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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5~8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。
The traditional signal detection algorithm in the uncertain performance of the marine environment decreased. The optimal detection algorithm based on the Bayesian principle can effectively detect the signal in the uncertain ocean environment, but the outstanding problem is the computational complexity. In this paper, a robust signal detector based on principal component analysis is proposed. The detector uses Bayesian principle to introduce environmental prior information into the detection algorithm. At the same time, the principal component analysis method is used to reduce the computation load. Fast and effective signal detection. The results show that: (1) The detection performance of Robust signal detector based on principal component achieves the effect of optimal Bayesian detector. (2) The online computation speed of this method is 5 ~ 8 times of Bayesian optimal detector. (3) Under the circumstance of the mismatch of environment a priori information, enlarging the uncertainty range of marine environmental parameter model will help to improve the detection performance.