This PDF file contains the front matter associated with SPIE Proceedings Volume 8746, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

We evaluate a recently reported algorithm for computing frequency-dependent radar imagery in scenarios relevant for performing spectral feature identification. For each image pixel in the spatial domain a computed frequency­ dependent reflectivity is used to produce a corresponding spectral feature identification. We show that this novel image reconstruction technique is capable of considerable flexibility for achieving fine spectral resolution in comparison with previous techniques based on conventional synthetic aperture radar (SAR), yet new challenges are introduced with regard to achieving fine range resolution.

The Born approximation is a common approach taken in modeling the physics of SAR imaging. In essence it says that radiation only scatters once when in space. This is a reasonable assumption for targets that lie far apart or that are far from the transmit and receive antennas, but it introduces error into the imaging process. The goal of this paper is to iteratively compensate for this error by using estimates of the target distribution to estimate multiple scattering phenomena. We will use a noise reduction technique at each iteration on the corrected data as well as the estimated image to control any excess error caused by the estimated multiple scattering phenomena. The physical model for our work will be based on the wave equation. We will briefly derive the important features of the model as well as account for the error brought by common approximations that are made. Typically one does not get an image that is approximately the target distribution, but rather an image that is approximately proportional to the target distribution. This means that there is a scaling parameter that must be chosen when using target distribution estimates to correct data. We will discuss methods for choosing this parameter. We will provide a few basic SAR imaging methods and perform simulation using the Gotcha Data set in combination with the iterative technique. At the end of the paper we will outline future work involving this method.

The increasing demand for wireless data services and communications is expanding the frequency footprint of both civilian and military wireless networks, and hence encroaches upon spectrum traditionally reserved for radar systems. To maximize spectral efficiency, it is desirable for a modern radar system to use waveforms with the ability to fit into tightly controlled spectral regions, which requires the formation of nulls with required notching levels on prescribed frequency stop-bands. Additionally, the waveform should posses a low peak-to-average ratio (PAR), and have good auto-correlation performance. In this work, we propose a novel method for the design of such a waveform using alternating convex optimization. The core module of the proposed algorithm is a fast Fourier transform, which makes the algorithm quite efficient and can handle waveform designs with up to 10⁵ samples. Moreover, our algorithm can achieve a flexible tradeoff between PAR and reduced pass band ripple. A simple application in synthetic aperture radar is considered to highlight the performance of the design algorithm.

The almost unique ability of azimuth deramping to preserve a smooth phase function in azimuth is exploited here to link two disparate spatial processing methods, Direction of Arrival (DOA) localization and Interferometric Synthetic Aperture Radar (IFSAR) and explore the achievable accuracy inherent in their common measurement scenario. Deramping in range quickly provides a …rst component for point source localization. Deramping in azimuth is phase preserving and provides an approximate localization in azimuth that is more accurate over narrower apertures and can be corrected in scenarios involving range migration and for its point source, azimuth location dependence. In cross-track IFSAR two antenna measurements azimuth/elevation DOAs can be calculated from their smooth azimuth functions at each range with a 1 D parametric estimate (exponential model) of point sources. Joint frequency estimates (both antennae) provide the azimuth DOA while the phase di¤erence between antenna amplitude estimates provides the elevation DOA. The cross track antenna measurements can also be processed via the IFSAR methodology producing two SAR images and the phase di¤erence between the two (an interferogram). This provides two images coordinates and a height for each pixel. The connection between the phase history DOA localization and the IFSAR is used to attain accuracy bounds for IFSAR. Extrapolation of the bounds is provided from two spatially un-aliased antennas to IFSAR scenarios with large baseline separations of the antennas. In addition imaging from the azimuth-elevation-range localization data and its ability to minimize layover (building tops imaged closer than their bases) is explored.

A recent circular synthetic aperture radar data collection contained various vehicles and calibration targets placed throughout a 5 km scene. By observing multiple orbits of the radar, the down-range distance measurements to scattering features show noticeable drift on the order of 2 m from orbit to orbit. The large scene contained 14 quad-trihedral calibration targets with radar cross sections that are similar to point targets in the elevation range of the scene. This paper presents an algorithm that uses the quad-trihedrals to generate global range focusing parameters and phase error corrections to the complex range profile. Qualitative and quantitative results show the focusing provides a significant improvement to wide-angle image registration and vehicle target discrimination.

This paper presents a novel algorithm for upsmapling level-1 processed (i.e., focused) Spotlight SAR imagery. A Spotlight Radarsat-2 single look complex (SLC) image for ground-truthed vehicle targets in Long-Harbour, Newfoundland (Canada) is used to demonstrate the applicability of our proposed algorithm. To achieve a finer resolution in the azimuth direction, the Spotlight imaging mode allows for a controllable steering of the radar antenna towards the same ground position. In effect, this creates a time-varying Doppler centroid system, wherein the Doppler centroid varies almost linearly with the platform velocity. Although the focused Spotlight Radarsat-2 SLC imagery is delivered referenced to zero-Doppler, linear variations in the Doppler frequency are preserved along the range direction around the zero-Doppler line. The impact of this effect on SAR image upsampling is pinpointed and accounted for in our proposed upsampling algorithm.

General Atomics Aeronautical Systems, Inc. (GA-ASI) is designing a real-time, video-SAR (synthetic aperture radar) mode for a test bed radar system. Typically, the flight path for video-SAR is circular with the sensor directed inward and broadside relative to the platform heading, providing continuous surveillance over a region of interest. The SAR frames are processed using the backprojection algorithm onto a Cartesian coordinate system at the nominal ground level and oriented in a fixed direction. Standard autofocus techniques are ineffective since the azimuth dimension will be oblique through the images when the synthetic apertures are not centered at a multiple of π/ 2 along the flight path. We have developed an algorithm that estimates the phase gradient from pseudo point-scatterers, regardless of the platform position, and focuses the images before they are compiled into a SAR video. Thus the efficiency and utility of fixed frame video-SAR processing is retained, while image sharpness and quality are not compromised due to antenna motion error measurements and / or severe atmospheric effects during propagation.

This paper details a Video SAR (Synthetic Aperture Radar) mode that provides a persistent view of a scene centered at the Motion Compensation Point (MCP). The radar platform follows a circular flight path. An objective is to form a sequence of SAR images while observing dynamic scene changes at a selectable video frame rate. A formulation of backprojection meets this objective. Modified backprojection equations take into account changes in the grazing angle or squint angle that result from non-ideal flight paths. The algorithm forms a new video frame relying upon much of the signal processing performed in prior frames. The method described applies an appropriate azimuth window to each video frame for window sidelobe rejection. A Cardinal Direction Up (CDU) coordinate frame forms images with the top of the image oriented along a given cardinal direction for all video frames. Using this coordinate frame helps characterize a moving target’s target response. Generation of synthetic targets with linear motion including both constant velocity and constant acceleration is described. The synthetic target video imagery demonstrates dynamic SAR imagery with expected moving target responses. The paper presents 2011 flight data collected by General Atomics Aeronautical Systems, Inc. (GA-ASI) implementing the video SAR mode. The flight data demonstrates good video quality showing moving vehicles. The flight imagery demonstrates the real-time capability of the video SAR mode. The video SAR mode uses a circular shift register of subapertures. The radar employs a Graphics Processing Unit (GPU) in order to implement this algorithm.

Legacy synthetic aperture radar (SAR) exploitation algorithms were image-based algorithms, designed to exploit complex and/or detected SAR imagery. In order to improve the efficiency of the algorithms, image chips, or region of interest (ROI) chips, containing candidate targets were extracted. These image chips were then used directly by exploitation algorithms for the purposes of target discrimination or identification. Recent exploitation research has suggested that performance can be improved by processing the underlying phase history data instead of standard SAR imagery. Digital Spotlighting takes the phase history data of a large image and extracts the phase history data corresponding to a smaller spatial subset of the image. In a typical scenario, this spotlighted phase history data will contain much fewer samples than the original data but will still result in an alias-free image of the ROI. The Digital Spotlight algorithm can be considered the first stage in a “two-stage backprojection” image formation process. As the first stage in two-stage backprojection, Digital Spotlighting filters the original phase history data into a number of “pseudo”-phase histories that segment the scene into patches, each of which contain a reduced number of samples compared to the original data. The second stage of the imaging process consists of standard backprojection. The data rate reduction offered by Digital Spotlighting improves the computational efficiency of the overall imaging process by significantly reducing the total number of backprojection operations. This paper describes the Digital Spotlight algorithm in detail and provides an implementation in MATLAB.

This paper examines the signature characteristics of moving targets in spotlight synthetic aperture radar (SAR) image data. This analysis considers the special case in which the radar sensor is assumed to move with constant speed and heading on a level flight path with broadside imaging geometry. It is shown that the resulting defocused smear signature in the spotlight SAR image exhibits range migration effects, as has been shown previously for strip map SAR analysis. In particular, cases of uniform target motion exhibit simply curved range migration paths, whereas non-uniform target motion can cause complicated smear shapes.

Wide-area persistent radar video offers the ability to track moving targets. A shortcoming of the current technology is an inability to maintain track when Doppler shift places moving target returns co-located with strong clutter. Further, the high down-link data rate required for wide-area imaging presents a stringent system bottleneck. We present a multi-channel approach to augment the synthetic aperture radar (SAR) modality with space time adaptive processing (STAP) while constraining the down-link data rate to that of a single antenna SAR system. To this end, we adopt a multiple transmit, single receive (MISO) architecture. A frequency division design for orthogonal transmit waveforms is presented; the approach maintains coherence on clutter, achieves the maximal unaliased band of radial velocities, retains full resolution SAR images, and requires no increase in receiver data rate vis-a-vis the wide-area SAR modality. For N_t transmit antennas and N samples per pulse, the enhanced sensing provides a STAP capability with N_t times larger range bins than the SAR mode, at the cost of O(log N) more computations per pulse. The proposed MISO system and the associated signal processing are detailed, and the approach is numerically demonstrated via simulation of an airborne X-band system.

In a previous SPIE paper we described several variations of along-track interferometry (ATI), which can be used for moving target detection and geo-location in clutter. ATI produces a phase map in range/Doppler coordinates by combining radar data from several receive channels separated fore-and-aft (along-track) on the sensor platform. In principle, the radial velocity of a moving target can be estimated from the ATI phase of the pixels in the target signature footprint. Once the radial velocity is known, the target azimuth follows directly. Unfortunately, the ATI phase is wrapped, i.e., it repeats in the interval [-π, π], and therefore the mapping from ATI phase to target azimuth is non-unique. In fact, depending on the radar system parameters, each detected target can map to several equally-likely azimuth values. In the present paper we discuss a signal processing method for resolving the phase wrapping ambiguity, in which the radar bandwidth is split into a high and low sub-band in software, and an ATI phase map is generated for each. By subtracting these two phase maps we can generate a coarse, but unambiguous, radial velocity estimate. This coarse estimate is then combined with the fine, but ambiguous estimate to pinpoint the target radial velocity, and therefore its azimuth. Since the coarse estimate is quite sensitive to noise, a rudimentary tracker is used to help smooth out the phase errors. The method is demonstrated on Gotcha 2006 Challenge data.

In along-track synthetic aperture radar systems, measurements from multiple phase centers can be used to remove bright stationary clutter in order to detect and estimate moving targets in the scene. The effectiveness of this procedure can be improved by increasing the number of antennas in the system. However, due to computational and communication constraints, it may be prohibitive to use a large number of antennas. In this work, an efficient resource allocation policy is provided to exploit sparsity in the scene, namely that there are few targets relative to the size of the scene. It is shown that even with limited computational resources, one can have significant estimation and computational gains over non-adaptive strategies. Moreover, the performance of the adaptive strategy approaches that of an oracle policy as the number of the stages grows large.

Shape- and motion-reconstruction is inherently ill-conditioned such that estimates rapidly degrade in the pres­ence of noise, outliers, and missing data. For moving-target radar imaging applications, methods which infer the underlying geometric invariance within back-scattered data are the only known way to recover completely arbitrary target motion. We previously demonstrated algorithms that recover the target motion and shape, even with very high data drop-out (e.g., greater than 75%), which can happen due to self-shadowing, scintillation, and destructive-interference effects. We did this by combining our previous results, that a set of rigid scattering centers forms an elliptical manifold, with new methods to estimate low-rank subspaces via convex optimization routines. This result is especially significant because it will enable us to utilize more data, ultimately improving the stability of the motion-reconstruction process. Since then, we developed a feature- based shape- and motion-estimation scheme based on newly developed object-image relations (OIRs) for moving targets collected in bistatic measurement geometries. In addition to generalizing the previous OIR-based radar imaging techniques from monostatic to bistatic geometries, our formulation allows us to image multiple closely-spaced moving targets, each of which is allowed to exhibit missing data due to target self-shadowing as well as extreme outliers (scattering centers that are inconsistent with the assumed physical or geometric models). The new method is based on exploiting the underlying structure of the model equations, that is, far-field radar data matrices can be decomposed into multiple low-rank subspaces while simultaneously locating sparse outliers.

Feature-aided tracking of targets in synthetic aperture radar is a topic of increasing interest. The aperture synthesized through the combination of target and platform motion facilitates the application of two-dimensional target recognition algorithms through noncooperative imaging of the target in question. Many non-parametric inverse synthetic aperture radar imaging techniques maximize image sharpness by estimating the phase error imposed by the unknown target motion. The resultant images can suffer from small unresolved phase errors and ambiguous cross range resolution. Downstream image exploitation algorithms must be robust to these effects. A set of civilian vehicles is investigated, which exacerbates image quality based ISAR algorithms due to their comparatively small radar cross section. This paper addresses the feasibility of peak-based classifcation of civilian targets moving through challenging tracking scenarios using ISAR images. Classifier performance is evaluated over a set of sensor, target, and environmental operating conditions through use of synthetically generated data.

In this paper we compare coherent change detection performance obtained using the maximum likelihood estimate (MLE) of the SAR image-pair coherence versus using the complex correlation coefficient coherence estimate (CCD). We also compare the non-coherent change detection performance (P_D vs. P_FA) versus the performance of the coherent change detection algorithms.

Modern radar systems equipped with agile-beam technology support multiple modes of operation, including, for example, tracking, automated target recognition (ATR), and synthetic aperture radar imaging (SAR). In a multimode operating environment, the services compete for radar resources and leave gaps in the coherent collection aperture devoted to SAR imaging. Such gapped collections, referred to as interrupted SAR, typically result in significant image distortion and can substantially degrade subsequent exploitation tasks, such as change detection. In this work we present a new form of exploitation that jointly performs imaging and coherent change detection in interrupted environments. We adopt a Bayesian approach that inherently accommodates different interrupt patterns and compensates for missing data via exploitation of 1) a partially coherent model for reference-pass to mission-pass pixel transitions, and 2) the a priori notion that changes between passes are generally sparse and spatially clustered. We employ approximate message passing for computationally efficient Bayesian inference and demonstrate performance on measured and synthetic SAR data. The results demonstrate near optimal (ungapped) performance with pulse loss rates up to ∼ 50% and highlight orders of magnitude reduction in false alarm rates compared to traditional methods.

Conventional synthetic aperture radar (SAR) Coherent Change Detection (CCD) has been found to be of great utility in detecting changes that occur on the ground. The CCD procedure involves performing repeat pass radar collections to form a coherence product, where ground disturbances can induce detectable incoherence. However there is always a difference in the radar collection geometry which can lead to incoherent energy noise entering the CCD. When sensing flat terrain in a far-field regime, the incoherence due to collection geometry difference can be removed through a conventional global Fourier image support trimming process. However, it has been found that when the terrain is either in a near-field regime or contains non-flat topography, the optimal trimming process is substantially more involved, so much so that a new per-pixel SAR back-projection imaging algorithm has been developed. The new algorithm removes incoherent energy from the SAR CCD collection pair on a per-pixel basis according to the local radar geometry and topography, leaving a higher coherence CCD product. In order to validate the approach, change detection measurements were conducted with GB-SAR, a ground-based indoor radar measurement facility.

In this paper we present a new method for restoring multi-pass synthetic aperture radar (SAR) images containing arbitrary gaps in SAR phase history data. Frequency and aspect gaps in SAR image spectrum manifest themselves as artifacts in the associated SAR imagery. Our approach, which we term LDREG for the (cursive ell);₁ difference regularization, jointly processes multi-pass interrupted data using sparse magnitude and sparse magnitude difference constraints, and results in improved quality imagery. We find that the joint processing of LDREG results in coherent change detection gains over independent processing of each data pass. To illustrate the capabilities of LDREG, we evaluate coherent change detection performance using images from the Gotcha SAR.

The following work discusses IAA’s approach to tackling the wide angle, circular spotlight, synthetic aperture radar (SAR) problem from the 2008 Gotcha wide angle SAR data set, which is publicly released, with unlimited distribution. This data set comes with a MATLAB image formation routine and attendant graphical user inter- face (GUI). We begin by introducing a simple approach to focusing the collected phase history data that utilizes point targets (quadrahedral targets) present in the scene. Two SAR imaging algorithms are then presented, namely, the data-independent backprojection (BP) algorithm and the data-adaptive sparse learning via itera- tive minimization (SLIM) algorithm. These imaging approaches are compared using the 2008 Gotcha wide angle SAR data to perform both a clutter discrimination experiment, as well as an automatic target recognition (ATR) experiment. The ATR system is composed of a target pose and target center estimation preprocessing system, and includes a novel target feature for the final classification stage. Empirical results obtained by applying the focusing approach and imaging algorithms to the 2008 Gotcha wide angle SAR data set are presented and described. The results presented highlight the benefit of applying the SLIM algorithm over its data-independent counterpart, as well as the utility of the novel target feature.

This paper discusses the problem of robust allocation of unmanned vehicles (T.N) to targets with uncertainties. In particular, the team consists of heterogeneous vehicles with different exploration and exploitation abilities. A general framework is presented to model uncertainties in the planning problems, which goes beyond traditional Gaussian noise. Traditionally, exploration and exploitation are decoupled into two assignment problems are planned with un-correlated goals. The coupled planning method considered here assign exploration vehicles based on its potential influence of the exploitation. Furthermore, a fully decentralized algorithm, Consensus-Based Bundle Algorithm (CBBA), is used to implement the decoupled and coupled methods. CBBA can handle system dynamic constraints such as target distance, vehicle velocities, and has computation complexity polynomial to the number of vehicles and targets. The coupled method is shown to have improved planning performance in a simulated scenario with uncertainties about target classification.

Recent years have seen growing interest in exploiting dual- and multi-energy measurements in computed tomog­ raphy (CT) in order to characterize material properties as well as object geometry. Materials characterization is performed by decomposing the scene into constitutive basis functions, such as Compton and photoelectric scattering functions used here. While well motivated physically, the joint recovery of the spatial distribution of photoelectric and Compton properties is severely complicated by the lack of sensitivity in the data to photoelec­ tric variations. Moreover, while we have prior knowledge of Compton and photoelectric coefficients for materials of interest, this prior knowledge is imperfect and the true physical properties may assume a range of values. We propose a model-based iterative approach which accounts for the polyenergetic nature of computed tomography and includes patch based regularization terms to stabilize inversion of photoelectric coefficients. Further, we use a level set-based method to provide high spatial resolution for materials of interest, allowing initial estimates of material properties to be adjusted within a user-specified range. Initial results indicate that this approach is promising for future dual- and multi-energy CT systems with enhanced material characterization capabilities, for use in airport baggage screening and potentially in medical imaging.

Point cloud data present a broad swath of intriguing problems in signal processing. Namely, the data may be sparse, may be non-uniformly sampled in space and time, and cannot be processed directly by way of conventional techniques such as convolutional filters. This paper addresses such data under the application umbrella of remote sensing. Specifically, we examine the potential of interferometric synthetic aperture radar for detecting geohazards that affect transportation. Using sparsely distributed coherent scatterers on the ground, our algorithms attempt to locate events in process such as sinkholes in the vicinity of highways. Theoretically, the problem boils down to the detection of Gaussian-shaped changes that evolve predictably in space and time. The solution to the detection problem involves two basic approaches, one grounded in pattern matching and the other in statistical signal processing. Essentially, the spatiotemporal pattern matching extends a Hough-like voting algorithm to a method that penalizes deviation from the known model in space and time. For confirmation of geohazard location, we can exploit a fixed-time analysis of the distribution of subsidence from the point cloud data by way of computing mutual information. Results show that the detection and screening strategies conform to geological evidence.

The potential applicability of multiple-channel coherence estimation in situations where one channel contains a noise-free signal replica (as in active radar) or a high-SNR reference signal (as in passive coherent radar) has been proposed in recent work. Invariance of the distribution of M-channel coherence estimate statistics, including recently derived variants optimized for detection of signals having known rank, to the presence of a strong signal on one channel provided all channels are independent and the other M 􀀀 1 channels contain only noise enables the desired use of these statistics without altering detection thresholds designed to provide desired false-alarm probabilities. Traditionally, multiple-channel detection using coherence estimates has assumed that time series data from all channels are aggregated at a fusion center. Mitigation of this requirement to demand global aggregation of only scalar statistics that can be computed locally by sharing of data between pairs of nodes has been explored, and the use of maximum-entropy methods to provide surrogate statistics for pairs of nodes that are not in direct communication within a network has been proposed for traditional passive detection problems. This paper examines the applicability of this idea in the presence of a strong reference channel with particular attention to ascertaining relationships between network topology and detection performance.