由於INS/GPS（Inertial Navigation System/Global Positioning System）整合系統可以克服個別系統單獨操作的缺點並且提供更佳的精度表現，INS/GPS整合系統已經成為目前相當廣泛的應用。在資料整合方面常使用擴展式卡曼濾波器（Extended Kalman Filter ,EKF）演算法求解。一般而言，鬆耦合整合架構是現今最常見之整合架構。鬆耦合架構的優點為本身架構簡單，因此應用於導航系統是相當容易的。但是在不佳的衛星情況下，如可視衛星顆數小於4顆時，鬆耦合架構的GPS KF即無法提供輸出資訊，以致導航的卡曼濾波器中之INS必須獨立運作。相對地若使用緊耦合整合架構時，其將GPS及INS資料經由同一卡曼濾波器計算，此一結構之優點為即使可視衛星顆數小於4，仍然可以使用GPS的原始觀測量進行整合運算。對於在接受衛星訊號困難之地區，如都市或郊區地區，此架構對汽車導航是有相當大之幫助。但最近研究指出，使用EKF的緊耦合架構對於GPS原始觀測量的品質是相當敏感的。通常在都市或郊區地區經常發生反射的GPS訊號的情況，此情況會對導航系統產生影響。因此本研究將使用可以調整觀測量協變方矩陣R之適應性卡曼濾波器（Adaptive Kalman Filter, AKF）作為緊耦合架構之INS/GPS的核心估算器。適應性卡曼濾波器是透過最大概似值估計的方式調整觀測量適當的權重，而後獲得卡曼增益矩陣。使用AKF最大的優點為濾波器與先驗的統計估值無關，其觀測量協變方矩陣R是隨著時間改變的。在本研究中將使用innovation-based adaptive estimation (IAE)之方法作為調整觀測量權重之依據。在GPS接收儀也使用了兩種不同等級之儀器，藉由不同等級的GPS接收儀呈現出兩者觀測量品質不同對整合系統之影響。同時實驗結果與分析也將在本論文中展示。

Integrated INS/GPS systems have become a popular application, because the systems can overcome the shortcoming of stand-alone GPS or INS so that provide superior performance. It is common practice to use an EKF to accomplish the data fusion. The most common integration scheme used today is loosely coupled (LC) integration. This kind of integration has the benefit of a simpler architecture which is easy to utilize in navigation systems. However, in the case of incomplete constellations, i.e. less than four satellites in view, the output of the GPS receiver has to be ignored completely, leaving the INS unaided. On the other hand, the tightly coupled integration uses a single Kalman filter to integrate GPS and IMU measurements. In the TC integration, the GPS pseudo-range and delta-range measurements are processed directly in the main Kalman filter. The primary advantage of this integration is that raw GPS measurements can still be used to update the INS when less than four satellites are available. This is of special benefit in a hostile environment such as downtown areas where the reception of the satellite signals is difficult due to obstruction when the vehicle navigates in urban or suburban area. However, according recent studies, an EKF based tightly-coupled architecture is sensitive the quality of GPS raw measurements. This scenario usually takes place in urban and suburban areas because of the impact of reflected GPS measurements. Therefore, this study applies the Adaptive Kalman Filter (AKF) as the core estimator of a tightly-coupled INS/GPS integration scheme by tuning the measurement noise matrix R adaptively. The Adaptive Kalman Filter is based on the maximum likelihood criterion for choosing the most appropriate weight and thus the Kalman gain factors. The primary advantage of AKF is that the filter has less relationship with the priori statistical information because the R varies with time. The innovation sequence in the study is used to derive the measurement weights through the covariance matrix R, innovation-based adaptive estimation (IAE). In the aspect of GPS receiver, two different accuracy levels of GPS receivers are used in this study to present the influence of the measurements’ qualities. The results and analyses of the field test are shown in this thesis.

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