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A totally distributed and scalable support vector machine (DSVM) for classification in ad hoc wireless sensor networks (WSNs) is proposed. A sequential gradient ascent based algorithm is first introduced and adapted for distributed and parallel SVM training using only the local dataset for each classification agent. Then the global nonlinear classifier is(More)
A novel networked data-fusion method is developed for the target tracking in wireless sensor networks (WSNs). Specifically, this paper investigates data fusion scheme under the communication constraint between the fusion center and each sensor. Such a message constraint is motivated by the bandwidth limitation of the communication links, fusion center, and(More)
Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth. We consider the problem of target tracking in a WSN with quantized measurements in this paper. Attention is focused on the design of measurement quantizer with adaptive thresholds. Based(More)
To avoid both the inconsistency of the Kalman filter and the performance conservation of the covariance intersection (CI) in the case of unknown correlations, an internal ellipsoidal approximation (IEA) method is proposed to fuse the local estimations. A numerical example of three-state radar tracking system is presented to illustrate the implementation and(More)
This work provides a convergence analysis for the estimate error covariance of Kaiman filtering based on quantized measurement innovations (QIKF). By taking the quantization errors as random perturbations in observation system, an equivalent state-observation system is given. Accordingly, the quantitative Kaiman filter for the original system is equivalent(More)