Performance stability of high-resolution matched-field processors to sound-speed mismatch in a shallow-water environment.

The development of effective passive sonar systems depends upon the ability to accurately predict the performance of sonar detection algorithms in realistic ocean environments. Such environments are typically characterized by a high degree of uncertainty, thus limiting the usefulness of performance prediction approaches that assume a deterministic environment. Here we derive closed-form receiver operating characteristic (ROC) expressions for an optimal Bayesian detector and for several typical suboptimal detectors, based on a statistical model of environmental uncertainty. Various scenarios extended from an NRL benchmark shallow-water model were used to check the analytical ROC expressions and to illustrate the effect of environmental uncertainty on detection performance. The results showed that (1) optimal detection performance in an uncertain environment in diffuse noise depends primarily on the signal-to-noise ratio at the receivers and the rank of the signal matrix, where the rank is an effective representation of the scale of environmental uncertainty; (2) the ROC expression for the optimal Bayesian detector provides a more realistic performance upper bound than that obtained from conventional sonar equations that do not incorporate environmental uncertainty; and (3) detection performance predictions can be performed much faster than with commonly used numerical methods such as Monte Carlo performance evaluations.

Section: A Environmental Uncertainty Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of environmental uncertainties on sonar detection performance prediction

Sha

Nolte

2005

“…Incomplete or imprecise knowledge of environmental parameters and randomness in the propagation environment are known to seriously deteriorate the performance of MFP, which has been investigated extensively in the past. [9][10][11][12][13][14][15][16][17][18] MFP has been demonstrated in a number of theoretical and experimental scenarios involving fluctuating or unknown environments, 14,17,18 but with significant localization ambiguities due to multimodal propagation and environmental mismatch. For example, in Ref.…”

Section: Introductionmentioning

confidence: 99%

General second-order covariance of Gaussian maximum likelihood estimates applied to passive source localization in fluctuating waveguides

Bertsatos

Zanolin

Ratilal

et al. 2010

A method is provided for determining necessary conditions on sample size or signal to noise ratio (SNR) to obtain accurate parameter estimates from remote sensing measurements in fluctuating environments. These conditions are derived by expanding the bias and covariance of maximum likelihood estimates (MLEs) in inverse orders of sample size or SNR, where the first-order covariance term is the Cramer-Rao lower bound (CRLB). Necessary sample sizes or SNRs are determined by requiring that (i) the first-order bias and the second-order covariance are much smaller than the true parameter value and the CRLB, respectively, and (ii) the CRLB falls within desired error thresholds. An analytical expression is provided for the second-order covariance of MLEs obtained from general complex Gaussian data vectors, which can be used in many practical problems since (i) data distributions can often be assumed to be Gaussian by virtue of the central limit theorem, and (ii) it allows for both the mean and variance of the measurement to be functions of the estimation parameters. Here, conditions are derived to obtain accurate source localization estimates in a fluctuating ocean waveguide containing random internal waves, and the consequences of the loss of coherence on their accuracy are quantified.

“…They are susceptible to large systematic errors from mismatch when adequate environmental information is not available. 7,8 The range of a source in a horizontally stratified ocean waveguide can sometimes also be estimated by the much simpler waveguide invariant method, [9][10][11] which employs only incoherent processing of acoustic intensity data as a function of range and bandwidth. The waveguide invariant method, however, requires knowledge of certain "invariant" parameters, which unfortunately often vary significantly with ocean sound speed structure.…”

Section: Introductionmentioning

confidence: 99%

The array invariant

Lee

Makris

2006

A method is derived for instantaneous source-range estimation in a horizontally stratified ocean waveguide from passive beam-time intensity data obtained after conventional plane-wave beamforming of acoustic array measurements. The method has advantages over existing source localization methods, such as matched field processing or the waveguide invariant. First, no knowledge of the environment is required except that the received field should not be dominated by purely waterborne propagation. Second, range can be estimated in real time with little computational effort beyond plane-wave beamforming. Third, array gain is fully exploited. The method is applied to data from the Main Acoustic Clutter Experiment of 2003 for source ranges between 1 to 8 km, where it is shown that simple, accurate, and computationally efficient source range estimates can be made.