The quadrupole resonance (QR) technology can be used as a confirming sensor for buried plastic landmine detection by detecting the explosives within the mine. We focus herein on the detection of TNT mines 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. 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 correlations of the RFIs to improve the TNT detection performance.

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.

A high-speed video camera and voltage-sensitive dyes were used to acquire high resolution (80times80 pixels) and high-speed (500mus/frame) optical signals of ventricular fibrillation in a Langendorff-perfused porcine heart. The resulting spatiotemporal dynamics were recorded before and after the application of a defibrillation shock in order to study the mechanism of defibrillation failure. We used recurrence plots as a tool to qualify the evolution of ordered behavior on the heart surface before fibrillation was reestablished in defibrillation failure. Such ordered periods may point to robust periods in which the defibrillation attempt has had the most effect and may provide a window in which a smaller, corrective shock may be applied to achieve defibrillation

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.

A high-speed video camera and voltage-sensitive dyes were used to acquire high resolution (80 /spl times/ 80 pixels) and high-speed (1 ms/frame) optical signals of ventricular fibrillation in a Langendorff-perfused porcine heart. The resulting spatiotemporal dynamics were recorded before and after the application of a defibrillation shock in order to study the mechanism of defibrillation failure. We created time space cubes and calculated nonlinear synchronization index measures to qualify the evolution of different types of activity on the heart surface (focal, reentry). We observed changes with time in all types of analysis, thus establishing these techniques as viable methods for detecting defibrillation failure before reentry reestablishes itself.

In this paper, we develop a novel approach to nonlinearly retrieving wind field from radial wind data, which are obtained by Single Doppler radar. In general, it is impossible to uniquely recover the 2-D (or 3D) wind field from single Doppler radar, since the problem is under-determined. In order to overcome the difficulty, according to the characteristic of the wind field distribution, we add the piecewise stationary assumption of the wind field function for the wind retrieval. Furthermore, when the wavelets are applied to approximate the wind field, we found the additional assumption yields the sparse coefficients. This connects the introduction of the entropy function, because their minimization results sparse solution. The corresponding nonlinear optimization can be solved by a modified affine scaling methodology, which is at a lower computational cost due to the behavior of the affine scaling technique. The real experiments demonstrate that the algorithms performs well.

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 considerable 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 (GAPES) 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.

The orthogonal frequency-division multiplexing access technique has been attracting considerable interest especially for wireless local area networks (WLANs). We consider the joint estimation of the symbol timing, the channel length and the channel-impulse response. A novel estimation algorithm based on maximum-likelihood principles and the generalized Akaike information criterion are proposed. We provide simulation results to illustrate the performance of our proposed algorithm.

We present an algorithm for nonparametric complex spectral analysis of gapped data via an adaptive finite impulse response (FIR) filtering approach, referred to as the gapped-data amplitude and phase estimation (GAPES) algorithm. The incomplete data sequence may contain gaps of various sizes. The GAPES algorithm iterates the following two steps: (1) estimating the adaptive FIR filter and the corresponding complex spectrum via amplitude and phase estimation (APES), a nonparametric adaptive FIR filtering approach, and (2) filling in the gaps via a least-squares APES fitting criterion. The initial condition for the iteration is obtained from the available data segments via APES. Numerical results are presented to demonstrate the effectiveness of the proposed GAPES algorithm.

We consider the problem of joint synchronization and channel estimation for orthogonal frequency division multiplexing (OFDM) systems. A new algorithm is proposed that estimates the channel and symbol timing simultaneously by using a technique based on maximum-likelihood (ML) theory and the generalized Akaike information criterion (GAIC). Finally, we demonstrate the performance of our algorithm by simulation results.

We present an efficient carrier frequency offset (CFO) estimation algorithm for the orthogonal frequency-division multiplexing (OFDM)-based wireless local area networks (WLANs). The packet preamble information we use is based on the high rate WLAN standards adopted by the IEEE 802.11 standardization group. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

We study the feature extraction of moving targets in the presence of temporally and spatially correlated 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, a parameter estimation algorithm is proposed for target feature extraction. From the VAR-filtered data, the target Doppler frequency and the spatial signature vectors are first estimated by using a maximum likelihood (ML) method. The target phase history and direction-of-arrival (DOA) (or the array steering vector for an unknown array manifold) are then estimated from the spatial signature vectors by minimizing a weighted least squares (WLS) cost function. The target radar cross section (RCS)-related complex amplitude and range-related frequency of each target scatterer are then extracted from the estimated target phase history by using RELAX, which is a relaxation-based high-resolution feature extraction algorithm. Numerical results are provided to demonstrate the performance of the proposed algorithm.

We propose to use the APES (amplitude and phase estimation) approach for the spectral estimation of gapped data and synthetic aperture radar (SAR) imaging with angular diversity. A relaxation-based algorithm, referred to as GAPES (Gapped-data APES), is proposed, which includes estimating the spectrum via APES and filling in the gaps via a least squares (LS) fitting. For SAR imaging with angular diversity data fusion, we perform one-dimensional (1-D) windowed fast Fourier transforms (FFTs) in range, use the GAPES algorithm to interpolate the gaps in the aperture for each range, apply 1-D inverse FFTs (IFFTs) and dewindow in range, and finally apply the two-dimensional (2-D) APES algorithm to the interpolated matrix to obtain the 2-D SAR image. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

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 moving target detection in the presence of temporally and spatially correlated ground clutter for airborne high range resolution (HRR) phased array radar. We divide the HRR range profiles into large range segments to avoid the range migration problems that occur in the HRR radar data. Since each range 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 autoregressive (VAR) filtering technique to suppress the ground clutter. Then a moving target detector based on a generalized likelihood ratio test (GLRT) detection strategy is derived. The detection threshold is determined according to the desired false alarm rate, which is made possible via an asymptotic statistical analysis. After the target Doppler frequency and spatial signature vectors are estimated from the VAR-filtered data as if a target were present, a simple detection variable is computed and compared with the detection threshold to render a decision on the presence of a target. Numerical results are provided to demonstrate the performance of the proposed moving target detection algorithm.

It is demonstrated through simulations that a combination of the vector auto-regressive (VAR) filtering technique and a maximum likelihood (ML) parameter estimation method is an effective means for carrying out clutter suppression and parameter estimation, and being more robust against system mismatches than conventional displaced-phase-centre-antenna (DPCA) processing.

We present synthetic aperture radar (SAR) target feature extraction and imaging techniques with angle divesity. We first establish a flexible data model that describes each target scatterer as a two-dimensional (2D) complex sequence with arbitrary amplitude and constant phase in range and cross-range. A new algorithm, referred to as the QUasiparametric ALgorithm for target feature Extraction (QUALE), is then presented for SAR target feature extraction via data fusion through angle diversity based on the flexible data model. QUALE first estimates the model parameters, which include, for each scatterer, a 2D arbitrary real-valued amplitude sequence, a constant phase, and scatterer locations in range and cross-reange. QUALE then averages the estimated 2D real-valued amplitude sequence over range by making the assumption that the scatterer radar cross section is approximately consant. QUALE next models the so-obtained 1D sequence with a simple sinc function by assuming that the scatterer is approximately a dihedral (a trihedral is approximated as a very short dihedral) and estimates the relevant sinc function parameters by minimizing a nonlinear least-squares fitting function. Finally, the approximate 2D SAR image is reconstructed by using the estimated features. Numerical examples are given to demonstrate the perfomance of the proposed algorithm.

High range resolution (HRR) moving target indication (MTI) is increasingly important in many military and civilian applications such as the detection and classification of moving targets in strong clutter backgrounds. Meanwhile using polarisation diversity in radar systems has been shown to result in improved performance as compared with using only a single polarisation channel. The authors extract HRR moving target features with polarisation diversity in the presence of strong stationary clutter. The problem considered takes into account arbitrary range migration and phase errors, which may be induced by unknown target and platform motions as well as atmosphere turbulence and/or system instability. A relaxation-based algorithm is presented for the joint clutter suppression and super resolution target feature extraction and its performance is compared to the Cramer-Rao bound, the best performance bound an unbiased estimator can achieve. Numerical results are also provided to demonstrate the performance of the proposed algorithm.

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.