Tracking and data association
Ant Colony Optimization
Artificial physics optimisation: a brief survey
International Journal of Bio-Inspired Computation
Social Cognitive Optimization Algorithm with Reactive Power Optimization of Power System
CASON '10 Proceedings of the 2010 International Conference on Computational Aspects of Social Networks
Expert Systems with Applications: An International Journal
An improved ant colony optimisation and its application on multicast routing problem
International Journal of Wireless and Mobile Computing
An improved multidimensional scaling localisation algorithm
International Journal of Wireless and Mobile Computing
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
Some assignment problems arising from multiple target tracking
Mathematical and Computer Modelling: An International Journal
Balanced data gathering strategy based on ant colony algorithm in WSNs
International Journal of Wireless and Mobile Computing
A multidimensional scaling localisation algorithm based on bacterial foraging optimisation
International Journal of Wireless and Mobile Computing
Hi-index | 0.00 |
Data association is an essential part of track maintenance in multiple target tracking, which can be solved by multidimensional assignment methods. When there is a need to solve the multidimensional assignment problem, the ant colony optimisation ACO algorithm stands out as it can solve combinatorial optimisation problem with excellent performance in acceptable CPU time. Here, each measurement is modelled as an ant, each track is modelled as a city, and the problem of data association is modelled as the food locating by ants. Thus, a novel data association based on an improved ant colony optimisation algorithm ACODA is proposed in this paper. The detailed corresponding relationship and theoretical analysis between basic ACO algorithm and the ACODA algorithm are given. Simulation results show that as the number of targets increases, the ACODA algorithm performs better than JPDA and NN, with superior performance both in computational time and accuracy.