Multi-array systems are widely used in sonar and radar applications. They can improve communication speeds, target discrimination, and imaging. In the case of a multibeam sonar system that can operate two receiving arrays, we derive new adaptive to improve detection capabilities compared to traditional sonar detection approaches. To do so, we more specifically consider correlated arrays, whose covariance matrices are estimated up to scale factors, and an impulsive clutter. In a partially homogeneous environment, the 2-step Generalized Likelihood ratio Test (GLRT) and Rao approach lead to a generalization of the Adaptive Normalized Matched Filter (ANMF) test and an equivalent numerically simpler detector with a well-established texture Constant False Alarm Rate (CFAR) behavior. Performances are discussed and illustrated with theoretical examples, numerous simulations, and insights into experimental data. Results show that these detectors outperform their competitors and have stronger robustness to environmental unknowns.

翻译：多阵列系统广泛应用于声呐和雷达领域，可提升通信速率、目标分辨能力和成像质量。针对可同时运行两个接收阵列的多波束声呐系统，我们推导出新型自适应检测方法以提升相较于传统声呐检测的性能。为此，我们特别考虑协方差矩阵仅需估计至尺度因子的相关阵列及冲激性杂波场景。在部分均匀环境中，采用两步广义似然比检验（GLRT）和Rao方法，最终得到自适应归一化匹配滤波器（ANMF）检验的推广形式，并构建了具有纹理恒虚警率（CFAR）特性的等效数值简化检测器。通过理论示例、大量仿真和实测数据验证，结果表明该检测器性能优于现有方法，且对环境未知量具有更强的鲁棒性。