A TOA/ AOA Location Algorithm in NLOS Environment

Mao Yong-yi; Li Ming-yuan; Zhang Bao-jun

doi:10.3724/SP.J.1146.2007.01157

Volume 31 Issue 1

Dec. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(1): 37-40

Mao Yong-yi, Li Ming-yuan, Zhang Bao-jun. A TOA/ AOA Location Algorithm in NLOS Environment[J]. Journal of Electronics & Information Technology, 2009, 31(1): 37-40. doi: 10.3724/SP.J.1146.2007.01157

Citation:

Mao Yong-yi, Li Ming-yuan, Zhang Bao-jun. A TOA/ AOA Location Algorithm in NLOS Environment[J]. Journal of Electronics & Information Technology, 2009, 31(1): 37-40. doi: 10.3724/SP.J.1146.2007.01157

Mao Yong-yi, Li Ming-yuan, Zhang Bao-jun. A TOA/ AOA Location Algorithm in NLOS Environment[J]. Journal of Electronics & Information Technology, 2009, 31(1): 37-40. doi: 10.3724/SP.J.1146.2007.01157

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A TOA/ AOA Location Algorithm in NLOS Environment

doi: 10.3724/SP.J.1146.2007.01157

Mao Yong-yi^{①②③;李明远},
Li Ming-yuan,
Zhang Bao-jun

Received Date: 2007-07-23
Rev Recd Date: 2008-01-10
Publish Date: 2009-01-19

Abstract

Abstract

In order to mitigate the effect of NLOS propagation, based on the Geometry Based Single- Bounced (GBSB)statistical model, a TOA/AOA location algorithm based on the RBF neural network is proposed. The fast study and non-linear approach capacity of the neural network is made use of to correct the error of NLOS propagation, then the position is calculated by Least-Square (LS) algorithm to improve the location[0] accuracy. The simulation results indicate that the location accuracy is significantly improved and the performance of this algorithm is better than that of Chan algorithm, Taylor algorithm and LS algorithm in NLOS environment.

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References(1)

References

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