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We present particle filtering algorithms for tracking a single target using data from binary sensors. The sensors transmit signals that identify them to a central unit if the target is in their neighborhood; otherwise they do not transmit anything. The central unit uses a model for the target movement in the sensor field and estimates the target's trajectory, velocity, and power using the received...
Target tracking in wireless sensor networks with constrained resources is a challenging problem. In this paper we consider scenarios where sensors sense an object of interest and process the received measurements using adaptive thresholds to obtain quantized data in the form of two levels. The data are quantized to address resource constraints in sensor networks. The processed data are then sent to...
In this paper we address the problem of tracking of multiple targets in a wireless sensor network using particle filtering. This methodology approximates the probability distributions of the objects of interest by using random measures composed of particles and associated weights. An important challenge of the resulting algorithms is the need for very large number of particles when the dimensions...
In this paper, we consider tracking time-varying number of targets which move along a two-dimensional area monitored by a network of wireless sensors. We propose a novel fusion algorithm based on particle filtering that accounts for both detection of the number of active targets in the field and estimation of their positions and velocities. The method uses measurements collected by acoustic sensors,...
Standard particle filtering (SPF) schemes rely on the availability of probability distributions of the state and observation noises involved in the dynamic state space model. Cost reference particle filtering (CRPF) techniques have proven to be a viable and robust alternative in situations when the probability distributions of these noise processes are unknown. In this paper, we propose two new CRPF...
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