Broadband ML-approach to environmental parameter estimation in shallow ocean at low SNR. (Q5940846)

Ocean acoustic parameter estimation using a horizontal hydrophone array is addressed in this paper. Shipping noise is exploited as a source of acoustic energy. Shipping noise shows large relative bandwidths: signal energy is spread over a couple of octaves. Locations of the acoustic sources are not assumed to be known and have to be estimated simultaneously with ocean depth. We develop a procedure based on conditional maximum-likelihood estimation for broadband signals in a shallow ocean. Monte Carlo simulations based on the bootstrap paradigm demonstrate feasibility of our approach at low signal-to-noise ratio and allow to analyze the estimator accuracy. A simple parametric approach is used in this paper which generates bootstrapped samples of the estimates of the array cross spectral density matrices. Bootstrapped parameter estimates are obtained by applying the proposed estimator to the bootstrapped samples. The joint distribution of the bootstrapped estimates shows low correlation between source bearing, source depth, and environmental parameters. Estimates of source range, however, are highly correlated with sound speed and ocean depth. If the source range estimate is larger than the true range value, the ocean depth estimate is found to be smaller than the true depth. The proposed broadband processor is applied to the sensor data obtained from an experiment with a towed horizontal hydrophone array in the Baltic Sea.