基于SAE-SVM算法的振动信号定位方法研究

Research on vibration signal localization method based on SAE-SVM algorithm

针对传统振动信号短时能量检测法精度低、需手工参数选择等问题,提出了一种稀疏自编码器(SAE)网络,用于提取振动信号有效特征,并将其用于支持向量机(SVM),从而检测脚步振动信号。为了缓解了振动信号色散效应造成的信号失真问题,使用了小波分解(WT)方法,并基于实验分析优化了分解参数,然后基于广义互相关和到达时间差(TDoA)算法进行定位解算。实验结果表明,相比人工特征筛选,SAE-SVM算法的活动段检测精度可达96.8%,系统平均定位误差为0.82 m。

A sparse autoencoder(SAE)network is proposed to extract the effective features of vibration signals and apply them to support vector machine(SVM)to detect footstep vibration events,aiming at the problems of low accuracy and manual parameter selection of traditional short-time energy detection method.To alleviate the signal distortion caused by the dispersion effect of vibration signal,the wavelet decomposition method is used,and the decomposition parameters are optimized based on the experimental analysis,and then the location is solved based on the generalized cross-correlation(GCC)and time difference of arrival(TDoA)algorithm.Experimental results show that,compared with manual feature screening,the detection accuracy of an active segment can reach 96.8%by the SAE-SVM algorithm,and the average positioning error of the system is 0.82 m.