Signal Processing Methods For X-Ray Pulsar Autonomous Navigation Syatems

Pulsars have become the most popular navigation signal source, because of their extremely stable rotation period and spatial distribution in the universe. X-Ray pulsar navigation autonomous system(XPNAV) compensates for the GPS and DSN’s low performance in deep space probe. This article starts from the analysis of mechanism of XPNAV systems, mainly discusses the acquisition of observation data, pulsar recognition and phase estimation problem. The main work includes:Simulation of X-ray pulsar signal. X-ray pulsar signal simulation is a necessary task because that we can’t receive the photons emitted by pulsars on the earth due to the long distance between the pulsar and the earth and doing the observation in the earth orbit will increase the cost of the navigation system. So generate some random sequence by software or hardware method to serve as the observation data is a feasible way. First, traditional simulation method based on Poisson model to generate the TOAs sequence of photons is introduced. Then a new simulation method based on mixture Gaussian model fitting and Monte Carlo method is proposed. This method makes use of Gaussian mixture function to fit the standard profile of pulsars so that the analytic expression is obtained. Then a Monte Carlo method is proposed to generate the TOAs according to the obtained analytic expression of profile. Numerical simulation shows the good performance of the proposed method.Pulsar recognition. For the pulsar recognition, first, Minimum entropy method is realized from the perspective of period detection. Then a concentration measurement function of the pulse profile is defined, and period detection based on the maximum of concentration measurement function is verified. Then a method combines the FFT method and maximum concentration function is proposed. To make use of the profile information, the higher order spectrum of the standard profile is extracted as the profile features, Then a RBF(Radial Basis Function) neural network classifier can be used to realize the profile matching after studying the extracted features.Phase estimation. Phase difference between the standard profile and observation profile as the input of the navigation filter is a basic and important task of navigation system. This article summarizes and realizes nonlinear least square method,circular correlation and maximum likelihood method to estimate the phase information. Moreover, the CRLB of phase estimation is derived to evaluate the different algorithms. Then considering the Doppler effect caused by the motion of spacecraft, two situations including the velocity of spacecraft when it is a constant or time varying are discussed.