基于压缩感知的无线阵列及协同信号处理

传感器阵列信号处理是目标监测重要手段之一，传感器节点向后端数据融合中心发送原始信号不可避免地导致传输延时大、能耗高等问题.为实现低功耗、高精度、灵活部署的目标监测，提出了基于压缩采样的无线阵列.借助新兴的压缩感知理论，解决低功耗低速率无线通信难以满足阵列原始信号的实时传输的问题，并保证阵列测向性能；同时根据相邻节点信号的相关性，设计了基于模型先验知识的信号协同重构算法，以较低的运算负荷完成信号的重构.仿真表明基于压缩感知的无线阵列能实现有效的目标测向，同时在数据量严重受限时性能明显优于传统方法.最后，通过简易实验验证了该方法在低成本平台上的可行性.

Compressive Sensing-Based Wireless Array and Collaborative Signal Processing Method

Sensor array signal processing has shown great potential in target monitoring applications. However, for wireless sensor network nodes, sending raw signal would result in large transmission delay and consume too much energy.To achieve low power consumption, high accuracy and deployment flexibility in target monitoring under the framework of wireless sensor network (WSN), a compressed sampling-based wireless array is presented. With the help of compressive sensing theory, real-time array signal transmission is made feasible on low-power, low-cost wireless platform. In this framework, signals are randomly sampled at a lower average sampling rate and reconstructed at the fusion center, which is supposed to have powerful processing capability. Furthermore, based on the correlation of signals within an array, collaborative array signal reconstruction algorithm is designed using priori knowledge model to reduce redundancy in signal collection. Simulation results show that compressed sampling-based wireless array can perform effective direction of arrival (DoA) estimation for targets without distinct performance decline, and it even outperforms traditional method obviously when data transmission is heavily restricted. It is show that CS based DoA estimation has similar performance with conventional method while requiring about 15% data in transmission. Experiment is also conducted on prototype system with low cost microphones arrays, thus verifying the feasibility of implementation.