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Active sonar systems involve the transmission and reception of one or more sequences, which provide a basis for extraction of the information on targets in the region of interest. The probing sequences at the transmitter and signal processing at the receiver play crucial roles in the overall system performance. We consider herein using CAN (cyclic algorithm-new) to synthesize probing sequences with good aperiodic autocorrelation properties. The performance of the CAN sequences will be compared with that of pseudo random noise (PRN) and random phase (RP) sequences, which often find uses in the active sonar systems. We will also consider two adaptive receiver designs, namely the iterative adaptive approach (IAA) and sparse learning via iterative minimization (SLIM) method. We will illustrate the performance of the algorithms via numerical examples, by comparing IAA and SLIM with the conventional matched filter (MF) method. Experimental results show that CAN, IAA and SLIM can contribute to the overall performance improvement of the active sonar systems.