Adaptive State Estimation Over Lossy Sensor Networks Fully Accounting for End-To-End Distortion
Bohan Li, Tejaswi Nanjundaswamy, Kenneth H. Rose · 2018
This paper investigates state estimation with wireless sensors communicating over unreliable bandwidth-limited networks. Specifically, we consider so-called smart sensors equipped with simple processing units, which enable predictive coding at the sensor side, to meet the bandwidth constraint. While predictive coding significantly reduces the bit-rate by removing temporal redundancies, it exacerbates the impact of packet loss, due to error propagation through the prediction loop, potentially causing significant degradation of the reconstructed signal. To fully account for and control the conflict between coding efficiency and robustness to packet loss, we propose a coding approach that explicitly optimizes the tradeoff between rate and end-to-end distortion (EED). The proposed method determines optimal switching decisions between available coding modes, offering different compression-robustness operating points, based on EED that is optimally estimated at the encoder (sensor), in order to realize the best rate-distortion tradeoff. Simulation results demonstrate that the proposed approach achieves considerable gains in signal-to-noise ratio for state estimation over lossy sensor networks.