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We present a distributed particle filter for bearings-only tracking of a target moving on the surface of a sphere, such as Earth. The proposed filter accounts for the curvature of the surface in the measurement model for more robust performance. In addition, a linearization of the likelihood function significantly reduces the communication overhead. Simulations demonstrate that the proposed distributed...
In our previous work, we compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the angle-only filtering (AOF) problem in 3D using Cartesian coordinates and modified spherical coordinates (MSC) for the relative state vector. We found that the UKF-MSC and EKF-MSC had the best performance in accuracy, the UKF-MSC being slightly better...
The constraint sufficient statistics distributed particle filter is a novel and effective solution for bearings-only single target tracking. The algorithm achieves a significant reduction in communication overhead by factorizing the likelihood function without suffering a major decrease in accuracy. However, the algorithm has some limitations which we discuss and explore in this paper. In particular,...
We consider the problem of localizing and tracking an acoustic noise source under water using bearing measurements taken from a small collection of acoustic sensors. Nodes must cooperate in order to improve their estimates and overcome significant noise levels and spurious measurements from clutter. However the underwater communication channel is highly unreliable, which makes coordination challenging...
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