To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two diff...To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.展开更多
基金supported by the National Natural Science Foundation of China (60902055)
文摘To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.
文摘该文基于MEMS电场敏感芯片,研制出了一种新型的地面大气电场传感器,解决了现有场磨式电场仪易磨损、功耗大、故障率高等问题。敏感芯片采用SOIMUMPS加工工艺制备,其芯片面积仅为5.5 mm×5.5 mm。该文提出了传感器敏感芯片的弱信号检测方法,设计出了满足环境适应性的传感器整体结构方案,并建立了传感器的灵敏度分析模型。对电场传感器进行测试,测量范围为-50 k V/m^50 k V/m,总不确定度为0.67%,分辨力达到10 V/m,功耗仅为0.62 W。外场试验结果表明,MEMS地面大气电场传感器在晴天和雷暴天的电场探测结果,与Campbell公司场磨式电场仪探测结果都有较好的一致性,说明该传感器能满足预测雷暴要求,实现雷电监测和预警功能。
