【摘要】：復雜的非視距傳播(NLOS)環(huán)境帶來的誤差會嚴重影響定位算法的精度和穩(wěn)健性,單純的迭代優(yōu)化算法面對大量的NLOS誤差表現(xiàn)得并不理想。針對經(jīng)典的TOA算法對非視距傳播誤差的敏感性問題,引入Bootstrap抽樣及蒙特卡羅思想,提出了一種改進的TOA算法。利用隨機化的思想對誤差分布進行更加精確的識別,并通過隨機模擬還原直線傳播數(shù)據(jù),結(jié)合牛頓迭代法對移動終端進行精確定位。實證分析證明,這種改進的TOA算法具有更高的精確性和穩(wěn)健性。
[Abstract]:The error caused by the complex non-line-of-sight propagation (NLOS) environment will seriously affect the accuracy and robustness of the location algorithm. The simple iterative optimization algorithm is not ideal in the face of a large number of NLOS errors. In order to solve the sensitivity of classical TOA algorithm to non-line-of-sight propagation error, an improved TOA algorithm is proposed by introducing Bootstrap sampling and Monte Carlo ideas. The error distribution is identified more accurately by using the idea of randomization, and the straight line propagation data is reduced by random simulation, and the mobile terminal is accurately located by Newton iterative method. The empirical analysis shows that the improved TOA algorithm has higher accuracy and robustness.
【作者單位】： 山東科技大學數(shù)學與系統(tǒng)科學學院;
【基金】：全國統(tǒng)計科學研究重點項目《網(wǎng)絡交易價格的大數(shù)據(jù)統(tǒng)計與數(shù)據(jù)挖掘方法研究》(2014LZ41)
【分類號】：TN92

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本文編號：2522015