Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA.
J Acoust Soc Am. 2011 Jun;129(6):3640-51. doi: 10.1121/1.3575604.
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.
主动声纳系统涉及一个或多个探测序列的发射和接收，这些序列为提取感兴趣区域的目标信息提供了基础。发射机中的探测序列和接收机中的信号处理在整个系统性能中起着至关重要的作用。在本文中，采用 CAN（循环算法-新）合成具有良好非周期自相关特性的探测序列。将比较 CAN 序列与伪随机噪声和随机相位序列的性能。还将考虑两种自适应接收机设计，即迭代自适应方法（IAA）和通过迭代最小化的稀疏学习（SLIM）方法。将 IAA 和 SLIM 与传统的匹配滤波器方法进行比较。通过数值示例说明了算法的性能，结果表明 CAN、IAA 和 SLIM 可以有助于提高主动声纳系统的整体性能。
