KEYWORDS: Detection and tracking algorithms, Acoustics, Roads, Sensors, Signal detection, Analytical research, Signal to noise ratio, Scene classification, Target detection, Algorithm development
In this paper we discuss an algorithm for classification and identification of multiple targets using acoustic signatures. We use a Multi-Variate Gaussian (MVG) classifier for classifying individual targets based on the relative amplitudes of the extracted harmonic set of frequencies. The classifier is trained on high signal-to-noise ratio data for individual targets. In order to classify and further identify each target in a multi-target environment (e.g., a convoy), we first perform bearing tracking and data association. Once the bearings of the targets present are established, we next beamform in the direction of each individual target to spatially isolate it from the other targets (or interferers). Then, we further process and extract a harmonic feature set from each beamformed output. Finally, we apply the MVG classifier on each harmonic feature set for vehicle classification and identification. We present classification/identification results for convoys of three to five ground vehicles.
Detecting minefield point patterns is an important problem for the Navy, Marines and Army. Because of the difficulty and uncertainty associated with accurately modeling enemy mine laying procedures, robust and flexible family of statistics are needed to detect minefields as deviations from complete spatial randomness. In this paper, a large family of minefield detection statistics are presented and compared using their asymptotic relative efficiency for testing multinomial and minefield mixture alternatives. A slightly modified version of the widely-used power-divergence statistics are introduced that are appropriate under sparseness assumptions. This family includes the empty boxes test which has been advocated previously as a simple and effective approach. Another family, called VC statistics, is presented that provides a low- complexity statistic with optimal performance. The efficiency of these methods are compared analytically and with a minefield benchmark used in previous work.
Previously, methods to estimate the number of jumps in a piecewise constant signal were presented in the framework of projection libraries. In this paper, these concepts are extended to general piecewise projection libraries appropriate for modeling, for example, piecewise polynomial and piecewise stationary signals. A general piecewise best basis algorithm is also presented that offers an efficient alternative to standard methods. Particularly, an algorithm for best piecewise wavelet basis is shown to reduce the entropy over wavelet packets. While a dynamic programming algorithm can still be employed to efficiently calculate optical estimates for these new piecewise projection libraries, additional modifications are often needed to reduce the computational requirements for practical implementation. An alternative approach, termed subspace pursuit, is presented that is applicable to all projection libraries and is especially suited for signal dimension estimation. The method is an order-recursive least square implementation of matched pursuit that requires roughly twice the computation but has the advantage that at each iteration the coefficients are optimal, that is, are obtained by a projection onto the subspace spanned by signals in the dictionary. Additionally, for the signal dimension estimation problem, an interesting paradox is presented where estimates are shown to be worse with increased signal-to-noise ratio (SNR) past a certain threshold and to converge to a level less than this optimum performance for infinite SNR.
KEYWORDS: Hough transforms, Sensors, Land mines, Image processing, Detection and tracking algorithms, Data modeling, Statistical analysis, Algorithms, Visualization, Signal to noise ratio
Minefields have point patterns that tend to exhibit regularity such as equal-spacing and collinearity that provide potentially valuable discriminants against natural occurring clutter. Previously, several simple procedures based on the empty boxes test and its extensions have ben shown to be effective detectors of generic regularity in minefields without explicitly taking advantage of collinearity and equal-spacing. Recently, modifications of the Euclidean algorithm have been applied to the problem of determining the period from a sparse set of detection times which arises in pulse repetition interval analysis. Besides the intriguing properties of this approach because of its number theoretic roots, the resulting algorithms are both computationally efficient and robust to errors introduced by missed detections, noise in time location, and false alarms. In this paper, a two-step procedure for detecting minefields is proposed whereby collinear points are first detected using a standard approach, the Hough transform, and the period estimated using the modified Euclidean algorithm. One advantage of this approach is that prior information on minefield spacing can be utilized to exclude collinear points that do not exhibit any periodicity or are spaced too close together or too far apart. The preliminary detection performance of some of these minefield detection methods are quantified for using a point pattern extracted from real sensor data.
Many results in approximation theory, nonparametric regression, and adaptive signal representation assume that the signal is a smooth function or nonstationary in some smooth sense. These assumptions are not always applicable and perhaps even more importantly the times where the signal is not smooth are themselves important features to preserve in the signal representation. Previously, computationally efficient methods were developed to implement these approaches for the relatively simple problem of estimating multiple change points from a piecewise constant signal. In this paper, this approach is presented in the modern framework of waveform dictionaries, bases libraries, and atomic decomposition.
Detecting minefields in the presence of clutter is an important challenge for the Navy. Minefields have point patterns that tend to exhibit regularity such as equal-spacing and collinearity that provide potentially valuable discriminants against natural occuring clutter. These tendencies arise because of a variety of compelling factors including strategic doctrine, safety, tactical and economic efficiency, and perhaps most intriguing, the human element. In this paper, we introduce several simple procedures to detect regularity in point proceses including the empty boxes test (EBT) and its extensions, the skeptical likelihood test (SLT), and a Fourier-based method. Several possible methods to specifically detect collinearity are also discussed. The preliminary detection performance of a variety of these minefield detection methods are investigated using simulated data and a point pattern extracted from real sensor data.
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