整合GNSS與INS量測資訊的地平面運動軌跡估測

Abstract

本論文提出了一套以卡爾曼濾波器整合鬆散耦合之GNSS/INS系統應用於軌跡紀錄中，離線適應性估測卡爾曼濾波器量測雜訊的方法。衛星定位系統中，環境會影響衛星訊號的接收，使量測雜訊的標準差隨環境與時間變化，因此估測量測雜訊相當重要。在以往提出的線上估測方法中，利用移動平均法，對過去視窗區間的卡爾曼創新序列估測當下的量測雜訊，僅在雜訊標準差隨時間變化不大的環境有較佳的結果，例如行駛於無遮蔽的高速公路上；但無法應付雜訊標準差變化快的環境，例如行駛於眾多高樓的市區中，接收衛星的數量會受高樓遮蔽影響，造成雜訊的標準差變動程度增加。 本論文利用可調維度的Savitzky-Golay濾波器，針對以當下取樣點為視窗區間中心，延遲取樣後所得到卡爾曼創新序列資訊，以可調變維度的局部多項式回歸法做量測雜訊的估測，並以統計學F檢定法適應性找出相對應維度，效果優於未經延遲取樣、僅參考過去區間資訊且固定維度的移動平均法，能估測雜訊標準差變化環境下的量測雜訊，在運動軌跡紀錄的結果優於以往線上估測的方法。模擬結果本方法的水平誤差為0.3m，較其它方法有約1倍精準度的提升。

This thesis proposes a new method which estimates the Kalman filter’s measurement noise covariance matrix in a loosely coupled GNSS/INS sensor fusion system. The signals received from the satellites are heavily influenced by the environmental conditions, and will cause the noise variance to change dramatically. Thus, estimating the measurement noise to improve the fusion quality is very important. Most of the proposed methods use the moving average method to estimate the measurement noise online from the innovation sequence of the Kalman filter in the window interval. However, those methods can only have a good performance in a quasi-static environment, e.g., freeway where the signals are rarely blocked. In dynamic environments such as the urban area, the satellite signals are often blocked by the buildings, resulting in a large range of the noise variance. This thesis proposes to use Savitzky-Golay filter with degree adaptation (ADSG) to estimate the measurement noise. The ADSG filter uses local polynomial regression method to fit the delayed-sample innovation sequence in the window interval, and adjust the polynomial order by the statistics F-test. Experiments show that the proposed method’s horizontal position error is about 0.3m, which is better than several existing methods.