| 其他摘要 | Since the first pulsar was discovered in 1967, its extreme physical state and precisely periodic signals have been the focus of our attention. And currently the periodic signal of pulsars is the unique probe for obtaining the information of the pulsar. The signal disperses when it reaches our observation equipment through the interstellar medium. To obtain the original signal of the pulsar, the observed pulsar signal must be de-dispersed, at the same time, the accuracy of pulsar timing will be improved. Nowadays, there are two kinds of de-dispersion methods: coherent de-dispersion and incoherent de-dispersion. Incoherent de-dispersion, based on time series, uses the filter component channel to shift the time domain sequence corresponding to a single channel to remove dispersion; coherent de-dispersion, in the frequency domain, remove dispersion in the whole passband by utilizing the transfer function of the intersatellite medium. In principle, the two methods of de-dispersion have advantages and disadvantages. At present, we have a qualitative understanding of that two methods: by contrast, the coherent de-dispersion algorithm is relatively simple, the de-dispersion effect is prefect, and the time resolution of the signal after de-dispersion is higher, but the amount of data after de-dispersion is larger, and the calculation is time-consuming. On the contrary, the amount of data after incoherent de-dispersion is smaller, the calculation time is less, yet the algorithm is more complex, de-dispersion is not complete, and the time resolution of signal after dispersion removal is lower. In the fields of pulsar research, there are some domestic and international scholars who compared these two methods of eliminating dispersion with some pulsar signals and obtained some comparative results. However, there is scare to systematic research on that.
This paper compares these two methods of de-dispersion in terms of computational time-consuming and the effect of de-dispersion. The idea is as follow: in the first place, I will apply MATLAB program to simulate the dispersion of the baseband signal, and then to achieve two kinds of de-dispersion algorithm, finally the correlation coefficient of the baseband signal before adding dispersion and the signal after removing dispersion, which is used to compare the effect to that two methods. Based on these correlation coefficients, under the condition of setting a certain threshold, the starting frequency of observation, which the two methods have the same dispersion-removing effect on, is determined. The structure of this paper is that, firstly, Chapter 1 introduces the theoretical background and research progress; secondly, Chapter 2 describes the theory, methods and tools used in the research; thirdly, Chapter 3 conducts data processing and analysis, and draws conclusions; in, the end, Chapter 4 will summarize the present the results of the study and look forward the next step of the research. |
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