Multi-Sensor Multi-Target Data Association Algorithm Based on Swarm Intelligence

Abstract:

An swarm intelligence algorithm, particle swarm optimization (PSO) algorithm, is used in data association problem for multi-sensor multi-target data association. The association relation between measurements and targets is described by the likelihood function of filter innovation to establish the model of the optimal combination. Lagrange relaxation technology is used to reduce the combination to two dimensions firstly when solving the optimal combination problem, and then the improved PSO algorithm, which based on the cross and mutation rules, is used to obtain the optimal solution, and get the optimal association pairs for measurements and targets. The simulation shows the superiority of the method in accuracy and speed at last.

• Periodical: Applied Mechanics and Materials (Volumes 385-386)
• Pages: 675-680
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.385-386.675
• Online since: August 2013
• Authors: De Ping Yuan, Juan Yi Zheng, Hao Shan Shi
• Keywords: Crossover, Data Association, Mutation, Particle Swarm Optimization Algorithm

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