Passive towed arrays assume great importance in shallow ocean surveillance, owing to their capability to detect long range underwater acoustic targets. The detection and classification performance is severely impacted...
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Passive towed arrays assume great importance in shallow ocean surveillance, owing to their capability to detect long range underwater acoustic targets. The detection and classification performance is severely impacted by the tow ship interference manifestation in the pressure sensor array towed behind the ship. In this work, a robust tow ship multipath interference mitigation algorithm intended for shallow ocean operation is developed, based on the dynamic mode decomposition (DMD) technique, which in turn exploits the coherent structure of the multimode acoustic wave propagation inherent in the shallow ocean scenario. The proposed algorithm effectively identifies the coherent normal mode interference subspace and eliminates its components from the received array data vector. Further, this approach compliments the shallow ocean normal mode acoustic field propagation and ensures simultaneous mitigation of all the multipath generated interferences, making it a superior method to reduce the azimuth spread of the interference and tow ship spectral leakage into the panoramic detection beams. This greatly enhances the detection and classification capability of sonar, especially for distant stealthy targets. The efficacy of the algorithm is validated using Monte Carlo simulations and further ratified by experimental data captured during the field trials in the Arabian Sea.
The authors present an efficient technique for computing the eigensubspace spanned by a received array data vector. First, the sensor array is partitioned into several subarrays without overlapped sensors. The basis m...
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The authors present an efficient technique for computing the eigensubspace spanned by a received array data vector. First, the sensor array is partitioned into several subarrays without overlapped sensors. The basis matrix for the signal subspace of each subarray is computed. Using these basis matrices, an additional subarray is constructed and the basis matrix for the corresponding signal subspace is also computed. By using these subarray basis matrices, the basis matrix for the signal subspace of the original sensor array can be computed with reduced computing cost. The statistical performance and computational complexity for adaptive beam-forming and for bearing estimation using the proposed technique are evaluated. The theoretical results are confirmed and illustrated by simulation results.
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