不确定海洋环境中基于主成分量的稳健检测器

传统的信号检测算法在不确定的海洋环境中性能出现下降。基于贝叶斯原理的最优检测算法可以实现对不确定海洋环境中信号的有效检测,但是其突出问题是计算量较大。本文提出了一种基于主成分量分析的稳健信号检测器,该检测器利用贝叶斯原理将环境先验信息引入到检测算法中,同时使用主成分量分析方法来降低运算量,实现了对信号的快速有效检测。分别使用标准失配海洋模型和海上实测数据进行了计算机仿真和实验验证,结果表明:(1)基于主成分量的稳健信号检测器检测性能达到最优贝叶斯检测器的效果。(2)本文方法在线运算速度是贝叶斯最优检测器的5^一8倍。(3)环境先验信息失配的情况下,扩大海洋环境参数模型的不确定度范围有助于提高检测性能。 

A robust detector based on the principal component analysis in uncertain ocean

Environmental uncertainty can cause severe performance degradation to sonar detection algorithms that rely on precise knowledge of the environmental parameters. The optimal uncertain field processor (OUFP) can detect the signal robustly. However, it suffers a big computation burden. A new robust detector is proposed in this paper which uses the Bayesian theory to utilize the a priori information of the environmental acoustic parameters and the principal component analysis to reduce the computation cost. The detector can detect the signal robustly and efficiently. Computer simulation and experimental data verification show that, (1) the proposed detector has an equal detection performance to the OUFP and outperforms the conventional used mean ocean detector and energy detector; (2) The computation complexity is typically 1/8-1/5 of the OUFP; (3) Assuming that the ocean environmental uncertainties range is somewhat greater than truth can get a better performance.