Matched-field depth estimation for active sonar.

This work concerns the problem of estimating the depth of a submerged scatterer in a shallow-water ocean by using an active sonar and a horizontal receiver array. As in passive matched-field processing (MFP) techniques, numerical modeling of multipath propagation is used to facilitate localization. However, unlike passive MFP methods where estimation of source range is critically dependent on relative modal phase modeling, in active sonar source range is approximately known from travel-time measurements. Thus the proposed matched-field depth estimation (MFDE) method does not require knowledge of the complex relative multipath amplitudes which also depend on the unknown scatterer characteristics. Depth localization is achieved by modeling depth-dependent relative delays and elevation angle spreads between multipaths. A maximum likelihood depth estimate is derived under the assumption that returns from a sequence of pings are uncorrelated and the scatterer is at constant depth. The Cramér-Rao lower bound on depth estimation mean-square-error is computed and compared with Monte Carlo simulation results for a typical range-dependent, shallow-water Mediterranean environment. Depth estimation performance to within 10% of the water column depth is predicted at signal-to-noise ratios of greater than 10 dB. Real data results are reported for depth estimation of an echo repeater to within 10-m accuracy in this same shallow water environment.

Duke Scholars

Author Jeffrey L. Krolik Electrical and Computer Engineering