Online rate adjustment for adaptive random access compressed sensing of time-varying fields

Abstract

We develop an adaptive sensing framework for tracking time-varying fields using a wireless sensor network. The sensing rate is iteratively adjusted in an online fashion using a scheme that relies on an integrated sensing and communication architecture. As a result, this scheme allows for an implementation that is both energy efficient and robust. The objective is to promote an “active" framework which uses the information extracted from the network data and iteratively adjusts the monitoring process to capture the temporal variations in the monitored field. We propose two metrics based on target detection/tracking for this feedback scheme that seek to trade off between energy efficiency and accuracy of the detection/tracking tasks. Our simulation results suggest that tying target detection with the rate adjustment algorithm ensures that the robustness to changes in the field can be achieved simultaneously with the end goal of accurate target detection. Compared to a baseline method that uses the correlation of the acquired field over time, our method exhibits better performance when the targets of interest have a smaller spatial spread.