Distributed Signal Estimation Using Binary Sensors with Multiple Thresholds

Estimating an unknown parameter using a sensor network has been considered when the fusion center receives only one bit from each sensor. The network is divided to a number of groups and all sensors in a group use a fixed and equal threshold for quantization. A combined weighted estimation structure has been proposed. At the fusion center weights are assigned to each group of sensors based on their quantized bit entropy. The results have been presented and compared to maximum likelihood estimator (MLE), which had been proposed for the same scenario. The simulation results show that the proposed method has identical performance for large number of sensors and reaches Cramer-Rao lower bound(CRLB), while it outperforms MLE for limited number of sensors and when the distance between quantization levels increases. Moreover the proposed method has very low complexity compared to MLE. It is also considerably faster than MLE, which makes it more suitable for wireless sensor networks.