Intelligently Tuned Wavelet Parameters for GPS/INS Error Estimation
Intelligently Tuned Wavelet Parameters for GPS/INS Error Estimation作者机构:Computer Systems Research Group Department of Computer and Communication Systems University Putra Malaysia 43400 UPM-Serdang Malaysia
出 版 物:《International Journal of Automation and computing》 (国际自动化与计算杂志(英文版))
年 卷 期:2011年第8卷第4期
页 面:411-420页
核心收录:
学科分类:08[工学] 080401[工学-精密仪器及机械] 081105[工学-导航、制导与控制] 0804[工学-仪器科学与技术] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程]
主 题:Global positioning system (GPS) inertial navigation system (INS) wavelet multi-resolution analysis (WMRA) genetic algorithm (GA) inertial measurement unit (IMU) level of decomposition (LOD) threshold selection rule (TSR).
摘 要:This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule (TSR), and the optimum level of decomposition (LOD). This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.