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Title: 多模型非線性濾波演算法於超緊密GPS/INS導航之設計
The Ultra-Tight GPS/INS Navigation Algorithm Using Interacting Multiple Model Nonlinear Filtering
Authors: Chia-Wei Hu
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: 交互多模型;卡爾曼濾波器;GPS;GPS/INS;超緊密整合
IMM;Kalman filter;GPS;GPS/INS;Ultra-tight
Date: 2010
Issue Date: 2011-06-22T08:48:15Z
Abstract: 本論文提出一多模型非線性濾波演算法於超緊密整合(ultra-tight integration)導航之設計。超緊密整合也稱為深度整合(Deep integration),可以使接收機具有較寬的追蹤頻寬和較好的抑制雜訊能力,顯著提升GPS接收機的性能,在GPS訊號中斷時,INS在接收機的擷取和重新擷取的過程中,仍可以使用位置、速度等數據對碼迴路和載波迴路進行初始化和參數外部估計輔助,從而提升接收機追蹤迴路的性能。 超緊密耦聯的架構將整合的概念應用到接收機內部架構上有許多優點,如抗干擾與多路徑之能力,高動態性能提升,微弱訊號追蹤能力之提升,精度之提高,市區或室內定位能力之提升,縮短擷取時間,改進鎖相迴路的頻寬,達到更精確的都卜勒頻移和相位量測值…等等。 無跡卡爾曼濾波器(Unscented Kalman filter,簡稱UKF)係透過一確定性採樣之方法以獲得一組sigma點,無需對系統模型進行線性化的動作,避免了傳統EKF線性化之後引入的誤差,因此無須求取Jacobian矩陣。再利用交互多模型演算法決定載體機動運動狀態之系統雜訊參數大小。因此藉由UKF可有效地處理導航之非線性問題,利用交互多模型Interacting Multiple Model (IMM)估測架構,可進一步地解決系統雜訊之不確定性。經由測試顯示,當應用本文提出之IMMUKF 演算法於超緊密整合導航設計中,與較傳統之UKF比較,有較優越之性能表現。
In this thesis, application of the ultra-tight integration navigation algorithm using interacting multiple model (IMM) nonlinear filtering is studied for GPS/INS. The ultra-tight integration is also known as deep integration, which increases the receiver tracking bandwidth and suppresses noise, so as to promote GPS receiver performance. When the GPS signal losses, assistance of the aiding INS in the receiver's acquisition and re-acquisition process can still use the position, velocity on the delay lock loop (DLL) and phase lock loop (PLL) to promote the receiver’s tracking loop performance. Using the structure of ultra-tight in the receiver has many advantages, such as disturbance rejection and multi-path rejection, promoting high dynamic performance, tracking weak signals, improving the accuracy, urban or indoor positioning capability, shorten acquisition time, improved phase locked loop bandwidth, achieve a more accurate Doppler frequency shift and measurement of phase, etc. The unscented Kalman filter (UKF) employ a set of sigma points through deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. There is no need to evaluate the Jacobian matrix. The use of IMM, which describes a set of switching models, finally provides the suitable value of process noise covariance. Consequently, the resulting sensor fusion strategy can efficiently deal with the nonlinear problem in vehicle navigation. The proposed IMMUKF algorithm shows significant improvement in navigation estimation accuracy as compared to the UKF approaches.
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