Research On Time Delay Estimation And Intelligent Filtering Algorithm Of Pulsar Navigation

Pulsars are signal sources that rotate at high speeds in the universe,and has stable pulsar periods.Pulsars can provide a rich amount of information by analyzed and processed its signals for spacecraft navigation,such as time,position,attitude and spe ed.Based on the above characteristics,a navigation technique called X-ray pulsar autonomous navigation has emerged.Pulse time of arrival(TOA)is the basic measurement parameter of X-ray pulsar navigation,and the TOA of the pulsar is measured by comparing the standard and observed pulsar-integrated pulse profiles.Studying a high-precision time delay estimation algorithm and controlling its computational complexity is the key to ensure the accuracy and real-time of X-ray pulsar navigation.In addition,the X-ray pulsar navigation filtering algorithm plays a direct role in the accuracy of spacecraft position,velocity and attitude estimation,and is a key part of pulsar navigation research.Improving the estimation accuracy of the filtering algorithm is crucial for the precise positioning of the spacecraft.As a traditional time delay estimation algorithm in X-ray pulsar autonomous navigation,the generalized weighted cross-correlation algorithm considers the possible correlation between signal and noise,and uses the weighting function to improve the classical cross-correlation algorithm.Although the algorithm has a good processing result for the stationary signals,the pulse signals are not absolute stationary in the universe,and the noise in the cosmic environment is complicated,which leads to poor performance of the algorithm.At the same time,due to the instability of the X-ray pulsar radiation pulse and the complex background noise of the universe,the statistical characteristics of the measurement noise of the X-ray pulsar are not static.However,the traditional EKF algorithm cannot adjust the noise matrix for the above variables,and uses the noise variance far from the real state to estimate the position and velocity,which will cause large errors.In view of the above two practical problems in X-ray pulsar navigation,this paper starts with the pulsar time delay estimation algorithm and navigation filtering algorithm,and studies the method of improving the navigation accuracy of spacecraft:(1)A generalized weighted cross-correlation X-ray pulsar delay estimation algorithm based on FRFT is proposed.The fractional Fourier transform,a mathematical tool for non-stationary signal processing,is introduced into the conventional GCC algorithm,tran sform the signal to the optimum FRFT domain to obtain the best effect of noise suppression on a signal,and then the pulse signal is correlated in the FRFT domain to obtain the FRFTbased signal cross power spectrum function.Finally,estimate time delay by using the FRFT-based GCC algorithm to achieve high accuracy.By this way,we can obtain higherprecision delay estimates with the ability to ensure good performance.(2)Combining the traditional X-ray pulsar navigation filtering algorithm and artificial intelligence algorithm,the particle swarm optimization(PSO)algorithm is introduced into the X-ray pulsar navigation positioning algorithm to construct an X-ray pulsar autonomous navigation positioning model based on PSO.The PSO algorithm is used to update and optimize the noise matrix in EKF to ensure that the statistical characteristics of system noise and measurement noise in the navigation filtering algorithm are similar to the actual noise,to cope with the problem of decreasing filtering accuracy when the statistical characteristics of system noise and measurement noise in X-ray pulsar autonomous navigation change uncertainties.