Signal Model Based Compressed Sampling for Wireless Sensor Array Network

High sampling rate signal acquisition is challenging for wireless platform in terms of energy supply and transmission delay. Instead of performing compression at sensor node or having in-network processing for data been sampled at Nyquist rate, Compressive Sensing (CS) is applied to enable real time wireless sensor network with strict energy and processing constraints by significantly reducing the sensor data volume that needs to be transmitted over wireless channels. This is accomplished by random sampling at sensor nodes without extra processing and a mixture model based collaborative signal reconstruction in the fusion centre. This method increases signal reconstruction performance while reducing the volume of transmission data. Analysis of data from experiment and simulation are provided, and the performance are evaluated by implementing a prototype wireless platform.