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.
KEYWORDS: Principal component analysis, Sensors, Explosives, Ground penetrating radar, Detection and tracking algorithms, General packet radio service, Radar, Land mines, Target detection, Signal to noise ratio
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.
This paper investigates the use of the apex-shifted hyperbolic Radon transform to improve detection of buried
unexploded ordinances with ground penetrating radar (GPR). The forward transform, motivated by physical signatures
generated by targets, is defined and implemented. The adjoint of the transform is derived and implemented as well. The
transform and its adjoint are used to filter out responses that do not exhibit the hyperbolic structure characteristic of GPR
target responses. The effectiveness of filtering off clutter via this hyperbolic Radon transform filtering procedure is
demonstrated qualitatively on several examples of GPR B-scan imagery from a government-provided dataset collected at
an outdoor testing site. Furthermore, a quantitative assessment of the utility within a detection algorithm is given in
terms of improved ROC curve performance on the same dataset.
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.
This paper investigates an algorithm for forming 3D images of the subsurface using stepped-frequency GPR data. The algorithm is specifically designed for a handheld GPR and therefore accounts for the irregular sampling pattern in the data and the spatially-variant air-ground interface by estimating an effective “ground-plane” and then registering the data to the plane. The algorithm efficiently solves the 4th-order polynomial for the Snell reflection points using a fully vectorized iterative scheme. The forward operator is implemented efficiently using an accelerated nonuniform FFT (Greengard and Lee, 2004); the adjoint operator is implemented efficiently using an interpolation step coupled with an upsampled FFT. The imaging is done as a linearized version of the full inverse problem, which is regularized using a sparsity constraint to reduce sidelobes and therefore improve image localization. Applying an appropriate sparsity constraint, the algorithm is able to eliminate most the surrounding clutter and sidelobes, while still rendering valuable image properties such as shape and size. The algorithm is applied to simulated data, controlled experimental data (made available by Dr. Waymond Scott, Georgia Institute of Technology), and government-provided data with irregular sampling and air-ground interface.
KEYWORDS: Principal component analysis, General packet radio service, Detection and tracking algorithms, Target detection, Radon, Ground penetrating radar, Signal detection, Sensors, Independent component analysis, Signal processing
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.
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.
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