Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters (original) (raw)
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Bearings-only tracking with particle filtering for joint parameter learning and state estimation
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This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter is presented. The learning particle filter has shown accurate estimation results and improved accuracy compared with the ...
2006 14th European Signal Processing Conference, 2006
In this paper we describe an efficient real-time tracking algorithm for multiple manoeuvring targets using particle filters. We combine independent partition filters with a Markov Random Field motion model to enable efficient and accurate tracking for interacting targets. A Poisson model is also used to model both targets and clutter measurements, avoiding the data association difficulties associated with traditional tracking approaches. Moreover, we present a variable rate dynamical model in which the states change at different and unknown rates compared with the observation process, thereby being able to model parsimoniously the manoeuvring behaviour of an object even though only a single dynamical model is employed. Computer simulations demonstrate the potential of the proposed method for tracking multiple highly manoeuvrable targets in a hostile environment with high clutter density and low detection probability.
A variable neighborhood search particle filter for bearings-only target tracking
Computers & Operations Research, 2013
ABSTRACT In this paper a novel filtering procedure that uses a variant of the variable neighborhood search (VNS) algorithm for solving nonlinear global optimization problems is presented. The base of the new estimator is a particle filter enhanced by the VNS algorithm in resampling step. The VNS is used to mitigate degeneracy by iteratively moving weighted samples from starting positions into the parts of the state space where peaks and ridges of a posterior distribution are situated. For testing purposes, bearings-only tracking problem is used, with two static observers and two types of targets: non-maneuvering and maneuvering. Through numerous Monte Carlo simulations, we compared performance of the proposed filtering procedure with the performance of several standard estimation algorithms. The simulation results show that the algorithm mostly performed better than the other estimators used for comparison; it is robust and has fast initial convergence rate. Robustness to modeling errors of this filtering procedure is demonstrated through tracking of the maneuvering target. Moreover, in the paper it is shown that it is possible to combine the proposed algorithm with an interacted multiple model framework.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICLE FILTERING
2012
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model . Our proposed approach controls deformation of target's model. If deformation of target's model is larger than a predetermined threshold, then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF) . DDPF approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target' s model. However, DDPF approach updates target's model when the rotation or scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficiently and accurately.