中文摘要：

将传感网中随机布设传感器节点所接收到的微弱信号进行合成,可有效增强传感器网络的信号感知能力.本文关注的是该微弱信号的合成权值估计问题.以合成信号的自相关系数作为目标函数,本文提出了一种基于特征值分解的合成权值估计算法.该算法无需估计噪声相关矩阵,适用于噪声方差不一致的环境.数值仿真结果显示,本文提出的基于自相关系数的特征值分解合成权值估计算法,在低信噪比、噪声方差不等的条件下,性能优于以合成信号信噪比或者合成信号功率为目标函数的特征值分解合成权值估计算法.

英文摘要：

Randomly distributed sensor nodes in the sensor network may receive weak signals. Signal combining technology can be used to enhance the performance of signal perception for the sensor network system. This paper focuses on the combining weight estimation for weak signal combining for the wireless sensor network. Taking the combined signal autocorrelation coefficient as the object function, this paper proposes an eigen value decomposition-based combining weight estimation algorithm. The algorithm does not require to estimate the noise correlation malrix, and can be applied to the non-uniform noise variance environment. The numerical simulation results show that the proposed algorithm outperforms classical eigen-based algorithm using the combined signal SNR or power as the object function, under low SNR environment with non-uniform noise variance for weak sensor nodes signal combining.