传感器网络基于特征值分解的信号被动定位技术

基于传感器网络的信号被动定位技术在电磁学、声学、声呐系统以及传热学等领域具有广泛的应用前景,当传感器网络节点所接收噪声强度不同或传输信道存在阴影衰落效应时,给出了目标信号到达距离比定位关联度量的估计方法与基于信号到达距离比的被动定位算法.将特征值分解技术引入到信号到达距离比定位关联度量估计中,通过接收信号协方差矩阵特征值分解技术估计各节点所接收噪声强度,并通过网络参考节点轮换与特征值分解方法消除阴影衰落效应所引入的定位误差,最后给出该算法的最小二乘定位解.该方法可较好的消除由于节点接收噪声强度不同以及阴影衰落效应等因素所带来的定位性能恶化.

Passive source localization using RROA based on eigenvalue decomposition algorithm in WSNs

When in WSNs sensors receive different noise intensities or the wireless transmission channel has the shadow fading effect, the association metrics estimation method for range ratios of arrival (RROA) and the passive source localization algorithm based on RROA are studied. Firstly, the eigenvector decomposition (EVD) approach is used to estimate the RROA association metrics. The noise intensity received by each sensor can be estimated by performing EVD on the covariance matrix of the received signal. Secondly, by rotating the array reference point at each of the array sensors, a number of covariance matrices are constructed and the EVD approach can be used to cancel the shadow fading effect. Thus RROA association metrics can be estimated reliably. Finally, the weighted-least-squares (WLS) algorithm based on the RROA association metrics is proposed. The proposed approach is robust to channel shadow fading effect and different noise intensities received.
Keywords： wireless sensor networks range ratio of arrival eigenvalue decomposition passive source localization