| With the development of car networking and smart car technologies,vehicle location technology has become a research hotspot.At this stage,the on-board navigation system is mainly a Global Positioning System(GPS).The system has the advantages of real-time,all-weather,high precision,but it will be affected in the environment where satellites such as city buildings,tunnels,and shaded areas are sheltered.Can not be positioned,so you need to use other positioning technology to assist GPS system positioning.This article uses the SINS assisted GPS system to compensate for the trajectory of the blind GPS zone.The main research work is as follows:Firstly,the paper introduces the background and significance of blind spot location of vehicles,and introduces the current research status of navigation and positioning technology,integrated navigation and positioning technology,micro-system technology(MEMS)and data fusion technology,and then introduces the commonly used navigation system.The commonly used geographical parameters;For the GPS system,the principle,structure and error of the GPS system are mainly analyzed.For the inertial navigation system,the commonly used coordinate system and its conversion method are introduced,and the solution principle of the inertial navigation is analyzed.Error sources are summarized.Based on the analysis of inertial navigation in the second chapter of this paper,the trajectory and inertial devices are simulated in the MATLAB environment.The trajectory simulation mainly includes the simulation of true value trajectory,blind trajectory,and GPS trajectory.The ideal force information and angular velocity information generated by using the true value trajectory and artificially injected inertial device errors achieve the purpose of simulating inertial devices.Through the error of the inertial navigation in the blind area,we can see that with the accumulation of time,the error is getting bigger and bigger,so we need to reduce the accumulated error by GPS positioning and data fusion algorithm.Then analyzes the combination of GPS/INS integrated navigation system and the feasibility of the project,and finally chooses the combination of loosely coupled integrated navigation system.Based on the analysis of the inertial navigation error in the paper,the mathematical model of the error of the integrated navigation system is deduced,and the state equation and measurement equation of the integrated navigation system are established.Then the data fusion algorithm of integrated navigation system is studied.Since the system model of the integrated navigation system is non-linear and there is noise in the system,it will affect the positioning accuracy of the system and cause the result of diverge in severe cases.The system model can be processed from two perspectives: an approximate nonlinear function and a Gaussian probability density function.In this paper,the Extended Kalman Filter is used to linearize the system model to complete the approximation of the state of the nonlinear system.Finally,the output value of the state is obtained.The unscented Kalman filter algorithm is used to solve the problem.The probability density distribution of the linear integrated navigation system is approximated,and a certain number of samples are used to approximate the state posterior probability density.However,for a system with a higher system state dimension,the covariance of the UKF algorithm may be indefinite.There is no guarantee of the numerical stability of the filtering algorithm.In order to solve this problem,this paper studies the Cubature Kalman Filter.The volume point is recalculated by a nonlinear function to obtain a new set of volume points,and then the weighted sum is used to approximate the state posterior mean and covariance.The above system model and filtering algorithm are implemented in MATLAB through m language.By analyzing and comparing the maximum value of position,speed,and attitude errors in dead zone and calculating the RMS error value of the entire trajectory,the analysis results show that CKF algorithm has higher navigation accuracy. |
