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A distributed nonlinear estimation method based on soft-data-constrained multimodel particle filtering and applicable to a number of distributed state estimation problems is proposed. This method needs only local data exchange among neighboring sensor nodes and thus provides enhanced reliability, scalability, and ease of deployment. To make the multimodel particle filtering work in a distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node and a consensus propagation-based distributed data aggregation scheme are used to dynamically reweight the particles' weights. The proposed method can recover from failure situations and is robust to noise, since it keeps the same population of particles and uses the aggregated global Gaussian to infer constraints. The constraints are enforced by adjusting particles' weights and assigning a higher mass to those closer to the global estimate represented by the nodes in the entire sensor network after each communication step. Each sensor node experiences gradual change; i.e., if a noise occurs in the system, the node, its neighbors, and consequently the overall network are less affected than with other approaches, and thus recover faster. The efficiency of the proposed method is verified through extensive simulations for a target tracking system which can process both soft and hard data in sensor networks.

In this paper we describe a fully integrated, real-time, miniaturized

We have developed a fully integrated, miniaturized embedded stereo vision system (MESVS-I) which fits into a tiny package of 5x5cm and consumes very low power (700mA@3.3V). The system consists of two small profile CMOS cameras, and a power efficient, dual-core embedded media processor, running at 600MHz per core. The stereo-matching engine performs sub-sampling, rectification, pre-processing using rank transform, correlation-based matching using three levels of recursion, L/R consistency check and post-processing. We have proposed a novel and efficient post-processing algorithm that removes outliers due to low-texture regions and depthdiscontinuities by combining the contributions from the variance map of the rectified image, disparity map, and the variance map of the disparity map. To further enhance the performance of the system, we have implemented a two staged pipelined-processing scheme that takes advantage of the dual-core architecture of the embedded processor, thereby achieving a processing speed of around 10fps for disparity maps.

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