Application of Riemann Distance in Depth Estimation of Active Sonar Targets in Shallow Waters

An obstacle to doing matched filed processing for depth estimation of active sonar targets is the amount of target data available. Goldhahn proposed in 2010 to sub-sampling the STFT vector of the target in the frequency domain to obtain multiple snapshots of the target data in the frequency domain to improve the robustness of active target depth estimation. Considering that the waveguide invariant-spectral density matrix is approximately a Hermitian matrix, forming a Riemannian manifold in the signal space, this paper introduces the correlation between the Riemann distance measurement spectral density matrix to construct the ambiguity surface of the depth estimation, and the simulation analysis compares the performance of estimators such as Bartlett, Minimum Variance and Riemann.