增強型無線定位性能的方法用於市區的高資訊率行動通訊環境

An enhanced method for improving wireless positioning performance in urban high data-rate mobile communications

陳柏元(Bo-Yuan Chen)

指導教授 ： 何天讚

中原大學/工學院/電機工程研究所/碩士(2014年)

https://doi.org/10.6840/cycu201400331

摘要

本論文提出了一種利用交互多模型擴展卡爾曼濾波器(IMM-EKF)模糊推論自適應系統，對目標物在惡劣的衰減情況下做更精確的移動定位。本論文提出的方法不同於以往的方法，對於環境中的非直視距(non-line of sight, NLOS)誤差用不同的方式處理。由於市區高資訊率無線通訊環境中，非直視距離誤差受到大規模或小規模的多路徑衰減的干擾而對定位準確度有很大影響。因此論文中將NLOS誤差考慮為一隨機變數有未知的分佈或未知的固定正值。這裡使用訊號傳播到達時間(time of arrival, TOA)和接收訊號強度(received signal strength, RSS)的數據融合以及對NLOS模型細分並加入偏移量消除NLOS傳播中所帶來的影響，再利用模糊控制理論調整過程干擾的協方差矩陣。經模擬證明，我們所提出的方法在市區高資訊率無線通訊環境中能有更好的定位準確度。

關鍵字

接收訊號強度 ； 到達時間 ； 移動定位 ； 交互多模型 ； 擴展卡爾曼濾波器 ； 數據融合 ； 非直視傳播

並列摘要

This thesis presents an enhanced interacting multiple-model Extended Kalman filter plus fuzzy inference (enhanced IMM-EKF + fuzzy) to effectively improve wireless location estimation performance in urban high data-rate mobile communication. Received measurements result from data fusion of range and signal power. The NLOS bias is considered as a random variable with an unknown distribution or an unknown positive constant. To limit the effect of the NLOS bias, received measurements are processed as validated measurements based on the physic-geometry of an urban environment. Subsequently, the proposed method is developed based on validated measurements and partitioned NLOS models. As well, fuzzy inferences are incorporated into the tuning of process noise variances to improve the performance of the proposed algorithm. Simulation results show that the proposed method can achieve remarkable performance improvement over several existing methods.

並列關鍵字

non-line of sight (NLOS) ； data fusion ； Extended Kalman filter ； mobile location ； interacting multiple-model

參考文獻

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