應用INS/GPS於管線快速動態定位之可行性研究

Abstract

GPS 與INS結合的應用在國外已有相當多的成功實例，絕大多數應用於軍事及導航，隨著GPS技術的進步，在短距離基線中，動態（Kinematic）GPS或即時動態（Real-time kinematic, RTK）的量測技術已經能達到公分級的精度，而INS礙於現今的技術，雖然同樣有相當高精度的慣性量測器元件(Inertial Measurement Unit, IMU)，但相對的其價格非常昂貴，為了在使用低價位INS的情況下能將這兩種技術結合，當GPS系統訊號接收不良時能依靠INS繼續定位，使其達到相輔相成且不錯的精度成果。 本文共模擬了兩次的INS資料以及收集OSU製圖中心的原始實測資料作研究；結合的方式是以分散式卡爾曼濾波(Decentralized Filter)，配合15個誤差參數的INS誤差模型。這種INS/GPS的分散式卡爾曼濾波結合方式多屬於資料的後處理，但在本文的研究中對此種方式加以改善，促使其在短時間內完成自動調整分散式卡爾曼濾波器中的雜訊程序協變方矩陣，再將所得到之參數套用在即時的資料，提供一個即時性處理的概念，以達到即時定位的效果。 本研究結果顯示，分割資料量並自動調整分散式卡爾曼濾波器中的雜訊程序協變方矩陣，使其達到最佳的結合狀態，於OSU實測資料中，中斷GPS觀測資料，20秒內INS定位之位置解與KGPS位置解相差為35公分內，但運算的時間仍然過長。另外，實驗發現，INS/GPS系統於有GPS訊號時和無GPS訊號時將其套用不同的雜訊程序協變方矩陣，比只套用一種雜訊程序協變方矩陣更有助於符合度的提升。

There are many successful examples in using of INS/GPS in the world, mostly apply to military and navigation. In short baseline, the precision of Kinematic GPS and Real-time kinematic (RTK) have already reached to the level of centimeter. The price of high precision INS instrument is expensive. To use cheaper INS instrument for positioning when GPS surveying is not work. So the integrating GPS with INS avoid the defects of each system, and raise the accuracy of positioning results. In this study, two simulative INS data and data collected from the Center for Mapping at the Ohio State University is applied for positioning. In addition, the decentralized Kalman Filter and 15 parameters INS model for data processing is used. Moreover, the data processing procedure of the Kalman Filter is modified to get automatic and real time positioning object. From the results, it was found that using divide data and adjust the Kalman Filter system noise covariance matrix automatically can combine both system into the optimum state. During the intentional gaps of GPS observation of the OSU practical data, the difference of INS position and KGPS solution reaches 35cm within 20 seconds. But it use more time to compute. On the other hand, use two different system noise covariance matrix which has GPS signal or not, the result batter then use a system noise covariance matrix with GPS signal or not.