Effects of environmental uncertainties on sonar detection performance prediction

Abstract

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.

Empirical dependence of acoustic transmission scintillation statistics on bandwidth, frequency, and range in New Jersey continental shelf

“…When a wide-area sonar system is used to study scattering from distributed scatterers, such as a large fish shoal or the sea bottom, over areas spanning tens of thousands of km 2 with varying bathymetry, it is necessary to correct for transmission loss ͑TL͒ over these vast areas. 2,14,[28][29][30][31] Since the waveguides are often random and the transmitted signals are often broadband, the number of computations required per radial with a range-dependent waveguide propagation model such as the parabolic equation make brute-force Monte Carlo simulations impractical for many applications that require rapid analysis of hundreds of wide-area sonar images typically collected in a single day at sea. 2,14 Here, we provide an approach for rapidly and accurately estimating broadband TL expected over wide areas.…”

Time-evolving acoustic propagation modeling in a complex ocean environment

“…Another necessary measure is to accompany predictions with a measure of uncertainty. This has been done in the context of passive sonar, for the influence of the water column on propagation loss [27,32,39,40] and beamforming gain [8] and the influence of uncertainty in bottom reflection properties on propagation prediction [41]. The complexity is increased for active sonar performance prediction.…”

Fast Unsupervised Seafloor Characterization in Sonar Imagery Using Lacunarity

“…Specifically, we show that it is a very powerful quantity for characterizing seafloor to aid targetdetection tasks in side-looking sonar images. This result can be exploited to create object-detection performance prediction models [24], [25] and to inform environmentally adaptive classification algorithms [26]- [28]. Moreover, the results suggest that the lacunarity would be a simple yet powerful feature for performing (hard) seafloor segmentation, if desired.…”