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Neural Networks-based Dynamic Compensation for Improving Filtering Estimation Accuracy
|Authors: ||Chia-Hsin Lin|
|Contributors: ||NTOU:Department of Communications Navigation and Control Engineering|
Dynamic compensation;Kalman filter;GPS;neural network
|Issue Date: ||2011-06-27T07:40:46Z
|Abstract: ||傳統卡爾曼濾波器(Kalman filter)估測目標的運動狀態，必須事先知道環境中雜訊的統計量及確切的系統狀態描述，才能使卡爾曼濾波器得以最佳化。一個以等速或PV模型描述的系統程序，卡爾曼濾波器足以精確的追蹤穩定速度之目標，然而對於突然具有加速度運動之機動目標，一般之追蹤技術皆無法測知進而加以補償。基於卡爾曼濾波器的限制，將利用類神經網路來補償卡爾曼濾波器於一個等速運動之目標，突然具有加速度運動之機動目標，而無法繼續鎖定追蹤，造成脫鎖(miss-tracking)之誤差補償修正，本論文使用時間延遲類神經網路(TDNN)與模糊化類神經網路(ANFIS)輔助卡爾曼濾波器於動態誤差補償上有不錯的效能。卡爾曼濾波器結合GPS求解定位，以模擬方式來驗證此方法的可行性，並與傳統的卡爾曼濾波器進行比較。 關鍵字：動態補償、卡爾曼濾波器、全球定位系統、類神經網路|
The Kalman filtering theory plays an important role in the fields of navigation filter designs. For obtaining optimal (in the viewpoint of minimum mean square error) estimate of the system state vector, the designers are required to have exact knowledge on both dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero-mean Gaussian white noises. The standard GPS Kalman filter, when employing the constant velocity (CV) or Position-Velocity (PV) process model, will be able to track a target with constant speed adequately. However, when an abrupt maneuver occurs or when the acceleration of a maneuvering vehicle can not be ignored, the filtering solution will be very poor or even diverge. To avoid the limitation of the Kalman filter, the neural network can be incorporated into the filtering mechanism as dynamic model corrector. As a dynamic model corrector, neural network will identify the real-time nonlinear dynamics errors when the modeling of uncertainty is considered. The partially unknown part of the dynamics is identified by the neural network and the modeling error will be compensated. In this thesis, the Time-Delay Neural Networks and Adaptive Network-Based Fuzzy Inference System approaches will be employed for identifying the dynamics errors so as to aid the GPS Kalman filter and therefore reduce the tracking error during substantial maneuvering. Simulation is conducted and a comparative evaluation based on the neural network aided Kalman filter and conventional Kalman filter is provided. KEYWORDS 1.Dynamic compensation 2.Kalman filter 3.neural network 4.GPS
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