Abstract
Energy efficient collaborative target tracking in a wireless sensor network (WSN) is considered. It is assumed that the distance estimates of range sensors are contaminated by distance-dependent multiplicative observation noises. The nonlinear measurement model leads to the application of a generalized unscented Kalman filtering (GUKF) tracking algorithm. Energy efficient operation is achieved by imposing an energy balance criterion to select a subset of sensors near the target to participate in collaborative tracking without compromising tracking performance. This is formulated as a multiobjective constrained optimization problem that minimizes both the state covariance of the GUKF algorithm and the variance of on-board residue energy of sensor nodes within the detection range of the target. An efficient, distributed, polynomial time heuristic algorithm that achieves a performance close to the optimal solution is proposed. Extended simulation results indicate that this proposed joint scheduling and tracking algorithm is capable of delivering desired tracking performance while significantly extending the WSN lifespan.
Highlights
Wireless sensor network consists of a large number of low cost sensor nodes that sense signals of environment or specific target/event and aggregate sensing data via wireless channels
Target tracking can be formulated as a sequential Bayesian estimation problem which is often realized in the forms of the Kalman filter (KF) [5] or its variants [6,7,8,9,10,11,12,13]
We focus on the range estimation sensor that returns a measurement of distance from sensor to target when such a distance is within the detection range of the sensor
Summary
Wireless sensor network consists of a large number of low cost sensor nodes that sense signals of environment or specific target/event and aggregate sensing data via wireless channels. Target tracking can be formulated as a sequential Bayesian estimation problem which is often realized in the forms of the Kalman filter (KF) [5] or its variants [6,7,8,9,10,11,12,13]. These algorithms assume the target movement can be described by a dynamic system model where the state (location, speed, etc.) can be observed via a measurement model. An EKF algorithm [17] using a distance-dependent multiplicative measurement noise model has been reported and a generalized unscented Kalman filter (GUKF) algorithm has been derived
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