Nicholas K. Spencer

A lower bound for the maximum likelihood ratio: Sparse antenna array applications

The authors recently introduced a novel approach for detection-estimation of more uncorrelated Gaussian sources than sensors in sparse linear antenna arrays that is critically dependent on accurate likelihood-ratio (LR) maximisation over the set of candidate model covariance matrices. Here we introduce a non-asymptotic lower bound for the maximised LR. Any solution could now be tested against this bound with a high probability of correctly identifying a non-optimal solution. We demonstrate that existing techniques, based not on the exact LR criteria, but on related criteria, fail to give solutions that approach this lower bound for typical sparse array applications. A novel LR optimisation technique is introduced; for such sensitive sparse array problems, this technique is shown to generate a class of solutions that statistically exceed the lower bound. Some properties of solutions belonging to this class are discussed.

Convergence analysis of stochastically-constrained sample matrix inversion algorithms

It has been recently demonstrated by both computer simulations and real data processing that multi-interference signal environments with different types of interference stationarity can be adequately treated by the newly proposed stochastically-constrained adaptive algorithm. This signal processing approach is evidently the prototype of a new class of adaptive algorithms, whose convergence properties are analytically and numerically examined in this paper. Interference scenario which reflect the main features of typical HF radar applications are presented; these demonstrate both the high efficiency of the approach described and the accuracy of the derived analysis.

Design of Nonuniform Linear Antenna Array Geometry and Signal Processing Algorithm for DOA Estimation of Gaussian Sources

Abramovich, Yuri I., and Spencer, Nicholas K., Design of Nonuniform Linear Antenna Array Geometry and Signal Processing Algorithm for DOA Estimation of Gaussian Sources, Digital Signal Processing10 (2000), 340?354.This paper discusses the problem of direction-of-arrival (DOA) estimation for Gaussian sources that have arbitrary correlation: from independent to fully correlated. For independent sources, the antenna array design is governed by two competing considerations: maximum aperture, which inclines toward increasing sparsity for a given number of array sensors, and identifiability, which tends to exclude extreme sparsity. For fully correlated sources, these two competing criteria are augmented by a third which allows for the initialization of DOA estimation by the generalized spatial smoothing (GSS) technique. The maximum number of fully correlated sources is in turn an important factor in the GSS algorithm and subsequent array geometry design. We present a geometry optimization technique that permits accurate DOA estimation of arbitrarily correlated sources.