全球定位系統(GPS)及慣性導航系統(INS)的整合式定位及定向系統由於能克服獨立運作系統的限制，因此成為了更具有優越成果的整合式定位定向系統。

現今GPS/INS整合式架構公認理想的最佳化估算工具為卡曼濾波器，但近年來陸續有研究學者提出它的限制及潛在的缺點。無可避免地，發展替代式的GPS/INS整合式架構受到越來越多的注目。發展替代整合式演算法之目標即是在減少卡曼濾波器限制因子以增進在GPS訊號遮蔽時的定位精度與姿態精度。

的確，由於傳統卡曼濾波器方法的限制驅使，導致在動態定位定向應用裡併入人工智慧技術去解決問題是近年來相關領域之熱門研究課題；但單純使用人工智慧的技術之INS/GPS替代演算法亦有其限制。本研究提出合併卡曼濾波器與類神經網路技術的混合式ANN-KF整合架構和合併類神經與平滑器的混合式ANN-RTS整合架構。由於卡曼濾波器有其限制因子存在，同時類神經亦有計算量龐大、不能在線應用等缺點，故希望合併兩者後能擷取兩者之優點並克服彼此缺點來組成一個更優越的整合系統。

由實驗結果得知，本文提出的類神經混合架構的成果確實比單一卡曼濾波器、RTS平滑器的成果更顯著。透過使用ANN-RTS架構使用者可以利用較低廉的方式透過演算法性能提升的方式將MEMS 及中精度戰術等級的INS/GPS整合式系統之精度提高一個等級，故該架構可以應用在高精度的定位與定向系統研究。

INS/GPS integrated positioning and orientation systems provide an enhanced system that has superior performance in comparison with either system operating in stand-alone mode as it can overcome each of their limitations.

The Kalman filter approach has been widely recognized as the standard optimal estimation tool for current INS/GPS integration scheme, however, it does have limitations, which have been reported by several researchers. Consequently, the development of alternative INS/GPS integration scheme has received more attention and the common goal is to reduce the impact of remaining limiting factors and improve the positioning accuracy and attitude accuracy during GPS outage.

Indeed, the incorporation of Artificial Intelligent techniques in the kinematic positioning and orientation applications has been gaining more attention in navigation related communities due to the limitations of conventional Kalman filter approach. The goal of applying artificial intelligent technologies is to provide intelligence and robustness in the complex and uncertain systems. Some AI based INS/GPS integration algorithms have been verified as effective solutions to overcome the limiting factors of Kalman filter; however, they have their limitations.

Therefore, two hybrid schemes consisting of artificial neural networks and the Kalman filtering (ANN-KF) as well as RTS backward smoother (ANN-RTS) have been proposed to overcome the limitations of Kalman filtering and learning issues of Artificial neural networks based alternative INS/GPS integration schemes and improve the performance of INS/GPS integrated systems successfully.

The results presented in this research indicate that the proposed ANN-KF and ANN-RTS hybrid INS/GPS integration schemes can provide superior performance in term of positioning and orientation accuracies comparing to Kalman filter and RTS smoother, respectively. Among the hybrid schemes proposed, the ANN-RTS INS/GPS integration scheme can be applied for positioning and orientation applications with high accuracy requirements because it can improve the performance of low cost MEMS based and low end tactical based INS/GPS integrated systems through the modification of algorithm in software level with less expanses.

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