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| A TOA/ AOA Location Algorithm in NLOS Environment |
| Mao Yong-yi①②③; Li Ming-yuan④; Zhang Bao-jun③ |
| ①National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, China;②Graduate University of the Chinese Academy of Sciences, Beijing 100039, China; ③Dept. of Electronic and Information, Xi’an University of Post and Telecommunications, Xi’an 710061, China;④School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China |
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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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Received: 23 July 2007
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2009, Vol. 31 
