SAM-7.1

Modeling and Estimation of Mutual Coupling in a Uniform Linear Array of Dipoles
Thomas Svantesson (Chalmers University of Technology)

The mutual coupling in a uniform linear array (ULA) of dipoles is calculated using basic electromagnetic concepts. Since the coupling often is unknown and needs to be estimated, a simpler model is proposed based on the electromagnetic analysis. The parametrization of this model is shown to be locally unambiguous. A necessary condition for the joint solution of directions and coupling parameters to be unique is also derived. Finally, the directions and coupling parameters are estimated using a maximum likelihood method. It is found that the simpler coupling model with just a few parameters well describes the full electromagnetic model.

SAM-7.2

Theoretical Noise Reduction Limits of the Generalized Sidelobe Canceller (GSC) for Speech Enhancement
Joerg Bitzer (University of Bremen , FB-1, Dept. of Telecommunications), Uwe Simmer (Aureca GmbH), Karl-Dirk Kammeyer (University of Bremen , FB-1, Dept. of Telecommunications)

In this paper we present an analysis of the generalized sidelobe canceller (GSC). It can be shown that the theoretical limits of the noise reduction performance depend only on the auto- and cross-spectral densities of the input signals. Furthermore, we compute the limits of the noise reduction performance for the theoretically determined diffuse noise field, which is an approximation for reverberant rooms. Our results will show that the GSC cannot reduce noise further than 1dB. These results were verified by simulation of reverberant environments. Only in sound-proofed rooms with a reverberation time less than 100ms the GSC performs well.

SAM-7.3

Adaptive Subarray Design for Interference Cancellation
Ho Yang, Mary Ann Ingram (Georgia Institute of Technology)

A method for designing near-optimal, tapered subarrays for adaptive interference cancellation is proposed. The limited aperture or limited element feature of these subarrays enables a low-complexity hardware implementation of a partially adaptive array. This approach optimizes canceller performance for a given number of beams and a given number of elements per beams.

SAM-7.4

Classification Using Dirichlet Priors When the Training Data are Mislabeled
Robert S Lynch, Jr. (Naval Undersea Warfare Center, Newport, RI 02841), Peter K Willett (University of Connecticut, Storrs, CT 06269)

The average probability of error is used to demonstrate performance of a Bayesian classification test (referred to as the Combined Bayes Test (CBT)) given the training data of each class are mislabeled. The CBT combines the information in discrete training and test data to infer symbol probabilities, where a uniform Dirichlet prior (i.e., a noninformative prior of complete ignorance) is assumed for all classes. Using this prior it is shown how classification performance degrades when mislabeling exists in the training data, and this occurs with a severity that depends on the value of the mislabeling probabilities. However, an increase in the mislabeling probabilities are also shown to cause an increase in M* (i.e., the best quantization fineness). Further, even when the actual mislabeling probabilities are known by the CBT, it is not possible to achieve the classification performance obtainable without mislabeling.

SAM-7.5

A Constrained Optimal Data Association for Multiple Target Tracking
Hong Jeong (POSTECH, Republic of Korea), Jeong-Ho Park (POSTECH)

One of the major problems in multiple target tracking is to obtain an accurate association between targets and noisy measurements. We introduce a new scheme, called Constrained Optimal Data Association (CODA), that finds the optimal data association by a MAP estimation method and uses a new energy function. In this scheme, the natural constraints between targets and measurements are defined so that they may contain missed detection and false alarm errors. Most current algorithms involve many heuristic adjustments of the parameters. Instead, this paper suggests an adaptive mechanism for such parameter updation. In this manner, the system automatically adapts to the clutter environment as it continuously changes in time and space, resulting in better association. Experimental results, using PDA, NNF, and CODA, show that the new approach reduces position errors in crossing trajectories by 13.9% on average compared to NNF.

SAM-7.6

Analysis of the Adaptive Matched Filter Algorithm for Cases with Mismatched Clutter Statistics
Yumin Zhang (EECS Department, Lehigh University), Rick S Blum (EECS Department, Lehigh Univeristy)

In practical radar applications of the adaptive matched filter algorithm, the covariance matrix for the clutter-plus-noise is typically estimated using data taken from range cells surrounding the cell under test. In a nonhomogeneous environment, this can lead to a mismatch between the mean of the estimated covariance matrix and the true covariance matrix for the range cell under test. Closed form expressions are provided, which give the performance for such cases. These equations are exact in some cases and provide useful approximate results in others. Performance depends on a small number of important parameters. These parameters describe which types of mismatches are important and which are not. Numerical examples illustrate how performance varies with each of the important parameters. Monte Carlo simulations are included which closely match the predictions of our equations.

SAM-7.7

Approximate Minimum-norm Subspace Projection of Least-squares Weights Without the SVD
Mark J Smith (Defence Evaluation and Research Agency, UK), Ian K Proudler (Defence Evaluation and Research Agency,UK)

A QR based technique is presented for estimating the approximate numerical rank and corresponding signal subspace of a matrix together with the subspace projection of the least squares weights. Theoretical difficulties associated with conventional QR factorisation are overcome by applying the technique of Row-Zeroing QR to the covariance matrix. Thresholding is simplified compared with the use of the data matrix as the diagonal value spectrum is sharpened and the subspace estimate is improved. An approximation to the minimum norm solution for the projection of the least squares weight onto the signal subspace of the data is obtained simply, without performing an SVD.

SAM-7.8

A broadband application of memoryless narrowband GSC/NLMS adaptive beamformers
Douglas Peters (Nortel Technology)

In this article, the adaptive performance of the normalized least mean-squares algorithm in the context of the generalized sidelobe canceller beamformer is considered. The implications of both the convergence behaviour and the misadjustment on various beamforming applications are discussed. In particular, an important case is identified for which there is near-instantaneous convergence. A misadjustment limit for which coherent post-processing is viable is also derived. Finally, a novel approach to coherent broadband beamforming is introduced and then tested via simulation.

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Last Update: February 4, 1999 Ingo Höntsch