水下混沌背景中的瞬态声信号检测法研究  

Detection of underwater transient acoustic signal under chaotic background

作　　者：杨德森 肖笛 张揽月 

机构地区：[1]哈尔滨工程大学水声技术重点实验室,哈尔滨150001

出　　处：《振动与冲击》2013年第10期26-30,共5页Journal of Vibration and Shock

摘　　要：水下瞬态声信号中蕴含着目标的特征信息,但其突发性强、持续时间短致使检测难度很大。为解决瞬态信号检测的问题,提出了混沌背景中瞬态冲击信号的RBF神经网络检测法。建立了混沌背景噪声的一步预测模型,通过预测误差的变化来检测瞬态信号。分别以Lorenz系统和Logistic系统作为混沌背景噪声进行了仿真,证明检测方法的有效性,并在Lorenz系统背景检测中加入白噪声来检验该方法抗白噪声干扰的能力,结果表明该方法对白噪声敏感;在理论研究的基础上通过对外场试验数据的处理验证了该方法的有效性,并在实际测量数据中加入混沌背景噪声,通过改变信噪比检验了该方法在不同信噪比情况下的性能。In order to detect the underwater transient acoustic signal, which often breaks out abruptly and only last a few periods, a RBF (Radical Basis Function) neural network detection method under chaotic background was put forward. Due to the characters of RBF network and chaotic system, a one-step predicting model was set up. The simulation experiments was carried out on both Lorenz system and Logistic system, and white noise was also put into the chaos background to inspect the detecting ability of the method. The method was proved to be sensitive to white noise. Based on the theoretical analysis, several data gained from the experiments on the lake were processed to prove the validity of the method presented above. In the experiments, a sect of stronger chaos background noise was added to investigate the performance of the detection method under different SNRs.

关 键 词：瞬态信号 混沌系统 RBF神经网络 信号检测 

分 类 号：O427.9[理学—声学]