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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 wireless sensor networks are of great importance to fault diagnosis. In order to solve the problem that current singular signals of wireless sensor networks are not ideal, this paper proposes a singular signal detection algorithm based on wavelet analysis and chaos theory. Wavelet analysis is used to decompose the singular signals in the sensor network. The chaotic theory is used to process the decomposed signal components. Data mining is used to detect the singular signals in the sensor network. The results show that compared with the traditional signal detection algorithm, the proposed algorithm obviously improves the detection accuracy of singular signals in sensor networks and reduces the false detection rate and missed detection rate of the singular signals, which can ensure the communication security of wireless sensor networks.