无线传感器网络中的奇异信号检测研究

Study on singular signal detection in wireless sensor network

无线传感器网络奇异信号对故障诊断具有重要意义,针对当前无线传感器网络奇异信号检测结果不理想等问题,提出小波分析和混沌理论相结合的传感器网络奇异信号检测算法。采用小波分析对传感器网络奇异信号进行分解,通过混沌理论对分解后信号分量进行处理,采用数据挖掘技术对传感器网络奇异信号进行检测。结果表明,相对于传统信号检测算法,本文算法明显提高了传感器网络奇异信号检测精度,降低了奇异信号误检率和漏检率,可以保证无线传感器网络的通信安全。

Singular signals of sensor network for fault diagnosis is of great significance, for the wireless sensor network for signal singularity detection result is not ideal, a sensor network signal singularity detection method is proposed by the combination of wavelet analysis and chaos theory. Firstly, wavelet analysis is used to decomposed the singular signal of sensor network, and then chaos theory is used to process decomposed signal, finally, data mining technology is used to detect signal singularity of sensor network. Experimental results show that compared with traditional signal detection method, the proposed method can significantly improve singular signal detection accuracy of sensor network, reduces the false detection rate and false negative rate, can guarantee communication security of wireless sensor network.