renewal process (ARP) model-based and Markov Decision Process (MDP) approach. As radio frequency (RF) environments grow more crowded, the need for such a system becomes necessary. The cognitive radar monitors the RF activity to train a model for RFI prediction and avoidance. By modeling activity as an alternating renewal process, the stochastic approach calculates the likelihood of interference from measured RFI statistics. Alternatively, the MDP uses reinforcement learning to determine the optimal sequence of decisions given measured RF activity. Both methods eventually select the widest radar transmit bandwidth to minimize interference. The performance of each approach is evaluated by the number of collisions and missed opportunities. A hardware implemented test-bed deploys both methods on a set of synthetic and real measured RFI spectra in real-time to compare performance with the goal of determining when each process is more beneficial (in terms of performance and complexity).

Cognitive radio networks (CRNs) are currently a rich area of development because of the promise of solving spectrum access challenges such as spectrum management and efficient spectrum use. Ad hoc networks in particular operate without fixed infrastructure and are decentralized in nature. Cognitive radio ad hoc networks (CRAHNs) offer robust connectivity and are highly adaptable, but the challenges in designing medium access control (MAC) protocols for them are the greatest. Rendezvous is a core element to initializing and maintaining a CRAHN; it is the process by which individual network nodes find one another and establish communication links. We propose a type of channel-hopping, adaptive blind rendezvous algorithm that integrates spectrum sensing and spectrum prediction using a non-parametric model of channel occupancy. One of the considerations lacking in many papers on blind rendezvous approaches is how the proposed algorithms contend with primary users (PU) that may be accessing the operating spectrum. Our rendezvous approach, which is called adaptive rendezvous with predictive collision avoidance (ARPCA), is an adaptive derivative of jump-stay developed using the assumption that PU activity is present and collisions between PUs and CRAHN users should be minimized. The channel occupancy prediction approach draws from an existing survival analysis-based DSA algorithm. We briefly explore how conventional jump-stay rendezvous contends with PU activity and use simulation results to illustrate the performance improvement in time-to-rendezvous (TTR) and collision avoidance demonstrated by ARPCA.

A novel approach to radar signal design and processing, the modified transmitted reference (MTR) technique enables multi-resolution timing and synchronization properties for radar systems. The MTR approach performs parasitic processing by supplementing existing signals in the ambient environment. The MTR technique augments ambient signals by generating copies of the waveforms, modifying the copies, and broadcasting the copies. The MTR technique encodes timing information via the relative differences between the ambient and copy signals, rather than the absolute properties of the signal. The MTR method modifies the copies in the ambiguity function domain, thus producing signals with desirable features for radar applications while concurrently facilitating robustness to adverse radar channels. In this paper, the modified transmitted reference technique is developed, and an application of the technique to the problem of wireless beaconing is presented. Modified transmitted reference wireless beaconing employs spatially distributed, coordinated signal transceivers that broadcast and collect wide-bandwidth data. Each transceiver coordinates with all other nodes to emit a cooperative waveform that defines a modified transmitted reference beaconing transmission. At signal reception, modified transmitted reference receivers processes the signals reflected from objects in the environment using the approach presented herein. Modified transmitted reference wireless beaconing systems enable discovery and localization of objects of interest, and may find utility in a broad range of applications.

Separation, detection and classification of multiple frequency modulation radio frequency (FMRF) signals are important in modern communication and radar environments. However, these problems are very challenging when multiple FMRF signals come from same locations, occupy in same frequency bands and transmit at same time. In this paper, we propose manifold representation and polynomial chirplet approach to separate, detect and classify multiple-FMRF-mixed signals. In the proposed approach, each FMRF signal is described by a one-dimensional manifold that is imbedded in a two-dimensional time-frequency space. This one-dimensional manifold is locally characterized by a vector that represents polynomial coefficients. Multiple FMRF signals, which may be inseparable in time and frequency, can be represented by multiple manifolds separable with polynomial coefficients. Through their separable polynomial coefficients, these inseparable FMRF signals can be separated and classified from the characteristics of their manifolds. In this paper, through simulation, we verify the manifold representation ideas and demonstrate that the proposed method can effectively separate, detect and classify multiple-FMRF-mixed signals even though these noisy FMRF signals are overlapped 100% in time and frequency band.

Much work has been done designing transmit waveforms for target identification, classification, and detection. In addition, these have also been studied in both single and multiple-antenna scenarios. In this work, we study the construction of a waveform when multiple radar sensors are used to image a target scene. The scene is assumed to have a prior distribution given by a Compound Gaussian (CG) - a model that has proven very useful in the field of image processing. Waveform optimization is done with the objective of optimizing mutual information, while reconstruction was performed using sparsity based reconstruction techniques. In our work, the waveform is tailored for a particular target of interest in the scene while suppressing the clutter. Using our waveform techniques, we demonstrate statistically significant improvements in the quality of the reconstructed image in peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). We validate our algorithms using the MSTAR database.

Supported by NASA’s Undergraduate Student Instrumentation Project (USIP), OU-IART worked with Garmin International Inc. to develop a novel, low C-SWaP, high-altitude storm-chasing radar, called MARBLE. The radar operates at 35 GHz (Ka-band), and the concept of operation is flying over the top of a convective storm to measure the reflectivity and vertical motion of the clouds. The system design is extremely simple and low-cost, which uses single-stage up-down conversion between Ka-Band operation and the X-band “base radar transceiver”. The radar is very easy to operate by a pilot, through a simple switch. Laboratory tests and ground verifications were performed, and the system airworthiness has been validated. The systems developments started in spring 2016, and the initial engineering validation flights were performed in Sep 2018, using a NASA high-altitude aircraft (ER-2). Initial flight tests show that MARBLE can operate in high-altitude environments and obtain return signatures from various targets. Further data processing is ongoing to evaluate the effectiveness of precipitation observations. In addition, the design, development and testing process successfully served the educational goals of the program.

During recent years of developing a near-range ground-penetrating radar for explosive hazard detection, re- searchers at the U.S. Army Combat Capabilities Development Command Army Research Laboratory have been focused on developing receiver design parameters that optimize system performance. In general, a radar de- signer often aims to reduce the bandwidth of a receiver because it will result in a reduction in noise floor and analog-to-digital converter requirements. However, if receiver blanking is employed (i.e., the process of pulsing transmission and reception so that they do not occur simultaneously), the system’s chosen bandwidth can negatively impact the effectiveness of receiver blanking. That is to say, the step response of the system (which approximately occurs during the receiver turn-on stage) is dictated by the receiver’s bandwidth. The response can be characterized by its delay and, more importantly, by its rise (or fall) time. The rise and fall time can manifest in a smearing (or ramping) at the near and far boundaries of the illuminated scene of interest. This could lead to missed detections of close-in or far-out targets. The aforementioned is discussed in detail, and the ramifications on near-range synthetic aperture radar image formation is presented.

We generally desire to relate radar data values to the Radar Cross Section (RCS) of a radar target echo. This is essential to selecting proper gain values in a radar receiver, maintaining dynamic range, and to properly interpret the resulting data and data products. Ultimately, this impacts proper radar design. We offer herein a basic analysis of relevant concepts and calculations to properly calibrate a monostatic radar’s echoes with respect to RCS, and to select appropriate receiver gain values.

A practically-realizable micro-Doppler experiment is designed, conducted, and demonstrated. The cost-effective experimental setup is composed of common signal generator, spectrum analyzer, transmitting and receiving antennas, and a metallic rod rotated via a driving stepper motor to induce micro-Doppler effect. For providing different micro- Doppler features, different revolution per minute (RPM) of the rod can be chosen and adjusted. The operating frequency is chosen in S band for long-range radar applications. To consider the demand of real-time computation for national defense purpose, two fast computed algorithms to correctly extract the micro-Doppler feature of a scattered target are proposed and illustrated by clear physical insight. The micro-Doppler effect of cross-polarized scattered signal is also investigated and the effectiveness and robustness of the proposed algorithms are further verified. The experimental result shows that the deviation between theory and measurement is not more than 1 %.

The consumer-level unmanned aerial vehicles (UAV) are small and an increasing treat to personal privacy and public security. Due to the small radar cross section (RCS) property and slow speed, it is difficult to detect UAVs by radar systems. This paper presents the design and analysis of the PLL-based synthesizer for passive or active UAV detection radar applications. The adaptive bandwidth of PLL ensures the loop stability to track exact frequency. In this design, the loop bandwidth is set around 400KHz with 8MHz reference clock. The integer N is programmed from 1 to 128, thereafter the inverse linear current in the charge pump is 64:1. Since the frequency operation ranges from 40MHz to 1.024GHz, the 8-bit end-of-counter (EOC) detector is a better solution for high-speed consideration. Based on a countdown counter configuration, the EOC detector detects the count state to reinitiate the counter to N and the cycle repeats. The PLL-based synthesizer is fabricated in a 90nm digital CMOS process with the core area of 0.24 x 0.47mm2. The core power consumption is 3.6mW from a 1V supply. The PLL-based synthesizer performs that the phase noise at 6MHz (or 8MHz) is below -100dBc/Hz, and below -110dBc/Hz at 12MHz (or 16MHz). The peak-to-peak tracking jitter at 240MHz is 7.2ns, and the RMS tracking jitter is 921ps. The overall settling time of the synthesizer is below 20μs, and the results guarantee the overall performance of the UAV detection radar.

The use of software defined radios (SDRs) for radio frequency (RF) applications has spread to research labs, commercial industry and hobbies in recent years. This is because SDRs are low cost, readily available and software-tunable over a wide range of RF. Many SDRs are capable of full duplex on multiple channels and contain all the RF hardware needed for a wide variety of applications. Unfortunately, this high flexibility and low price point come at a cost of RF performance. This paper illustrates the limitations of SDR RF hardware and the impact of these limitations on radar performance. It then presents a technique for improving radar performance on a SDR.