Threshold Optimization for Distributed Detection using Particle Filtering Methods

Local processing on the nodes of a distributed sensing and processing system has the benefits of reducing the data volume transferred from the nodes to the fusion center, reducing both transmission power requirements and the computational burden on the fusion center. The individual nodes obtain measurements from the environment and transmit a quantized detection statistic to the fusion center. Quantization threshold levels need to be found for each sensor that maximize the performance of the system. We propose a global optimization method, the particle filtering optimization method, that uses particle filtering to propagate the values of the thresholds of a distributed detection system to sensor threshold values that are optimal with respect to some measure of system performance. We demonstrate, through simulations, the effectiveness of the particle filtering optimization method in finding the threshold of each of the sensors used in detection scenario