Brian Thelen

Distributed RADAR waveform design based on compressive sensing considerations

In this paper we describe a joint waveform design methodology for distributed imaging RADARs using the concepts of compressive sensing. Compressive sensing is an active area of research that offers the promise of good object reconstruction with a sparse measurement set. The measurement set of the scene is based on a set of dasiaprobes,psila the radar waveforms. The set of measurements must satisfy the restricted isometry property and the scene being interrogated must be dasiacompressiblepsila meaning that it can be sparsely represented in some basis. We examine waveform and position considerations for a distributed radar system to satisfy these constraints and show their impact on waveform design and image reconstruction.

Parametric reconstruction of internal building structures via canonical scattering mechanisms

In this paper, we describe a model-based, non-linear reconstruction method for mapping internal building structures using through-wall radar data. We based the model on canonical geometry constructs that are commonly used in construction practices. These constructs are then formulated as sets of simple scattering mechanisms, which can be estimated from the data. Our non-linear approach employs an iterative, conditional estimation method as a function of the intervening structures between the sensor and the object under consideration. Specific associations of scattering mechanisms are then used to re-create various building structures such as walls, doors, stairs, etc. We discuss some examples of estimating specific scattering mechanisms and a model-based reasoning approach for assembling them to reconstruct the interior structure of a building.

GPR Anomaly Detection with Robust Principal Component Analysis

This paper investigates the application of Robust Principal Component Analysis (RPCA) to ground penetrating radar as a means to improve GPR anomaly detection. The method consists of a preprocessing routine to smoothly align the ground and remove the ground response (haircut), followed by mapping to the frequency domain, applying RPCA, and then mapping the sparse component of the RPCA decomposition back to the time domain. A prescreener is then applied to the time-domain sparse component to perform anomaly detection. The emphasis of the RPCA algorithm on sparsity has the effect of significantly increasing the apparent signal-to-clutter ratio (SCR) as compared to the original data, thereby enabling improved anomaly detection. This method is compared to detrending (spatial-mean removal) and classical principal component analysis (PCA), and the RPCA-based processing is seen to provide substantial improvements in the apparent SCR over both of these alternative processing schemes. In particular, the algorithm has been applied to both field collected impulse GPR data and has shown significant improvement in terms of the ROC curve relative to detrending and PCA.

Optimization and waveforms for compressive sensing applications in the presence of interference

Compressive sensing concepts have potential applications to multiple RADAR problems, which include Moving Target Indication, and RADAR imaging in two and three spatial dimensions. Currently known sufficient conditions for reliable sparse signal reconstruction do not seem to be directly applicable or practical for some traditional RADAR problems. But experiments and mathematical invariance properties of some reconstruction methods indicate that useful products can often be obtained using these methods for circumstances outside the usual conditions.

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

Enhanced buried UXO detection via GPR/EMI data fusion

This paper investigates the enhancements to detection of buried unexploded ordinances achieved by combining ground penetrating radar (GPR) data with electromagnetic induction (EMI) data. Novel features from both the GPR and the EMI sensors are concatenated as a long feature vector, on which a non-parametric classifier is then trained. The classifier is a boosting classifier based on tree classifiers, which allows for disparate feature values. The fusion algorithm was applied to a government-provided dataset from an outdoor testing site, and significant performance enhancements were obtained relative to classifiers trained solely on the GPR or EMI data. It is shown that the performance enhancements come from a combination of improvements in detection and in clutter rejection.

A comparison of robust principal component analysis techniques for buried object detection in downward looking GPR sensor data

Explosive hazards are a deadly threat in modern conflicts; hence, detecting them before they cause injury or death is of paramount importance. One method of buried explosive hazard discovery relies on data collected from ground penetrating radar (GPR) sensors. Threat detection with downward looking GPR is challenging due to large returns from non-target objects and clutter. This leads to a large number of false alarms (FAs), and since the responses of clutter and targets can form very similar signatures, classifier design is not trivial. One approach to combat these issues uses robust principal component analysis (RPCA) to enhance target signatures while suppressing clutter and background responses, though there are many versions of RPCA. This work applies some of these RPCA techniques to GPR sensor data and evaluates their merit using the peak signal-to-clutter ratio (SCR) of the RPCA-processed B-scans. Experimental results on government furnished data show that while some of the RPCA methods yield similar results, there are indeed some methods that outperform others. Furthermore, we show that the computation time required by the different RPCA methods varies widely, and the selection of tuning parameters in the RPCA algorithms has a major effect on the peak SCR.

Novel application of windowed beamforming function imaging for FLGPR

Backprojection of cross-correlated array data, using algorithms such as coherent interferometric imaging (Borcea, et al., 2006), has been advanced as a method to improve the statistical stability of images of targets in an inhomogeneous medium. Recently, the Windowed Beamforming Energy (WBE) function algorithm has been introduced as a functionally equivalent approach, which is significantly less computationally burdensome (Borcea, et al., 2011). WBE produces similar results through the use of a quadratic function summing signals after beamforming in transmission and reception, and windowing in the time domain. We investigate the application of WBE to improve the detection of buried targets with forward looking ground penetrating MIMO radar (FLGPR) data. The formulation of WBE as well the software implementation of WBE for the FLGPR data collection will be discussed. WBE imaging results are compared to standard backprojection and Coherence Factor imaging. Additionally, the effectiveness of WBE on field-collected data is demonstrated qualitatively through images and quantitatively through the use of a CFAR statistic on buried targets of a variety of contrast levels.

Comparative analysis of image formation techniques for FLGPR

This paper discusses the application of several image formation techniques to forward looking ground penetrating radar (FLGPR) data to observe if they improve target-to-clutter ratio. Specifically, regularized imaging with 𝐿1 and total variation constraints and coherence-factor filtered images are considered. The technical framework and software implementation of each of these image formation techniques are discussed, and results of applying the techniques to field collected data are presented. The results from the different techniques are compared to standard backprojection and compared to each other in terms of image quality and target-to-clutter ratio.

A novel latent gaussian copula framework for modeling spatial correlation in quantized SAR imagery with applications to ATR

With all of the new remote sensing modalities available, and with ever increasing capabilities and frequency of collection, there is a desire to fundamentally understand/quantify the information content in the collected image data relative to various exploitation goals, such as detection/classification. A fundamental approach for this is the framework of Bayesian decision theory, but a daunting challenge is to have significantly flexible and accurate multivariate models for the features and/or pixels that capture a wide assortment of distributions and dependen- cies. In addition, data can come in the form of both continuous and discrete representations, where the latter is often generated based on considerations of robustness to imaging conditions and occlusions/degradations. In this paper we propose a novel suite of ”latent” models fundamentally based on multivariate Gaussian copula models that can be used for quantized data from SAR imagery. For this Latent Gaussian Copula (LGC) model, we derive an approximate, maximum-likelihood estimation algorithm and demonstrate very reasonable estimation performance even for the larger images with many pixels. However applying these LGC models to large dimen- sions/images within a Bayesian decision/classification theory is infeasible due to the computational/numerical issues in evaluating the true full likelihood, and we propose an alternative class of novel pseudo-likelihoood detection statistics that are computationally feasible. We show in a few simple examples that these statistics have the potential to provide very good and robust detection/classification performance. All of this framework is demonstrated on a simulated SLICY data set, and the results show the importance of modeling the dependencies, and of utilizing the pseudo-likelihood methods.