Power-efficient dimensionality reduction for distributed channel-aware Kalman tracking using wireless sensor networks

Estimation and tracking of nonstationary dynamical processes is of paramount importance in various applications including localization and navigation. The goal of this paper is to perform such tasks in a distributed fashion using data collected at power-limited sensors communicating with a fusion center (FC) over noisy links. For a prescribed power budget, linear dimensionality reducing operators are derived per sensor to account for the sensor-FC channel and minimize the mean-square error (MSE) of Kalman filtered state estimates formed at the FC. Using these operators and state predictions fed back from the FC online, sensors compress their local innovation sequences and communicate them to the FC where tracking estimates are corrected. Analysis and corroborating simulations confirm that the novel channel-aware distributed tracker outperforms competing alternatives.