A De-correlation Algorithm Based on Chaos Adaptive Mutation PSO Optimization

Zhang Lu-you; Zhang Yong-shun; Yang Yun

doi:10.3724/SP.J.1146.2008.01188

Volume 31 Issue 8

Dec. 2010

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Zhang Lu-you, Zhang Yong-shun, Yang Yun. A De-correlation Algorithm Based on Chaos Adaptive Mutation PSO Optimization[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1825-1829. doi: 10.3724/SP.J.1146.2008.01188

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Zhang Lu-you, Zhang Yong-shun, Yang Yun. A De-correlation Algorithm Based on Chaos Adaptive Mutation PSO Optimization[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1825-1829. doi: 10.3724/SP.J.1146.2008.01188

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A De-correlation Algorithm Based on Chaos Adaptive Mutation PSO Optimization

doi: 10.3724/SP.J.1146.2008.01188

Received Date: 2008-09-22
Rev Recd Date: 2009-03-16
Publish Date: 2009-08-19

Abstract

Abstract

Meeting to the need of coherent signal direction-of-arrival estimation, applying particle swarm optimization algorithm, a Generalized Maximum Likelihood algorithm Based on Chaos Adaptive Mutation PSO (CAMPSOGML) optimization is proposed, the arrays geometry is unrestricted, furthermore, the number of sources resolved can be larger than the number of sensors, chaos initiation and adaptive mutation strategy is applied to basic PSO algorithm, the algorithm convergence speed is improved. The PSO algorithms defect which is easy to running into local optimum is overcome. Computer simulation results show that compared with the GML algorithm based on real genetic algorithm (GA) or PSO algorithm, CAMPSOGML algorithm is better at the aspects of convergence speed and estimation accuracy, this algorithm is a new and effective de-correlation DOA algorithm.

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References(1)

References

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