This paper describes the impact of ground target motion in Synthetic Aperture Radar (SAR) and video SAR mode imagery. The observations provide an approach for optimizing algorithms that detect moving targets by using only the magnitude of a SAR image. A slowly moving target at a constant velocity in the along-track direction or accelerating in the cross-track direction often generates a ridge of intensity that is distinguishable from the background clutter. The direction and location of a detected ridge provide information about the motion of the associated target. The ridge can be approximated as a linear feature and detected using the Hough transform. This approach acts as a complement to detecting the radar shadow of a moving target, improving detection probability. The method is robust enough to discriminate between a ridge associated with a moving target and false alarms due to vegetation, boulders, or stationary manmade objects. Simulated and flight test data collected by General Atomics Aeronautical Systems, Inc. (GA-ASI) validate the method.

Along-track interferometry (ATI) has the ability to generate high-quality synthetic aperture radar (SAR) images and concurrently detect and estimate the positions of ground moving target indicators (GMTI) with moderate processing requirements. This paper focuses on several different ATI system configurations, with an emphasis on low-cost configurations employing no active electronic scanned array (AESA). The objective system has two transmit phase centers and four receive phase centers and supports agile adaptive radar behavior. The advantages of multistatic, multiple input multiple output (MIMO) ATI system configurations are explored. The two transmit phase centers can employ a ping-pong configuration to provide the multistatic behavior. For example, they can toggle between an up and down linear frequency modulated (LFM) waveform every other pulse. The four receive apertures are considered in simple linear spatial configurations. Simulated examples are examined to understand the trade space and verify the expected results. Finally, actual results are collected with the Space Dynamics Laboratorys (SDL) FlexSAR system in diverse configurations. The theory, as well as the simulated and actual SAR results, are presented and discussed.

This paper investigates methodologies for predicting the smear signatures in squinted spotlight synthetic aperture radar (SAR) imagery collections due to surface targets that are undergoing braking maneuvers. Previous analysis considered the case of broadside collection geometries. Analytic computation of a power series expansion is used to compute a generic expression for the down-range and cross-range components of the predicted mover signature. In addition, recent analysis presents capabilities for predicting the full signature shape, including the smear width and interference effects. The current investigations focuses on the effects of squinted collection geometries upon braking targets signatures.

This paper provides analytical principles to relate the signature of a moving target to parameters in a SAR system. Our objective is to establish analytical tools that could predict the shift and smearing of a moving target in a subaperture SAR image. Hence, a user could identify the system parameters such as the coherent processing interval for a subaperture that is suitable to localize the signature of a moving target for detection, tracking and geolocating the moving target. The paper begins by outlining two well-known SAR data collection methods to detect moving targets. One uses a scanning beam in the azimuth domain with a relatively high PRF to separate the moving targets and the stationary background (clutter); this is also known as Doppler Beam Sharpening. The other scheme uses two receivers along the track to null the clutter and, thus, provide GMTI. We also present results on implementing our SAR-GMTI analytical principles for the anticipated shift and smearing of a moving target in a simulated code. The code would provide a tool for the user to change the SAR system and moving target parameters, and predict the properties of a moving target signature in a subaperture SAR image for a scene that is composed of both stationary and moving targets. Hence, the SAR simulation and imaging code could be used to demonstrate the validity and accuracy of the above analytical principles to predict the properties of a moving target signature in a subaperture SAR image.

There is a history and understanding of exploiting moving targets within ground moving target indicator (GMTI) data, including methods for modeling performance. However, many assumptions valid for GMTI processing are invalid for synthetic aperture radar (SAR) data. For example, traditional GMTI processing assumes targets are exo-clutter and a system that uses a GMTI waveform, i.e. low bandwidth (BW) and low pulse repetition frequency (PRF). Conversely, SAR imagery is typically formed to focus data at zero Doppler and requires high BW and high PRF. Therefore, many of the techniques used in performance estimation of GMTI systems are not valid for SAR data. However, as demonstrated by papers in the recent literature,^1-11 there is interest in exploiting moving targets within SAR data. The techniques employed vary widely, including filter banks to form images at multiple Dopplers, performing smear detection, and attempting to address the issue through waveform design. The above work validates the need for moving target exploitation in SAR data, but it does not represent a theory allowing for the prediction or bounding of performance. This work develops an approach to estimate and/or bound performance for moving target exploitation specific to SAR data. Synthetic SAR data is generated across a range of sensor, environment, and target parameters to test the exploitation algorithms under specific conditions. This provides a design tool allowing radar systems to be tuned for specific moving target exploitation applications. In summary, we derive a set of rules that bound the performance of specific moving target exploitation algorithms under variable operating conditions.

BAE Systems has developed a baseline real-time selected vehicle (SV) radar tracking capability that successfully tracked multiple civilian vehicles in real-world traffic conditions within challenging semi-urban clutter. This real-time tracking capability was demonstrated in laboratory setting. Recent enhancements to the baseline capability include multiple detection modes, improvements to the system-level design, and a wide-area tracking mode. The multiple detection modes support two tracking regimes; wide-area and localized selected vehicle tracking. These two tracking regimes have distinct challenges that may be suited to different trackers. Incorporation of a wide-area tracking mode provides both situational awareness and the potential for enhancing SV track initiation. Improvements to the system-level design simplify the integration of multiple detection modes and more realistic SV track initiation capabilities. Improvements are designed to contribute to a comprehensive tracking capability that exploits a continuous stare paradigm. In this paper, focus will be on the challenges, design considerations, and integration of selected vehicle tracking.

Synthetic aperture radar (SAR) imaging is often used to image an area using airborne platforms that generate a large aperture by virtue of the platform motion. Large apertures generate a large synthetic array providing fine cross-range resolution, and together with wide bandwidth waveforms that provide high range resolution, fine resolution images can be generated. SAR algorithms make use of coherent phase compensation from various pulses for focusing and the technique works exceedingly well for scenes containing stationary scattering centers. When moving targets are present, their images are smeared and shifted due to the motion, and to take advantage of this shift, nearby receiver plates are used to form multiple SAR images and together with along track interferometry (ATI), it generates a phase factor that can be used to detect moving target presence.

This paper examines the distribution of the phase variable used in ATI for zero mean Complex Gaussian clutter/target data, and uses the results to address the target in clutter problem as a hypothesis testing problem to compute the probability of detection/false alarm as a function of target to clutter ratio and its velocity.

Wideband radar waveforms that employ spread-spectrum techniques were investigated and experimentally tested. The waveforms combine bi-phase coding with a traditional LFM chirp and are applicable to joint SAR-GMTI processing. After de-spreading, the received signals can be processed to support simultaneous GMTI and high resolution SAR imaging missions by airborne radars. The spread spectrum coding techniques can provide nearly orthogonal waveforms and offer enhanced operations in some environments by distributing the transmitted energy over a large instantaneous bandwidth. The LFM component offers the desired Doppler tolerance. In this paper, the waveforms are formulated and a shift-register approach for de-spreading the received signals is described. Hardware loop-back testing has shown the feasibility of using these waveforms in experimental radar test bed.

Advanced spread spectrum linear frequency modulated (LFM) waveforms are being developed for advanced capability synthetic aperture radar (SAR) and ground moving target indication (GMTI) applications. We have demonstrated by analysis and simulation the feasibility of these new type waveforms and are now in the process of implementing them in hardware. The basic approach is to combine a traditional LFM radar waveform with a direct sequence spread spectrum (DSSS) waveform, and then on receive to de-spread the return and capture the resultant LFM return for traditional matched filter processing and enhanced SAR and GMTI. We show the analysis, simulation and some preliminary hardware results.

In Synthetic Aperture Radar (SAR) the resultant image gives not only the complex reflectivity of image points but also their interdependency with respect to time and observation angle. In range or fast-time changes in reflectivity are expectedly slight, however, in observation azimuth or slow-time the reflectivity pattern, movement or vibration of strong scatterers is revealed. These azimuth signals can subsequently reveal pass to pass changes over inter-pass time or observation elevation. Key to extracting the slow-time signals is the imaging method involved. If imaging preserves the phase function across azimuth then the time or aspect phenomenon riding on top of the phase can be extracted. In other cases the phase is distorted or overridden by imaging artifacts. The choice of imaging method is fundamental in determining not only image resolution but also the fidelity with which secondary signals along the aperture can be determined. The achievable envelope of secondary signal amplitude, bandwidth and resolution are determined here for several imaging methods including the fraction Fourier transform, deramping, range Doppler, chirp scaling, wave-front and matched filtering. Method of extracting these secondary azimuth dependent signals are developed and results are presented for an orbital scenario. Naturally sampling speed, pulse spacing and the flight path in slow-time enclose the largest potential envelope of measurable secondary signals while the selection of imaging method restricts the potential measurable signals to a smaller envelope. Sampling restrictions and bounds on range migration curvature for different imaging methods are also found.

In this paper we present a method for passive radar detection of ground moving targets using sparsely distributed apertures. We assume the scene is illuminated by a source of opportunity and measure the backscattered signal. We correlate measurements from two different receivers, then form a linear forward model that operates on a rank one, positive semi-definite (PSD) operator, formed by taking the tensor product of the phase-space reflectivity function with its self. Utilizing this structure, image formation and velocity estimation are defined in a constrained optimization framework. Additionally, image formation and velocity estimation are formulated as separate optimization problems, this results in computational savings. Position estimation is posed as a rank one PSD constrained least squares problem. Then, velocity estimation is performed as a cardinality constrained least squares problem, solved using a greedy algorithm. We demonstrate the performance of our method with numerical simulations, demonstrate improvement over back-projection imaging, and evaluate the effect of spatial diversity.

As a high resolution radar imaging modality, SAR detects and localizes non-moving targets accurately, giving it an advantage over lower resolution GMTI radars. Moving target detection is more challenging due to target smearing and masking by clutter. Space-time adaptive processing (STAP) is often used on multiantenna SAR to remove the stationary clutter and enhance the moving targets. In (Greenewald et al., 2016),¹ it was shown that the performance of STAP can be improved by modeling the clutter covariance as a space vs. time Kronecker product with low rank factors, providing robustness and reducing the number of training samples required. In this work, we present a massively parallel algorithm for implementing Kronecker product STAP, enabling application to very large SAR datasets (such as the 2006 Gotcha data collection) using GPUs. Finally, we develop an extension of Kronecker STAP that uses information from multiple passes to improve moving target detection.

Performance models are needed for automatic target recognition (ATR) development and use. ATRs consume sensor data and produce decisions about the scene observed. ATR performance models (APMs) on the other hand consume operating conditions (OCs) and produce probabilities about what the ATR will produce. APMs are needed for many modeling roles of many kinds of ATRs (each with different sensing modality and exploitation functionality combinations); moreover, there are different approaches to constructing the APMs. Therefore, although many APMs have been developed, there is rarely one that fits a particular need. Clarified APM concepts may allow us to recognize new uses of existing APMs and identify new APM technologies and components that better support coverage of the needed APMs. The concepts begin with thinking of ATRs as mapping OCs of the real scene (including the sensor data) to reports. An APM is then a mapping from explicit quantized OCs (represented with less resolution than the real OCs) and latent OC distributions to report distributions. The roles of APMs can be distinguished by the explicit OCs they consume. APMs used in simulations consume the true state that the ATR is attempting to report. APMs used online with the exploitation consume the sensor signal and derivatives, such as match scores. APMs used in sensor management consume neither of those, but estimate performance from other OCs. This paper will summarize the major building blocks for APMs, including knowledge sources, OC models, look-up tables, analytical and learned mappings, and tools for signal synthesis and exploitation.

This study investigates vehicle detection performance for an airborne video SAR over increasingly wide collection angles. Using a parameterized video SAR generation, it is possible to see the effects of detection on various frame generation methods and apertures. A cell-averaging CFAR detection algorithm is applied to each frame, searching for vehicles. Results show that increasing the azimuth extent of the video SAR improves detection performance. With some imaging methods, a larger azimuth extent helps to suppress background speckle. Also, a larger azimuth extent improves the chances of imaging a vehicle from a more informative cardinal angle.

An algorithm is presented for synthesizing mathematical models of terrain elevation and re ectivity from digital elevation terrain data (DTED) and national land cover data (NLCD). Assuming the DTED and NLCD have spatial intersection, it is straightforward to interpolate each set individually to a common set of coordinates in the intersection. However, DTED is continuous and NLCD is not typically which results in different and sometimes contrasting sampling requirements of the intersecting region. This study evaluates different similarity measures used to assess the quality of re-sampling DTED and NLCD data for the purpose of building elevation and reflectivity profiles for physical optics calculation of site-specific radar clutter. Examples of the algorithm are presented for clutter scene generation with the Raider Tracer prediction tool.

For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior performance for object recognition in image processing applications. This paper discusses modern training approaches adopted from the image processing literature and shows how those approaches enable significantly improved performance for synthetic aperture radar (SAR) automatic target recognition (ATR). In particular, we show how a set of novel enhancements to the learning algorithm, based on new stochastic gradient descent approaches, generate significant classification improvement over previously published results on a standard dataset called MSTAR.

Typical ATR performance metrics are based on the results of empirical studies on truthed datasets where it is difficult to fully sample the space of expected variation yielding potentially false generalizations of empirical performance results to a rigorous performance assessment. This is especially difficult when many sources of variation can exist in the data, typically referred to as operating conditions. Here, we propose a general method to analytically predict the classification performance of the MPM algorithm when samples are assumed realizations of two separate MPM template parametrizations differing as a function of a single, conditionally independent operation condition. This performance prediction approach is then used to investigate the role the ideal point response has in the classification performance of synthetic aperture radar targets. The exact trade-off we study is coherently processing an aperture to yield a single higher resolution image versus non-coherently processing the aperture to yield multiple lower resolution looks of a scene. Experiments are conducted using SAR imagery from the Air Force Research Laboratories Civilian Vehicle dataset. An additional performance analysis presents an analytic approach to predict algorithm performance under additive white Gaussian noise for a general N_q allowing the performance loss under IPR variations to be mapped to an equivalent loss in signal-to-noise ratio.

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