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）-b...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.展开更多
基金supported in part by Graduate School of Studies through the Graduate Research Fellowship (GRF) sponsored by University Putra Malaysia
文摘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.
文摘针对输电线路杆塔需要在容易下陷的土壤上进行液压提升作业前进行调平控制的情况,对受压土壤下陷特性进行研究,得出在液压杆对土壤施压的情况下,土壤下陷程度与施压程度是线性关系。在此基础上利用惯性测量单元(Inertial measurement unit,IMU)和实时动态差分定位技术(Real time kinematic,RTK)对调平过程中的杆塔倾斜状态进行测量,并且在二者频率不一致的基础上以卡尔曼滤波进行数据融合后,再以模糊PID控制算法实现杆塔调平控制。文章在试验中对比IMU和RTK在卡尔曼滤波融合下和没有融合的杆塔状态估计,其融合数据更具鲁棒性。并且在控制算法上对比传统PID控制算法和模糊PID控制算法,在超调量和恢复稳态时间上,模糊PID控制更具优势。
