目前全球定位系統(Global Positioning System, GPS)已經發展成熟，且已被廣泛地應用在車輛導航上。當車子行駛於市區中會受到高樓、隧道等之遮蔽與多路徑效應的影響，導致訊號中斷。為了解決這個問題，以慣性導航系統(Inertial Navigation System, INS)與GPS的整合式導航技術已成為導航系統的主要方向。一般GPS/INS整合導航系統的作法是使用擴展式卡爾曼濾波器(Extended Kalman Filter, EKF)根據GPS與INS的量測量，並搭配導航演算法來推估載具的位置、速度與姿態。然而由於車輛之動態行為隨著時間有極高之變化與複雜性，若如一般導航演算法所使用之卡爾曼濾波器單一模型之架構，很難將載具之行為作完整的描述。因此，本論文發展了交互多模型(Interacting Multiple Model, IMM)架構車輛導航演算法。交互多模型考慮數個模型來表示系統之行為，藉由調整各模型之間比例，使其能針對情況調整出適當之模型，以確保其定位精度。

Nowadays, the Global Positioning System (GPS) has been widely used for vehicle navigation. However, GPS cannot provide an uninterrupted positioning solution when vehicle drives in areas such as urban canyons or tunnels, because the system suffers from signal blockage and multipath effects. In order to deal with these problems, GPS/Inertial Navigation System (INS) integrated navigation technique has become the main direction to facilitate a continuous positioning solution. A GPS/INS integrated navigation system typically utilizes an Extended Kalman Filter (EKF) based navigation algorithm to estimate vehicle position, velocity, and attitude based on GPS and INS measurements. However, as the dynamic state of vehicles is highly variable and complex over time, utilizing single EKF model is not sufficient enough to capture the movement of vehicles. Therefore, this thesis develops an Interacting Multiple Model (IMM) positioning algorithm. IMM approach considers that the system follows one of a finite number of different models, the appropriate state estimates are combined according to the ratio adjustment between models to ensure the positioning accuracy.

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