The Key Techniques Of Ultra-Tightly Integrated GPS/SINS Navigation Systems

Ultra-tightly integrated GPS/SINS navigation system enhances the GPS correction, use the estimation of the navigation data to aid the process of GPS signal tracking, which enhances the anti-jamming capability and the navigation performance of the integrated system in the weak signal and high-dynamic environment.The filter has large amount of calculation with GPS corrected algorithm for ultra-tightly integrated GPS/SINS navigation system, GPS tracking process vulnerable to the impact of unstable tracking loop. To address these issues, this paper proposes a dual-track and closed-loop GPS correction for ultra-tightly integrated GPS/SINS navigation system. The dual-track and closed-loop correction algorithm improve GPS performance by enhancing the tracking performance of GPS tracking loop. GPS mainly track the signal independently, from the SINS output navigation information to estimate the Doppler frequency of the tracking signal, providing GPS tracking process with external amendments. Within the tracking loop, the code and carrier track together for mutual support. Use a good anti-interference features of code tracking and high-precision features of carrier tracking, which enhanced GPS autonomous tracking performance. The integration navigation filter measurements with the SINS navigation deviation, real-time estimate and correct the SINS errors, corrected SINS provide high update rate and reliable information to enhance the anti-jamming capability for GPS tracking. As a result of channels of the tracking loop are not directly connect, which reduce the mutual interference of the tracking process and enhance the tracking performance in weak signal. The correction of the carrier frequency and phase tracking error are separated, the channel filter and integrated filter estimate and correct the errors, which reduce the load. The simulation results show that the dual-track and closed-loop corrected algorithm enhance the performance of the GPS tracking process in weak signal and high dynamic environments, which meet the requirements which increased the performance of the ultra-tightly integrated navigation system.Observability analysis is important for ultra-tightly integrated navigation system design. Use the traditional method to analysis the observability of the ultra-tightly integrated navigation system is difficult, because of the complex time varing characteristics. Form the definition of the observability, this paper proposes a observability analysis method for ultra-tightly integrated navigation system by analyzing the ultra-tight integrated navigation system. According to the characteristics of the system, through a series of approximation, which simplifies the process of observability analysis. Through the decomposition of the movement and the observability results understand the information that the observability variation with the body movement, which provide guidance for system design.A detection method of abnormal Doppler frequency based on local wave analysis is proposed. According to the problem that the empirical mode decomposition method are not strict and existing pseudo-component, improves selection conditions, proposes the elimination of pseudo-component method. Using Doppler frequency shift signal with the self-similar characteristics, analyzes the changes of self-similarity in the same time between Doppler frequency and in-phase signal with local wave method, obtains judgments of abnormal Doppler. Experimental results show that this method can effectively detect the Doppler shift arising from abnormal. Based on the AR model, this paper proposes SINS supporting information of timing estimation method, effectively suppress the short-term disturbance. A time synchronization method between GPS and SINS is proposed based on the GPS second pulse. According to the problem that observation time data and update synchronization time are different by channel filtering process and integrated navigation filtering process, this paper proposes a data synchronization method. Use the time and data update method to estimate the observations in filtering time by the optimal estimation of the channel filter and integrated navigation filter output, reducing the filtering error. The simulation results show that the algorithm proposed by this paper effectively reduce the time warp and enhance the accuracy of GPS tracking process and integration filtering process.Navigation parameter estimation is most important issue for integrated system. According to the problem which incorrect error model and inaccurate noise estimation lead to larger filter error. Through study the ultra-tightly integrated navigation system, the estimation method of system noise and measurement noise based on the maximum likelihood estimation is proposed. Analyzing the observability of the ultra-tightly integrated navigation system, which results show that there have unobservable state within system, this state change with the movement of body , this unobservability generally much shorter. The variation of state observability have a significant impact to integrated navigation system, leading to increased filtering error or divergence. This paper proposes a adaptive fading memory kalman filtering method based on fuzzy rules. This method sets a weight for kalman filter gain matrix, by changing the weight to adjust the impact from observable information to estimate the new state. Based on the observability analysis results for ultra-tightly integrated navigation system in front, designing the method for adjusting the weights. From the velocity, accelerate, attitude information and observability analysis results, adjusting the weight in real time, which reduce the impact from unobservable state to filtering process. The simulation results show that the fuzzy fading memory adaptive kalman filtering method reduce the impact of short-time change of state observability, enhance the accuracy of filtering and performance of ultra-tightly integrated navigation system.