Abstract
In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.
Highlights
Wireless localization which can determine the position of mobile station (MS) in wireless network has received considerable attention over the past years, especially the application of the location based services (LBSs)
We investigate hybrid time of arrival (TOA)/angle of arrival (AOA) non-line of sight (NLOS) identification with a residual test and the weighting localization approach to minimize the effect of NLOS error
We carry out some simulations to prove the performance of the proposed NLOS identification and probability weighting localization algorithm
Summary
Wireless localization which can determine the position of mobile station (MS) in wireless network has received considerable attention over the past years, especially the application of the location based services (LBSs). Its main idea is to divide the range measurements into different combinations, each combination obtains the intermediate position estimate of MS with nonlinear least square (NLS) algorithm, and the final position estimate of MS is weighted by the intermediate position estimate and the corresponding normalized residual. It does not discard any combination which may be corrupted greatly by the NLOS propagation and has high computational complexity when the number of involved BSs is big.
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