Distributed Kalman filtering based on quantized innovations

We consider state estimation of a Markov stochastic process using an ad hoc wireless sensor network (WSN) based on noisy linear observations. Due to power and bandwidth constraints present in resourcelimited WSNs, the observations are quantized before transmission. We derive a distributed recursive mean-square error (MSE) optimal quantizer-estimator based on the quantized observations. The resultant Kalman-like algorithm based on quantized observations exhibits MSE performance and computational complexity comparable to the Kalman filter based on un-quantized observations even for 2-3 bits of quantization per observation.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Eric J. Msechu, Alejandro Ribeiro, Stergios I. Roumeliotis, Georgios B. Giannakis
• Keywords: Exhibits Mse Performance, Hoc Wireless Sensor, ICASSP 2008, Noisy Linear Observations, Signal Processing