In this paper, we propose a multiobjective optimization framework for the sensor selection problem in uncertain Wireless Sensor Networks (WSNs). The uncertainties of the WSNs result in a set of sensor observations with insufficient information about the target. We propose a novel mutual information upper bound (MIUB) based sensor selection scheme, which has low computational complexity, same
Index TermsTarget tracking, sensor selection, Fisher information, mutual information, information fusion, multiobjective optimization, wireless sensor networks.
A passive radar system that coopts digital broadcast (DAB/ DVB) signals is appealing but challenging: a single-antenna receiver system can only measure a bistatic range/range-rate; and, due to the energy-efficient multitransmitter commercial implementation, there is a confounding uncertainty as to which transmitter (illuminator) was responsible for a given "hit." In a companion paper we proposed some ways to track directly in native geographic coordinates. Here we suggest and compare two approaches for track initiation.
We present a target tracking system for a specific sort of passive radar, that using a Digital Audio/Video Broadcast (DAB/DVB) network for illuminators of opportunity. The system can measure bi-static range and range-rate. Angular information is assumed here unavailable. The DAB/DVB network operates in a single frequency mode; this means the same data stream is broadcast from multiple senders in the same frequency band. This supplies multiple measurements of each target using just one receiver, but introduces an additional ambiguity, as the signals from each sender are indistinguishable. This leads to a significant data association problem: as well as the usual target/measurement uncertainty there is additional "list" of illuminators that must be contended with.Our intention is to provide tracks directly in the geographic space, as opposed to a two-step procedure of formation of tracks in (bi-static) range and range-rate space to fuse these onto a map. We offer two solutions: one employing joint probabilistic data association (JPDA) based on an Extended Kalman Filter (EKF), and the other a particle filter. For the former, we explain a "super-target" approach to bring what might otherwise be a three-dimensional assignment list down to the two dimensions the JPDAF needs. The latter approach would seem prohibitive in computation even with these; as such, we discuss the use of a PMHT-like measurement model that greatly reduces the numerical load.
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