A Real-Time Moving Ant Estimator for Bearings-Only Tracking.
ABSTRACT A real-time moving ant estimator (RMAE) is developed for the bearings-only target tracking, in which ants located at their
individual current state utilize the normalized weight and pheromone value to select the one-step prediction state and the
dynamic moving velocity of each ant is depended directly on the normalized weights between two states. Besides this, two pheromone
update strategy is implemented. Numerical simulations indicate that the RMAE could estimate adaptively the state of maneuvering
or non-maneuvering target, and real-time requirement is superior to the moving ant estimator (MAE).
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ABSTRACT: In this article we propose a new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle filtering, and the efficiency of the Monte Carlo sampling is improved by using Rao-Blackwellization.Information Fusion. 01/2007;
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ABSTRACT: The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other methods are required. Methods known generically as `particle filters' are considered. These include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters represent the posterior distribution of the state variables by a system of particles which evolves and adapts recursively as new information becomes available. In practice, large numbers of particles may be required to provide adequate approximations and for certain applications, after a sequence of updates, the particle system will often collapse to a single point. A method of monitoring the efficiency of these filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation. This is illustrated with the classic bearings-only tracking problemIEE Proceedings - Radar Sonar and Navigation 03/1999; · 0.55 Impact Factor
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ABSTRACT: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 02/1996; 26(1):29-41. · 3.24 Impact Factor