| With the development of Fiber technology,the fiber Strapdown Inertial System,on behalf of optics navigation systems,has been widely used in different kinds of environment.The Inertial Measurement Unit(IMU)as the vital component of airborne navigation system is prone to affected by the movement condition and the uncertain factor in environment.Hence,it is necessary to improve the environmental suitability and the precision of measurement and state estimation of integrated system under complicated environment,which under the current technology and technique.The response characteristics of Fiber Optic Gyroscope(FOG)output signal with the variation of temperature and dynamic stress are investigated on account of the basic theory.Then,the high precision identification of dynamic error under different environment are analyzed.At last,an integrated filter method based on Interaction Multiple Model is proposed based on the different models of vehicle related to the various state of motion.The contents and contributions of this thesis are as follows:1)Due to the elastic-optic effect of fiber which will introduce non-negligible error in the output of FOG.Based on the temperature drift characteristic of FOG,a novel modeling and compensation method which integrated the artificial fish swarm algorithm(AFSA)and back-propagation(BP)neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system.In order to verifying the effective of the proposed algorithm,the predict results of BP optimized by genetic algorithm(GA)and AFSA are compared and a quantitative evaluation of compensation results are analyzed by Allan variance.The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced about 50%.2)As the elastic-optic effect of fiber which will introduce non-negligible error in the output of FOG under vibration and shock environment.To overcome the limitation of mechanism structure improvement methods and the traditional nonlinear analysis approaches,a hybrid algorithm of optimized local mean decomposition-kernel principal component analysis method(OLMD-KPCA)is proposed in this paper.The vibration signal features of higher frequency components’ which were optimized by OLMD are analyzed and the energy of which are calculated to take shape as the input vector of KPCA.In addition,the output data of three axis gyroscopes in an inertial measurement unit(IMU)under vibration experiment were analyzed to validate the effectiveness and generalization ability of the proposed approach.Experimental results demonstrate,comparing with the wavelet transform(WT),LMD-KPCA,the vibration noise in FOG output was greatly reduced.Besides,the Allan variance analysis results indicate one order magnitude increasement of the error coefficients could be achieved by OLMD-KPCA.3)To have a better evaluation of FOG under complicated environment which exhibit nonstationary dynamic characteristics over time,as the traditional Allan variance is difficult to indicate the instability of the signal and determine the stochastic error.A novel hybrid algorithm based on Overlap Nonstationary Dynamic Allan Variance(DAVAR)is proposed which considering the FOG time-varying nature.The overlap sampling could make full use of entire data in every cluster and reveal the transition during the dynamic process.The estimation accuracy of the different sampling methods based Allan Deviations are compared by simulation data and the advantages of the introduced method are verified.To track the variation of FOG error coefficients,the frequency of output signal is analyzed to determine the window length of DAVAR.Furthermore,a fast algorithm in Overlap Nonstationary DAVAR is introduced to make the computation more efficient in processing long cluster time data.In the implemented FOG vibration experiment,the changing of primary error coefficients like Quantization noise could reflects the vibration characteristics visibly.The 3-D diagrams of Overlap Nonstationary DAVAR could identify and distinguish the dynamic stochastic error items.4)In order to improve the accuracy of system measurement and navigation,the multi-errors like the drift error of FOG,approximation error of algorithm are studied when propagated in the system.A system error estimation approach is developed based on Artificial Neural Network to analysis the influence of different errors,the effectiveness is validated by compare the the estimation results with iteration Kalman filter under airborne simulations.At last,the IMM-CKF is introduced to improve the performance of vehicle velocity measurement by different motion models,especially when the noise statistical property is uncertain.The system performance is enhanced when integrated the inertial sensor signal processing and filtering methods. |
