INS/GNSS整合系統可克服GNSS或INS獨立運行之缺點，進而提升更高精度的定位定向能力。INS使用整合後的加速度及角速度觀測量提供相對位置，GNSS則是提供初始值和位置更新至整合演算法中，降低INS隨時間而累積的誤差，倘若GNSS發生訊號失鎖，INS則可持續提供導航定位解。
在INS/GNSS整合演算法中，擴展式卡曼濾波器(EKF)最為常見，現今常見的整合架構為鬆耦合；在鬆耦合架構中，GNSS濾波器估算出GNSS位置及速度解，更新至INS濾波器中進行最後估算，產生導航定位解，鬆耦合架構簡單且容易實現，但GNSS濾波器在可視衛星低於4顆的情形下，無法提供的位置及速度解，導致INS獨立運作；相對來說，緊耦合僅使用單一濾波器整合INS和GNSS原始觀測量，故當只要可視衛星大於1顆以上，GNSS可持續提供位置及速度更新至整合演算法中。近期許多研究亦指出緊耦合相較於鬆耦合，可提供較穩定的導航定位解。
使用EKF緊耦合架構在都市或郊區中衛星訊號遮蔽嚴重之區域雖有幫助，卻無法降低不可靠的衛星訊號影響，而導致導航定位精度受損。適應性卡曼濾波器(AKF)最大概似法給定適當的權重和卡曼增益矩陣，進而調整觀測量協變方矩陣(R)，故AKF比起EKF可有效降低不可靠的衛星訊號影響。當進行觀測量更新時，本研究使用狀態更新序列給定權重，而後調整觀測量協變方矩陣。
在車載導航系統中，車體正常行駛於路面上不易產生上下震動或左右滑動情形，故車體速度僅有行進方向(X軸)之速度，而垂直於行進方向的Y軸和Z軸速度可約制為零，形成非諧和約制。本研究分別實現EKF和AKF緊耦合結合非諧和約制，透過實驗成果顯示，兩種卡曼濾波器結合非諧和約制相較於原始的卡曼濾波器可提升20%~30%的定位精度。特別是在GNSS訊號失鎖時，非諧和約制可以有效約制單獨運作的INS的飄移量。因此，AKF結合非諧和約制發展緊耦合INS/GNSS整合演算法，在GNSS訊號失鎖時，較能提供穩定的導航定位解，適合廣泛應用於都市或郊區中衛星訊號遮蔽嚴重之區域。

Integrated Inertial Navigation System (INS)/Global Navigation Satellite Systems (GNSS) systems can overcome the shortcoming of GNSS alone or INS alone so that provide superior performance. Due to the integrations any bias is accumulated resulting in divergence of the positions with time. INSs use integrated accelerations and angular rates to develop relative positions, thus their position estimates tend to diverge with time if left uncorrected. GNSS can be used to update the inertial estimates and minimize their drift over time. The inertial measurements can then be relied upon when the GNSS signals are blocked.
It is common to use an Extended Kalman Filter (EKF) to accomplish the data fusion. Loosely-coupled integration has the benefit of a simpler architecture which is easy to utilize in navigation systems. The position and velocity of the estimated by the GNSS KF is processed in the navigation Kalman filter to aid the INS, which is known as decentralized or cascaded filtering as well. However, the errors in the position and velocity information provided by the GNSS KF are time-correlated, which can cause a degradation in performance or even instability of the navigation Kalman filter, if these correlations are not considered by some means. In the case of incomplete constellations, i.e. less than four satellites in view, the output of the GNSS receiver has to be ignored completely, leaving the INS alone.
On the other hand, the tightly-coupled integration uses a single Kalman filter to integrate GNSS and INS measurements. In the TC integration, the GPS pseudo-range and delta-range measurements are processed directly in the main Kalman filter. For some authors, the aiding of the GNSS receiver tracking loops using velocity information provided by the INS is an essential characteristic of a tightly-coupled system, too. The primary advantage of this integration is that raw GNSS measurements can still be used to update the INS when less than four satellites are available.
Therefore, this study implements a tightly-coupled INS/GNSS integration scheme using the Adaptive Kalman Filter (AKF) as the core estimator by tuning the measurement covariance matrix (R) adaptively. The AKF is based on the maximum likelihood criterion for choosing the most appropriate weight and thus the Kalman gain factors. The conventional EKF implementation suffers uncertain results while the update measurement covariance matrix R does not meet the case. 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 measurement covariance matrices R, innovation-based adaptive estimation (IAE). The measurement covariance matrices (R) are adapted in the study when measurements update with time.
The integrated algorithm in this study is applied for land vehicle navigation and there are two non-holonomic constraints (NHC) available. Land vehicles will not jump off the ground or slid on the ground under normal condition. Using these constraints, the velocity of the vehicle in the plane perpendicular to the forward direction (x-axis) is almost zero. EKF and AKF based tightly-coupled scheme with NHC are implemented in the study.
To validate the performance of the KFs (EKF and AKF) based tightly-coupled INS/GPS integration scheme with NHC, simulation scenarios and field scenarios were conducted in the downtown area of Tainan city. The test platform was mounted on the top of a land vehicle. The results show the 20%~30% improvements in position errors when compared with the results without NHC. Therefore, NHC can be used as a stand-alone positioning tool during GNSS outages of 1 minute. AKF based tightly-coupled INS/GNSS integration scheme can provide more stable solutions combined with NHC during GNSS outages of 1 minute likewise.

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