Probing waveforms and adaptive receivers for active sonar

Active sonar systems involve the transmission and reception of one or more probing sequences, which provide a basis for extraction of target information in a region of interest. The probing sequences at the transmitter and signal processing at the receiver play crucial roles in the overall system performance. In this paper, CAN (cyclic algorithm-new) is employed to synthesize probing sequences with good aperiodic autocorrelation properties. The performance of the CAN sequences will be compared with those of pseudo random noise and random phase sequences. Two adaptive receiver designs, namely the iterative adaptive approach (IAA) and the sparse learning via iterative minimization (SLIM) method, will also be considered. IAA and SLIM will be compared with the conventional matched filter method. The performances of the algorithms will be illustrated via numerical examples, which show that CAN, IAA, and SLIM can contribute to the overall performance improvement of the active sonar systems.