Pan Xiang, Jiang Jingning, Li Si, Ding Zhenping, Pan Chen, Gong Xianyi
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Hangzhou Xuejun High School, Hangzhou 310012, China.
Sensors (Basel). 2018 Apr 10;18(4):1154. doi: 10.3390/s18041154.
A coherent-noncoherent joint processing framework is proposed for active sonar to combine diversity gain and beamforming gain for detection of a small target in shallow water environments. Sonar utilizes widely-spaced arrays to sense environments and illuminate a target of interest from multiple angles. Meanwhile, it exploits spatial diversity for time-reversal focusing to suppress reverberation, mainly strong bottom reverberation. For enhancement of robustness of time-reversal focusing, an adaptive iterative strategy is utilized in the processing framework. A probing signal is firstly transmitted and echoes of a likely target are utilized as steering vectors for the second transmission. With spatial diversity, target bearing and range are estimated using a broadband signal model. Numerical simulations show that the novel sonar outperforms the traditional phased-array sonar due to benefits of spatial diversity. The effectiveness of the proposed framework has been validated by localization of a small target in at-lake experiments.
提出了一种用于主动声纳的相干-非相干联合处理框架，以结合分集增益和波束形成增益，用于在浅水环境中检测小目标。声纳利用间距较大的阵列来感知环境，并从多个角度照射感兴趣的目标。同时，它利用空间分集进行时间反转聚焦，以抑制混响，主要是强烈的海底混响。为了增强时间反转聚焦的鲁棒性，在处理框架中采用了自适应迭代策略。首先发射一个探测信号，并将可能目标的回波用作第二次发射的导向矢量。利用空间分集，使用宽带信号模型估计目标方位和距离。数值模拟表明，由于空间分集的优势，新型声纳优于传统相控阵声纳。所提出框架的有效性已通过在湖上实验中对小目标的定位得到验证。
