Power-Efficient Dimensionality Reduction for Distributed Channel-Aware Kalman Tracking Using WSNs

Estimation of nonstationary dynamical processes is of paramount importance in various applications including target tracking and navigation. The goal of this paper is to perform such tasks in a distributed fashion, using data collected at power-limited sensors which either communicate with a fusion center (FC) over noisy links, or, communicate with each other over nonideal channels in an ad hoc setting. In FC-based wireless sensor networks (WSNs) with a prescribed power budget, linear dimensionality reducing operators which account for the sensor-to-FC channel are derived per sensor to 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 reduce the dimensionality of their local innovation sequences and communicate them to the FC where tracking estimates are corrected. Analytical and numerical results advocate that the novel channel-aware distributed tracker outperforms competing alternatives. In ad hoc WSNs deployed to perform distributed tracking, one sensor broadcasts reduced-dimensionality data per time slot, according to a prespecified transmission order. The dimensionality reducing operators employed by the broadcasting sensor are selected to meet its transmit-power budget, while minimizing the state estimation MSE of the sensor with the lowest receiving SNR. Based on the received reduced-dimensionality data from the broadcasting sensor, every sensor in range performs the NISE optimal tracking. Corroborating distributed target tracking simulations based on distance-only observations illustrate that the novel scheme provides sensors with accurate estimates at affordable communication cost.