Target-depth estimation in active sonar

This paper presents a new target-depth estimation method that is based on ray back-propagation with a probabilistic approach. This localization algorithm tries to minimize the mean-squared error of elevation angles at the receiver and arrival times between a model and measures. This method was tested on Monte-Carlo simulations of classic active sonar scenarios and using experimental data from a real tank. In active sonar with a point-target model, combined acoustic paths may exist. These have a different path between the sonar-target and the target-array. This paper discusses also about this ray identification. Simulations with vertical array suggest that the target-depth estimation can be realized with a low uncertainty compared to the water column for long ranges in a Mediterranean sound speed profile. However, some environmental parameters as random sound-speed profile, array depth or array tilts could increase the bias and the variance of the target-depth estimator. Results on experimental data with surface noise reveal a good estimation of the target depth and validate our localization algorithm on a constant sound-speed profile with a vertical array.