全球定位系統（GPS）是目前普遍使用的導航工具，提供包括對許多應用的位置和速度。然而，對全球定位系統有一定的局限性，如輸出數據速率低，GPS信號在惡劣的環境中容易中斷等。為了克服這些缺點，現已被廣泛採用慣性導航系統（INS）和GPS兩種資料融合，來增加彼此效用。通常可使用卡爾曼濾波器（KF）作為最優估計的融合工具。然而， 卡爾曼濾波器存在些限制，如實用的功能和線性假設高斯分佈等。為了克服卡爾曼濾波器的限制，本研究探討集合卡爾曼濾波(EnKF)作為估計策略。
集合卡爾曼濾波是一種數據融合方法，該方法可以應用在非線性函數，包含非高斯的噪聲。為了評估所提出的方法，首先，實施不同的測試場景模擬。卡爾曼濾波、集合卡爾曼濾波和粒子濾波（PF）分別用於線性和非線性函數來作性能評估。應用卡爾曼濾波和不同數量成員的集合卡爾曼濾波（數量成員為集合卡爾曼濾波的輸入參數）來作現地測試。以台灣台南為現地測試地點，收集IMU和GPS數據。從兩個測試案例中，其中包括原來的GPS數據和模擬GPS中斷的測試。試驗結果證明具有足夠成員數的集合卡爾曼濾波，其性能優於卡爾曼濾波，集合卡爾曼濾波可克服卡爾曼濾波的限制，只是計算時間略為增長。

Global Positioning System (GPS) is now popularly used to provide navigation solutions including position and velocity for many applications. However, the GPS has some limitations such as low output data rate, GPS signal may be interrupted in GPS-hostile environments. To overcome these disadvantages, the combination of Inertial Navigation System (INS) and GPS is now widely applied. Commonly Kalman filter (KF) is used as an optimal estimator for data fusion. However, some restrictions of KF have been reported by researchers in the literature. Linear in applied functions and assuming Gaussian distribution of noises are major limitations of the KF. In order to overcome the limitation of KF, this research investigates the Ensemble KF (EnKF) as an alternate estimation strategy. EnKF is known as a data fusion method, which can be applied on non-linear functions and can account for non-Gaussian noise. To evaluate the proposed method, first, simulations with different testing scenarios were implemented. KF, EnKF and Particle filter (PF) are applied on linear and non-linear function to evaluate the performance of three estimation strategies. Then standard KF and EnKF with different number of members (an input parameter of EnKF) are applied to process field test INS/GPS data. The field test data including IMU and GPS data were collected in Tainan, Taiwan. The loosely coupled is applied for INS/GPS integration. Two testing scenarios including original GPS data and simulated GPS outages were tested. The processing time is also considered to evaluate the efficiency of the EnKF compared to KF.
The test result indicated that with a large-enough number of members, the performance of EnKF is slightly better than that of KF; however, the longer in computational time in that case is an limitation of EnKF compared to KF.

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