A Fast Search Method for Pulsar Frequency Parameters

Abstract

The invention provides a fast search method for pulsar frequency parameters, specifically a fast search method for frequency parameters applied to spaceborne detectors receiving pulsar signals, including three steps: determining the index function for frequency parameter search; applying dynamic The reduced cross-entropy method performs a large-scale coarse search of the frequency parameters of the pulse signal to obtain an accurate search sample space; the small-scale differential evolution method is used to complete the fast iterative refinement calculation of the frequency parameters in the accurate sample space. The scheme realizes fast and accurate estimation of the frequency parameters of the pulsar measured signal, in order to complete the real-time on-orbit dynamic signal processing process, and obtain accurate estimates of the pulse signal frequency and the first-order derivative of the frequency.

Description

Technical field [0001] The present invention relates to the field of aerospace agency, and more particularly to a fast search method of pulse star frequency parameters. Background technique [0002] X-ray pulse star navigation currently has entered the space demonstration verification phase, which is the focus of the signal processing technology of rail spacecraft to meet the actual engineering application requirements of the pulse star navigation. [0003] The X-ray pulse star signal is extremely weak. During the X-ray pulse star navigation process, the spacecraft needs to be in the rails, and the Doppler effect caused by the spacecraft track motion causes the detector. The received pulse star signal frequency is frequent. For dynamic signal processing techniques, it utilizes an estimated track information, and the location information of the spacecraft through the track kinetic model is extracted through the track dynamics model, and the initial error of the system will be enla...

AI Technical Summary

Problems solved by technology

Since photon TOA data is a non-equally spaced discrete sequence, frequency domain search methods mostly use approximate processing methods such as resampling or equal space interpolation, and problems such as "spectrum leakage" will lead to poor frequency search accuracy. The processing results of some frequency-domain search methods are significantly different from the actual situation; the time-domain search methods generally have high precision and can meet high-precision frequency estimation, but the large search range will affect the calculation efficiency, and the strong index function of the search process Problems such as noise and multiple extreme values ​​will also affect the frequency search accuracy

[0005] The search methods in the prior art have the following problems: (1) Most of the existing frequency (period) search methods do not consider the actual orbital dynamics of the spacecraft or use the simulation data of the simulated orbital dynamics for inspection, but the actual data processing shows that most of the The processing results of the frequency domain search method are significantly different from the actual situation, the estimation accuracy is poor, and the amount of calculation is huge; (2) Most of the existing frequency (period) search methods only search for the frequency (period) of the received pulsar signal, without considering the The impact of the orbit dynamics on the first-order derivative of the frequency leads to poor error absorption effect, and the high precision of processing and calculation efficiency cannot be balanced; (3) The calculation efficiency of the traditional grid two-dimensional search algorithm is extremely poor, and the distribution of index function values ​​​​is messy , there are a large number of local value peaks at different positions, which is a complex multi-extreme value optimization problem, and it is easier to fall into a local optimum during the search process; (4) Some methods reduce the frequency of calculation by batch processing photon data (Periodic) search method, because less photon information is used each time, the result is easier to converge to a local optimum, and the robustness is poor

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