基於整合型卡爾曼濾波器之混合式無線追蹤定位演算法

Abstract

近年來為行動終端裝置所提供的定位追蹤和量測技術逐漸引起越來越 多的關注；隨著越來越多的定位系統投入商業運轉，環境中充滿了許多原理互異的定位量測資訊。已有許多不同的技巧經過研究或進一步合併使用，例如最小平方法結合卡爾曼濾波器進行定位和追蹤。本篇論文提出一個基於整合型卡爾曼濾波器的混合式無線追蹤定位演算法(HUKT)，統整來自到達時間和到達時間差這兩種相異定位系統的量測資訊以提供精準的定位追蹤服務，而伴隨著定位計算的非線性特性則以一個額外的狀態變量被整合成卡爾曼濾波器的內部參數之一。與現有的架構比較，模擬分析結果顯示本篇論文提出的 HUKT演算法可以更加提高行動定位追蹤的準確度，並且在量測訊號源不足的情況下依然能有不錯的表現。

Location estimation and tracking for the mobile stations have attracted a significant amount of attention in recent years. Moreover, different types of signal sources are considered available to provide the measurement inputs for location estimation and tracking. Various techniques have been studied and combined for location tracking, e.g. the least square methods for location estimation associated with the Kalman filters for location tracking. In this thesis, a hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both the time-of-arrival (TOA) and time-difference-of-arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation in order to consider the nonlinear behavior for wireless location estimation. Comparing with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, especially under the environments with insufficient number of signal sources in a single signal path.