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Peak-picking in Spatial Spectra

In passive SONAR, methods such as conventional beamforming, Capon and MUSIC are often used to calculate the spatial spectrum. It is basically a curve displaying power versus steering angle (see [1] for an introduction into the terminology used). Under ideal circumstances, i.e. large number of samples and spatially white noise, the spatial spectrum will exhibit one peak corresponding to each impinging source, making the peak-picking trivial. Under more practical circumstances the information in the spatial spectrum is more vague. Classifying all the peaks as being true sources will result in the reporting of many false bearings. The problem of peak-picking in passive SONAR has not been systematically addressed in public literature as far as we now. We suggest the use of a metric to judge the likelihood of a peak being a true far-field source vs a spurious one. To investigate this approach we define four scenarios with distributions for the occurrence and parameters of true far-field sources, noise correlation matrices (the noise correlation matrices are modelled by near-field sources plus spatially white noise). In the paper we find that a neural network based solution generally gives the best results when it is trained and applied on the same scenario. A more robust, but still well performing solution, called Peak over local average (PoLA) below, classifies the peaks by comparing their height over the mean in their local surrounding.