Target Tracking In a Sensor Network Based on Particle Filtering and Power-Aware Design

Abstract

In this paper, we present a novel target tracking method applied to a distributed acoustic sensor network. The underlying tracking methodology is described as a multiple sensor tracking/fusion technique based on particle filtering (PF). As discussed in the most recent literature, particle filtering is defined as an emerging Monte-Carlo non-linear state estimation method. More specifically, in our proposed method each activated sensor transmits the received acoustic intensity and the target direction of arrival (DOA) to the sensor fusion center (a dedicated computing and storage platform, such as a micro-server). The fusion center uses each received DOA to generate individual estimates based on state partition technique as described later in the paper. In addition, a set of sensor weights are calculated based on the acoustic intensity received by sensors. Next, the weighted sum of the estimates are used as the proposal distribution in a particle filter for sensor fusion. This technique renders a more accurate proposal distribution and hence yields a more robust estimation of the target. Moreover, because of the improved proposal distribution, the new filter can achieve a given level of performance using fewer samples than the traditional bootstrap filter.