Optimum signal processing for passive sonar range and bearing estimation

Optimum signal processing for passive sonar target range and bearing estimation is discussed for the case where the sonar array consists of an M‐element linear array of hydrophone point detectors whole individual outputs are corrupted by sensor‐to‐sensor‐independent self‐noises of arbitrary power spectra. By comparing the measurement error covariance matrix to the Cramer–Rao matrix bound the system performance, relative to the theoretical optimum, is determined. Further, the Cramer–Rao bound is used to determine an optimum signal processor. The optimum processor is configured as a set of M (M‐1)/2 cross‐correlator delay estimators (one for each hydrophone pair), followed by a Gauss–Markov estimation of the array delay vector (target steering vector), which in turn is followed by a linear weighting of the estimated delay vector elements to determine a bearing estimate and a range estimate. The processor is shown to have the Cramer–Rao matrix bound for its measurement error covariance matrix.Subject Classif...