State estimation with quantized innovations in wireless sensor networks

(in lingua inglese)

This paper explores an innovative Gaussian mixture state estimation algorithm. By studying the assumptions of prior and posterior pdfs which are based on the quantized innovations, a Gaussian mixture estimator has been derived in the paper. Besides, a recursive posterior CRLB for state estimation using quantized innovations in WSNs is developed. The theoretical lower bound is estimated approximately by adopting a Monte-Carlo method. Performance analysis and simulation experiments show that the Gaussian mixture estimator is better than those quantization KF algorithms.