Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy
dissipation are critical requirements to save the limited resource of sensor nodes. A new energy efficient collaborative
target tracking algorithm via particle filtering (PF) is presented. Assuming the network infrastructure is cluster-based,
collaborative scheme is implemented through passing sensing and computation operations from one active cluster to
another and an event driven cluster reforming approach is also proposed for evening energy consumption distribution. At
each time step, measurements from three sensors are chosen at the current active cluster head to estimate and predict the
target motion and the results are propagated among cluster heads to the sink. In order to save the communication and
computation resource, we present a new particle filter algorithm called Gaussian Rao-Blackwellised Particle Filter
(GRBPF), which approximate the posterior distributions by Gaussians and only the mean and covariance of the
Gaussians need to be communicated among cluster heads when target enter another cluster. The GRBPF algorithm is
also more computation efficient than generic PF by dropping the resampling step. In the simulation comparison, a target
moves through the sensor network field and is tracked by both generic PF and the GRBPF algorithm using our proposed
collaborative scheme. The results show that the latter works very well for target tracking in wireless sensor networks and
the total communication burden is substantially reduced, so as to prolong the lifetime of wireless sensor networks.
KEYWORDS: Sensors, Acoustics, Detection and tracking algorithms, Sensor networks, Head, Target detection, Particle filters, Particles, Systems modeling, Monte Carlo methods
An energy-aware, collaborative target tracking algorithm is proposed for ad-hoc wireless sensor networks. At every time
step, current measurements from four sensors are chosen for target motion estimation and prediction. The algorithm is
implemented distributively by passing sensing and computation operations from a subset of sensors to another. A robust
multimodel Rao-Blackwellised particle filter algorithm is presented for tracking high maneuvering ground target in the
sensor field. Not only is the proposed algorithm more computation efficient than generic particle filter for high dimension
nonlinear and non-Gaussian estimation problems, but also it can tackle the target's maneuver perfectly by
stratified particles sampling from a set of system models. In the simulation comparison, a high maneuvering target
moves through an acoustic sensor network field. The target is tracked by both generic PF and the multimodel RBPF
algorithms. The results show that our approach has great performance improvements, especially when the target is making maneuver.
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