Waveguide Invariant Active Sonar Target Detection and Depth Classification in Shallow Water

Abstract

Reverberation and clutter are two of the principle obstacles to active sonar target detection in shallow water. Diffuse seabed backscatter can obscure low energy target returns, while clutter discretes, specific features of the sea floor, produce temporally compact returns which may be mistaken for targets of interest. Detecting weak targets in the presence of reverberation and discriminating water column targets from bottom clutter are thus critical to good performance in active sonar. Both problems are addressed in this thesis using the time-frequency interference pattern described by a constant known as the waveguide invariant which summarizes in a scalar parameter the dispersive properties of the ocean environment. Conventional active sonar detection involves constant false alarm rate (CFAR) normalization of the reverberation return which does not account for the frequency-selective fading in a wideband pulse caused by multipath propagation. An alternative to conventional reverberation estimation is presented, motivated by striations observed in time-frequency analysis of active sonar data. A mathematical model for these reverberation striations is derived using waveguide invariant theory. This model is then used to motivate waveguide invariant reverberation estimation which involves averaging the time-frequency spectrum along these striations. An evaluation of this reverberation estimate using real Mediterranean data is given and its use in a generalized likelihood ratio test (GLRT) based CFAR detector is demonstrated. CFAR detection using waveguide invariant reverberation estimates is shown to outperform conventional cell-averaged and frequency-invariant CFAR detection methods in shallow water environments producing strong reverberation returns which exhibit the described striations. Results are presented on simulated and real Mediterranean data from the SCARAB98 experiment. The ability to discriminate between water column targets and clutter discretes is vital to maintaining low false alarm rates in active sonar. Moreover, because of the non-stationarity of the active sonar return, classification is most typically achieved using a single snapshot of test data. As an aid to classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide-invariant spectral density matrix (WI-SDM) which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is performed by a waveguide-invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) and averaging the signal replica vectors over the unknown channel parameters. Simulation and real data results from the SCARAB98, CLUTTER07, and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach.