Alex B. Gershman

Matrix fitting approach to direction of arrival estimation with imperfect spatial coherence of wavefronts

The performance of high-resolution direction of arrival (DOA) estimation methods significantly degrades in several practical situations where the wavefronts have imperfect spatial coherence. The original solution to this problem was proposed by Paulraj and Kailath (1988), but their technique requires a priori knowledge of the matrix characterizing the loss of wavefront coherence along the array aperture. A novel solution to this problem is proposed, which does not require a priori knowledge of the spatial coherence matrix.

Adaptive beamforming algorithms with robustness against jammer motion

The performance of adaptive array algorithms is known to degrade in rapidly moving jammer environments. This degradation occurs due to the jammer motion that may bring the jammers out of the sharp notches of the adapted pattern. We develop the robust modifications of the sample matrix inversion (SMI) algorithm, loaded SMI (LSMI) algorithm, and eigenvector projection (EP) algorithm by means of artificial broadening of the null width in the jammer directions. For this purpose, data-dependent sidelobe derivative constraints that do not require any a priori information about the jammer directions are used.

Experimental results of localization of moving underwater signal by adaptive beamforming

The problem of weak moving signal localization and tracking in the presence of single motionless strong interference is investigated using real data of an underwater experiment in the Baltic sea (Sept. 1990) with a horizontal receiving array of 64 hydrophones and with two independent powerful narrowband sources imitating the signal and interference. Three simple adaptive beamforming methods were employed for the experimental data processing. The first one is based on the well-known projection approach to adaptive beamforming, the second method uses the adaptive canceler approach (also termed the dipole pattern method), and the third method combines these approaches. The signal-to-interference power ratio (SIR) threshold of the signal localization and tracking is evaluated by a special technique, which allows examination of the considered algorithms with change of the SIR in consecutive order. The results of the data processing show the high possibilities of signal localization in the presence of strong interference. The combined method performs better than the methods considered and enables localization of the signal source up to an SIR/spl sime/-25 dB. >

Constrained Hung-Turner adaptive beam-forming algorithm with additional robustness to wideband and moving jammers

We present a new modification of the Hung-Turner (HT) adaptive beam-forming algorithm, providing additional robustness of a narrowband adaptive array in wideband and moving-jammer scenarios. The robustness is achieved by involving the derivative constraints toward the jammer directions in the conventional Hung-Turner (1983) algorithm. The important advantage of the constraints used is that they do not require any a priori information about jammer directions. The computer simulations with wideband and moving jammers show that the proposed algorithm provides the significant improvement of the adaptive array performance as compared with the conventional HT algorithm. At the same time, for a moderate order of derivative constraints, the new algorithm has a computational efficiency, comparable with the conventional HT algorithm.

A pseudo-noise approach to direction finding

Abstract This paper addresses the problem of the design of eigenstructure estimator banks using a new pseudo-noise approach. We propose to obtain the underlying estimators for the estimator bank using a new type of resampling of some chosen eigenstructure estimator via pseudo-randomly generated noise. Censored selection of the results of pseudo-randomly resampled estimators is then exploited based on an appropriate local performance test. To illustrate the universality of our pseudo-noise approach and to demonstrate its applicability to the direction finding problem, we formulate a novel pseudo-noise-based estimator bank technique with particular application to the MUSIC estimator, though our concept can be applied in the same way to any other direction finding algorithm as well. The technique presented is shown to have considerable improvements of the threshold performance relative to conventional MUSIC. The strength and limitations of the pseudo-noise approach are demonstrated on simulations and real ultrasonic array data.

MODE with extra-roots (MODEX): a new DOA estimation algorithm with an improved threshold performance

We propose a new MODE-based direction of arrival (DOA) estimation algorithm with an improved SNR threshold as compared to the conventional MODE technique. Our algorithm preserves all good properties of MODE, such as asymptotic efficiency, excellent performance in scenarios with coherent sources, as well as a reasonable computational cost. Similarly to root-MODE, the proposed method does not require any global multidimensional optimization since it is based on a combination of polynomial rooting and a simple combinatorial search. Our technique is referred to as MODEX (MODE with EXtra roots) because it makes use of a certain polynomial with a larger degree than that of the conventional MODE-polynomial. The source DOAs are estimated via checking a certain (enlarged) number of candidate DOAs using either the stochastic or the deterministic maximum likelihood function. To reduce the computational cost of MODEX, a priori information about source localization sectors can be exploited.

Nonwave field processing using sensor array approach

Abstract The application of sensor array processing methods for estimation and localization of wavefield sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing approach to estimating the parameters of the fields of nonwave nature (the so-called nonwave fields). Considering the static and the diffusion field as typical examples of nonwave fields, and assuming that measurements are carried out by an antenna array, we derive the Cramer-Rao bounds of source parameter estimation errors. These theoretical results are completed by the experimental results of localization of the diffusion sources in distilled water by chemical sensor array, showing high performance of sensor array processing approach to the problem considered. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing.

An alternative approach to coherent source location problem

Abstract We propose a novel preprocessing scheme, referred to as (eigen) vector smoothing , as an alternative to the conventional spatial smoothing for solving the multiple source location problem involving coherent sources or a rank-deficient source covariance matrix. The essence of the technique is to preprocess the signal subspace eigenvectors rather than the covariance matrix as in spatial smoothing. It is shown by analysis and computer simulations that these two approaches are related, and that they have comparable estimation performances when employed with the MUSIC-type DOA estimators. Additionally, eigenvector smoothing offers advantages in terms of computational simplicity and flexibility. The latter is especially true with eigenstructure DOA estimators in adaptive estimation problems, i.e., when the signal subspace eigenvectors are updated using fast adaptive algorithms.

Removing the outliers in root-MUSIC via conventional beamformer

Abstract A simple approach is proposed to make high-resolution techniques robust against outliers associated with the false roots. It combines root-MUSIC and conventional beamformer into one scheme.

Joint estimation strategy with application to eigenstructure methods

Numerous authors have attempted to improve the performance of eigenstructure methods, but all these approaches do not employ the additive information arising when several direction of arrival (DOA) estimation algorithms (referred to as underlying estimators) are used simultaneously. We show that involving this information, one can achieve much better DOA estimation performance than that of each underlying estimator used separately. We introduce a joint estimation strategy (JES) which represents a simple and effective way of extracting and combining such information. This strategy is then applied to the set of eigenstructure underlying DOA estimators including the MUSIC and generalized min-norm (GMN) estimators.