A Bayesian network (BN) is a directed acyclic graphical model that encodes probabilistic relationships among variables of interest. BNs not only provide a natural and compact way to represent the domain knowledge and encode joint probability distributions, but also provide a basis for efficient probabilistic inference. We apply BNs to wide area airborne minefield detection (WAAMD) due to their powerful representation ability of encoding the domain knowledge and their flexible structural extendibility for multi-look and multi-sensor data fusion. We first design BN models for both single-look detection and multi-look and multi-sensor data fusion and then refine them via learning from data using a structural expectation-maximization (SEM) algorithm. We evaluate the performance of our landmine detection scheme using data sets collected by three airborne ground penetrating synthetic aperture radars (GPSARs) (Lynx Ku-band, Mirage stepped-frequency (0.3 - 2.8 GHz), and Veridian X-band GPSARs) from various testing sites that have different terrain and vegetation conditions. Experimental results indicate that BNs can help improve the landmine detection performance significantly. The use of BNs for multi-look and multi-sensor data fusion is also shown to provide significant false alarm reductions.
The quadrupole resonance (QR) technology can be used as a confirming sensor for buried plastic landmine detection by detecting the explosives (e.g., TNT and RDX) within the mine. We focus herein on the detection of TNT via the QR sensor. Since the frequency of the QR signal is located within the AM radio frequency band, the QR signal can be corrupted by strong radio frequency interferences (RFIs). Hence to detect the very weak QR signal, RFI mitigation is essential. Reference antennas, which receive RFIs only, can be used together with the main antenna, which receives both the QR signal and the RFIs, for RFI mitigation. By taking advantage of the spatial correlation of the RFIs received by the antenna array, the RFIs can be reduced significantly. However, the RFIs are usually colored both spatially and temporally and hence exploiting only the spatial diversity of the antenna array may not give the best performance. We exploit herein both the spatial and temporal correlation of the RFIs to improve the TNT detection performance. First, we consider exploiting the spatial correlation of the RFIs only and propose a maximum likelihood (ML) estimator for parameter estimation and a constant false alarm rate (CFAR) detector for TNT detection. Second, we adopt a multichannel autoregressive model to take into account the temporal correlation of the RFIs and devise a detector based on the model. Third, we take advantage of the temporal correlation by using a two-dimensional robust Capon beamformer (RCB) with the ML estimator for improved RFI mitigation. Finally, we combine the merits of all of the three aforementioned approaches for TNT detection. The effectiveness of the combined method is demonstrated using the experimental data collected by Quantum Magnetics, Inc.
We investigate both two-dimensional (2-D) and three-dimensional (3-D) synthetic aperture radar (SAR) imaging techniques for a forward-looking ground penetrating radar (FLGPR) system. In particular, we consider SAR imaging using the delay-and-sum (DAS), phase-shift migration, and spectral estimation (joint APES (Amplitude and Phase EStimation) and RCB (Robust Capon Beamforming)) approaches with the PSI (Planning Systems Inc.) FLGPR Phase II system. For the DAS and phase-shift migration approaches, we use shading in both frequency and cross-track aperture dimensions to reduce sidelobe leakages and clutter. We perform both coherent and non-coherent multi-look processing as well as smoothing to improve the SAR imaging quality and landmine detection capability of the system. The effectiveness of the approaches are demonstrated with an experimental data set collected by the PSI FLGPR Phase II system.
The Amplitude and Phase EStimation (APES) approach to amplitude spectrum estimation has been receiving considerably attention recently. We develop an extension of APES for the spectral estimation of gapped (incomplete) data and apply it to synthetic aperture radar (SAR) imaging with angular diversity. It has recently been shown that APES minimizes a certain least-squares criterion with respect to the estimate of the spectrum. Our new algorithm is called gapped-data APES and is based on minimizing this criterion with respect to the missing data as well. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm and its applicability to SAR imaging with angular diversity.
We study the moving target detection and feature extraction in the presence of ground clutter for airborne High Range Resolution (HRR) phased array radar. To avoid the range migration problems that occur in HRR radar data, we first divide the HRR range profiles into Low Range Resolution (LRR) segments. Since each LRR segment contains a sequence of HRR range bins, no information is lost due to the division and hence no loss of resolution occurs. We show how to use a Vector Auto-Regressive (VAR) filtering technique to suppress the ground clutter. Then we show how to estimate the target parameters of interest, including the target Doppler frequency, Direction-of-arrival (DOA), and complex amplitude and range frequency of each target scatterer. A moving target detector based on a Generalized Likelihood Ratio Test (GLRT) detection strategy is also derived. Numerical results are provided to demonstrate the performance of the proposed algorithms.
We present a semi-parametric spectral estimation algorithm for fully polarimetric synthetic aperture radar (SAR) target feature extraction and image formation. The algorithm is based on a flexible data model that models each target scatterer as a two-dimensional complex sinusoid with arbitrary amplitude and constant phase in cross-range and with constant amplitude and phase in range. The algorithm is a relaxation-based optimization approach that minimizes a nonlinear least squares (NLS) cost function. Due to using the fully polarimetric radar measurements (HH, HV, and VV) simultaneously, the algorithm provides not only more accurate target features, but also more useful information about the target of interest than the single polarization based algorithm. The algorithm has the ability to discriminate corner reflector types by also exploiting the differences in the polarimetric scattering properties of the scatterers of the target of interest. Numerical examples are presented to demonstrate the performance of the proposed algorithm.