Ant-based load balancing in telecommunications networks
Adaptive Behavior
Future Generation Computer Systems
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Particle filters for maneuvering target tracking problem
Signal Processing
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Location tracking in mobile ad hoc networks using particle filters
Journal of Discrete Algorithms
Image separation using particle filters
Digital Signal Processing
On a novel ACO-Estimator and its application to the Target Motion Analysis problem
Knowledge-Based Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A multi-rate multiple model track-before-detect particle filter
Mathematical and Computer Modelling: An International Journal
A new method based on ant colony optimization for the probability hypothesis density filter
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Expert Systems with Applications: An International Journal
A real-time moving ant estimator for bearings-only tracking
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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Standard ant colony optimization (ACO) algorithm is usually utilized to solve various hard combinatorial optimization problems. In this paper, however, the idea of the generic ACO is extended to the scope of recursive parameter estimation, i.e., ant estimator is first proposed and investigated to track target of interest. In the proposed ant estimator framework, some basic properties of particle filter (PF) and ant colony optimization (ACO) are inherited, and the ''fittest variables'' are determined by ants with probability decisions. In addition, the pheromone update strategy is also well defined in order to guide more ants to better solutions. Finally, two improved versions of the original ant estimator are investigated and applied to some benchmark target tracking problems. Numerical experiments demonstrate that these proposed ant estimators perform well, and moreover, each could deal with maneuvering target tracking problem without any auxiliary technique.