Maximum-likelihood spatial spectrum estimation in dynamic environments with a short maneuverable array

Abstract

This work concerns the development of field directionality mapping algorithms for short acoustic arrays on mobile maneuverable platforms that avoid the left/right ambiguities and endfire resolution degradation common to longer non-maneuverable line arrays. In this paper, it is shown that short maneuverable arrays can achieve a high fraction of usable bearing space for target detection in interference-dominated scenarios, despite their lower array gain against diffuse background noise. Two narrowband techniques are presented which use the expectation-maximization maximum likelihood algorithm under different models of the time-varying field directionality. The first, derivative based maximum likelihood, uses a deterministic model while the second, recursive Bayes maximum likelihood, uses a stochastic model for the time-varying spatial spectrum. In addition, a broadband extension is introduced that incorporates temporal spectral knowledge to suppress ambiguities when the average sensor array spacing is greater than a half-wavelength. Dynamic multi-source simulations demonstrate the ability of a short, maneuvering array to reduce array ambiguities and spatial grating lobes in an interference dominated environment. Monte Carlo evaluation of receiver operating characteristics is used to evaluate the improvement in source detection achieved by the proposed methods versus conventional broadband beamforming.

Approximately optimal distribution of depth sensors over towed array

“…The covariance matrix of the state is the analytic solution to an algebraic Riccati equation, which is pointed out in chapter 9.3.2.1 in [28]. This Riccati equation is (5) in our study which is (10) in [15].…”

“…After thorough checking the simulation results, MAMSEs (2.3375σ 2 , 4.4181σ 2 , 6.4451σ 2 ) are achieved at (3,5), (7,13), (11,21), respectively. However, the previous analysis indicates that when N = 8, MAMSEʹs are achieved at (2,5), (3,5) and (3,6). The AMSEs of these distributions are 2.4196σ 2 , 2.3375σ 2 , and 2.3536 σ 2 .…”

“…In terms of data source, they are normally classified into acoustic methods and non-acoustic methods. The former class utilises the relevance of data from acoustic hydrophones to estimate array shape [3][4][5][6][7][8][9][10][11][12]. However, multivariate maximisation, which exists in this kind of processor, results in high computational burden.…”

Heading and hydrophone data fusion for towed array shape estimation

“…The bias at the mth sensor is denoted by β m . The estimate with minimum mean squared error, for a single hydrophone heading, is given byθ = κθ α + (1 − κ)θ H (10) where the variance of the acoustic and hydrophone based estimates, σ α and σ H respectively, appear in the weighting factor given by…”

Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

“…The aspect of the phase difference forms the essence of localization using spatial information [11]. Methods for acoustic source localization can be generally divided into three categories based on computational complexity: time delay based methods [12][13][14], subspace based methods, and parametric methods [15][16][17]. The time difference of arrival (TDOA) in time delay based methods is obtained from the phase differences of microphones [18]; it is easy to compute the TDOA if delays are known to be less than the sampling period.…”