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Ivars P. Kirsteins, NUWCDIVNPT (U.S.A.)
Sanjay K. Mehta, NUWCDIVNPT (U.S.A.)
John Fay, NUWCDIVNPT (U.S.A.)
We propose a new non-parametric adaptive detector for detecting an unknown broadband signal in interference consisting of non-stationary narrowband components and a locally stationary broadband component. An important feature of this detector is that it needs no prior information about the signal or interference. The proposed detector is based on the integration of the non-parametric power law detector of Nuttall with robust narrowband interference removal and whitening using a multiple taper spectral estimation-based technique. Experimental results indicate that the proposed detector outperforms conventional detectors.
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Vittorio Rampa, C.S.T.S. - C.N.R. (Italy)
Umberto Spagnolini, Politecnico di Milano (Italy)
The Time of Delay (TOD) estimation of multiple echoes is here solved with an iterative multitarget detection/tracking algorithm. The evaluation of the TODs is based on their a-posteriori probability, while a first-order Markov model is used for a-priori probability estimation. The effectiveness of the algorithm (low false-alarm rate and robustness) is also experimentally proven. Moreover the algorithm exhibits a better noise rejection and an improved target resolution with respect to algorithms that perform separate detection and tracking.
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Francisco M. Garcia, ISR - IST (Portugal)
Isabel M.G. Lourtie, ISR - IST (Portugal)
In underwater acoustics, the modeling of impulsive noise ambients by symmetric-(alpha)-stable laws is motivated by the generalized central limit theorem. However, detection of stochastic signals under such additive noise is a difficult task to implement, due to the lack of a closed-form expression of the a-posteriori probability density function. In this paper, we present a suboptimal detector for Gaussian bandpass transients in impulsive noise that uses a nonlinear, memoryless prefilter followed by a discrete wavelet transform. The resulting signals present a Gaussian-like behavior and the decision is achieved by the comparison of a quadratic likelihood ratio with a threshold. The tuning of the nonlinearity parameter is performed either by looking at the receiver operating characteristic or using the Chernoff distance, that, although resulting in an approximate solution, is easier to compute. Simulation results are presented by Monte-Carlo simulation.
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Gilles Dassot, LETI CEA/Grenoble (France)
Roland Blanpain, LETI CEA/Grenoble (France)
Claude Jauffret, GESSY, University Toulon et Var (France)
This paper is devoted to the localization of magneto- acoustic sources moving in a straight line at a constant speed. Our technique is based on the association of narrow band acoustic signals and magnetostatic measurements. First of all, we describe features that make possible the association of magnetic and acoustic data, secondly, we show that positioning accuracy is much improved by this association. In this paper we focus on solving the problem with as few sensors as possible. A geometric discussion of identifiability is proposed, as well as a Batch Maximum Likelihood estimator whose covariance matrix asymptotically achieves Cramèr Rao Lower Bounds (CRLB).
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Joseph Tabrikian, Duke University (U.S.A.)
Jeffrey L. Krolik, Duke University (U.S.A.)
Matched-field methods are known to have a severe ambiguity problem. In low signal-to-noise-ratios (SNR's), where the estimator cannot distinguish between the ambiguity function peak near the true source location and ambiguous ones, its mean square error deviates radically from the Cramer-Rao lower bound (CRLB). In this paper, the Barankin bound for the source localization problem in an uncertain shallow water environment is derived. In particular, a method of selection of the test-points for evaluation of the bound is presented. The bound is evaluated using a ``general mismatch'' benchmark scenario. The results presented here predict the threshold SNR below which the performance degrades dramatically. Channel uncertainties in the benchmark scerario are shown to increase this threshold SNR by as much as 3dB.
ic970499.pdf ic970499.pdf TOPZoi-Heleni Michalopoulou, CAMS, NJIT (U.S.A.)
Processing marine-mammal signals for species classification and monitoring of endangered marine mammals are problems that have recently attracted attention in the scientific literature. For classification it has been proposed to use methods appropriate for non-stationary signals, such as time-frequency and time-scale analysis. This paper shows that a factor that can significantly affect results from marine-mammal signal processing is the impulse response of the ocean in which the signals propagate. The ocean is a dispersive propagation medium and, therefore, affects the time-frequency characteristics of a propagating acoustic signal. Because of this distortion, feature selection should be performed after the oceanic impulse response has been deconvolved from the recorded signals. The paper also discusses localization of vocalizing marine mammals using matched-field processing and shows how this becomes a part of the deconvolution process.
ic970503.pdf ic970503.pdf TOPAlexander Goldin, IBM Israel S&T (Israel)
The paper describes an application of the numerical optimization methods for the design of non-adaptive multi-sensor arrays. The parameters and the geometry of such arrays do not change with changes in the input signals, and must be chosen in advance. Generally, the goal of a non-adaptive multi-sensor array may be numerically expressed through its pattern function which shows the gain for a signal coming from a particular direction in space. The real pattern function depends on the geometry of the array and on the processing which signals from every sensor undergo. The array pattern function is non-linear and it is frequency dependent. The geometry and the processing parameters of the multi-sensor array are optimized to provide the minimum difference between the goal and the real functions over a specified frequency range. Optimization results for several goal functions for multi-microphone arrays are provided and discussed.
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Jason Goldberg, Tel Aviv University (Israel)
Ana Perez-Neira, UPC (Spain)
Miguel Lagunas, UPC (Spain)
An algorithm for the joint tracking of source DOA's and sensor positions is presented to address the problem of DOA tracking in the presence sensor motion. Initial maximum likelihood estimates of source DOA's and sensor positions are refined by Kalman filtering. Spatio-temporally correlated array movement is considered. Source angle dynamics are used to achieve correct data association. The new technique is capable of performing well for the difficult cases of sources that cross in angle, fully coherent sources, as well as sources of identical or vastly different (possibly time-varying) power. Computer simulations show that the approach is robust in the presence of array motion modeling uncertainty and effectively reduces dependence on expensive and possibly unreliable hardware.
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Mark L. Krieg, DSTO, CSSIP, University of Adelaide (Australia)
Douglas A. Gray, University of Adelaide, CSSIP (Australia)
A key element for successful tracking is knowing from which target each measurement originates. These measurement-to-target associations are generally unavailable, and the tracking problem becomes one of estimating both the assignments and the target states. We present the Probabilistic Least Squares Tracking (msPLST) algorithm for estimating the measurement-to-target assignments and the track trajectories of multiple targets, using measurements from multiple sensors. This is a different approach to that used in Probabilistic Multi-Hypothesis Tracking (PMHT), although both algorithms employ the concept of an extended observer containing both the target states and the measurement-to-target assignments. A comparison of both algorithms is made, and their performance is evaluated using simulated data.
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Alex B. Gershman, Ruhr University Bochum (Germany)
Christoph F. Mecklenbräuker, Ruhr University Bochum (Germany)
Johann F. Böhme, Ruhr University Bochum (Germany)
The performance of high-resolution direction finding methods degrades in several practical situations where the wavefronts have imperfect spatial coherence. The original solution to this problem was proposed by Paulraj and Kailath, but their technique requires a priori knowledge of the matrix characterizing the loss of wavefront coherence along the array aperture. Below, a novel solution to this problem is proposed, which does not require a priori knowledge of the spatial coherence matrix. Our technique is based on the multidimensional minimization of appropriate concentrated cost function using Genetic Algorithm (GA).
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Saman S. Abeysekera, Curtin University of Technology (Australia)
Y.H. Leung, Curtin University of Technology (Australia)
The use of wideband active sonar array processing to estimate the range, velocity and bearing of a target has received much interest in the literature recently. Although increased attention has been focused on wideband correlation processing for estimating range and velocity, array directivity patterns are almost always computed and interpreted under the narrowband signal assumption. This paper considers the target bearing estimation problem using the wideband correlation approach. Via this approach, it will be shown how an optimum set of array weights can be selected for a known transmitted signal. The optimization procedure also provides robustness against errors in the array structure.
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