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: Digital filtering, Filtering (signal processing), Electronic filtering, Target detection, Detection and tracking algorithms, Signal attenuation, Signal detection, Sensor networks, Signal processing, Seismic sensors
This paper describes an approach which can reduce envelope delay effectively to improve traditional filter. In some
applications, traditional filter is applied to get the envelope of signal, but there is long envelope delay using traditional
filter which is not suitable for real time systems, such as ground moving target detection in wireless sensor network. This
paper presents a weighted filter approach to reduce envelope delay.
KEYWORDS: Fuzzy logic, Sensor networks, Acoustics, Binary data, Detection and tracking algorithms, Sensors, Signal processing, Signal to noise ratio, Interference (communication), Fusion energy
A new cascaded fuzzy classifier (CFC) is proposed to implement
ground-moving targets classification tasks locally at
sensor nodes in wireless sensor networks (WSN). The CFC is composed of three and two binary fuzzy classifiers (BFC)
respectively in seismic and acoustic signal channel in order to classify person, Light-wheeled (LW) Vehicle, and Heavywheeled
(HW) Vehicle in presence of environmental background noise. Base on the CFC, a new basic belief assignment
(bba) function is defined for each component BFC to give out a piece of evidence instead of a hard decision label. An
evidence generator is used to synthesize available evidences from BFCs into channel evidences and channel evidences
are further temporal-fused. Finally, acoustic-seismic modality fusion using Dempster-Shafer method is performed. Our
implementation gives significantly better performance than the implementation with majority-voting fusion method
through leave-one-out experiments.
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.
This paper presents one noisy image fusion scheme of pixel level based on fuzzy neural network. The simulation proves that the performance of fuzzy neural network is steady and convergent. By fuzzy pixel clustering of the competitive layer, the pixel level image fusion has been realized. The experiment and application verify that the presented method can fuse images with noise effectively.
Coupling theory is employed to analyze the coupling gain and a novel optical system is presented for image edge-enhancement by employing photorefractive two-wave coupling in BaTiO3 crystal, in which the ordinarily discarded background light is recycled as the pump source to amplify the signal light. Further, we demonstrate the principle of optical correlation and compare the discrimination capability of two kinds of correlators by computer simulation, in one of which input images are edge-enhanced and in the other the ones do not experience edge-enhancement. At last, we draw a conclusion that edge-enhancement preprocessing can improve discrimination capability effectively.
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