A Satellite Signal Frequency Tracking Method Based on Correlation Evaluation

Abstract

The present invention relates to a satellite signal frequency tracking method based on correlation evaluation. The tracking method includes: S1. Extracting each signal connection start and disappearance event, forming sequence data by combining the event type and the corresponding timestamp, and performing time series correlation analysis between signals with non-overlapping connection start times and different frequencies in pairs; S2. Obtaining signal pairs with a correlation degree greater than a threshold limit1 according to the correlation analysis result, sorting them according to the correlation degree, and outputting them as the final result. The corresponding frequencies of each pair of signals are different communication frequencies of the same object. The present invention improves the problem of weak anti-noise ability by converting the sequence into a broken line; by calculating the overall direction of each line segment in the broken line, it avoids various problems such as the exponential increase in computational complexity when the sampling rate is high and inaccurate results caused by the distortion of the original data when the sampling rate is low when using other correlation degree formulas after resampling.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a satellite signal frequency tracking method based on correlation evaluation. Background Art

[0002] In recent years, with the rapid development of aerospace technologies, the number of launched satellites has increased rapidly, and the number of acquired signals has also increased accordingly. On the other hand, with the rapid development of the computer industry, the improvement of software and hardware environments provides a good environment for the implementation of complex algorithms. Therefore, it is an inevitable development trend to use computers to automatically extract valuable information from large-scale satellite signal data. For a certain object to be recognized, it can emit signals at different frequencies at different times, and the acquired data at this time is multiple independent and irrelevant data segments. How to find out different frequency communication data that may come from the same object from a large amount of independent data is of great significance for signal and object recognition.

[0003] Existing correlation analysis methods mainly include methods such as calculating cosine similarity and Pearson correlation coefficient, all of which require aligned and sufficient sample points representing the change trend. For satellite signal time series, the appearance and disappearance events are random, and the timestamps of two different signals are almost impossible to be aligned, and the signal event values are only binary. Representing the change trend directly in this way will be seriously affected by noise. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art, and provides a satellite signal frequency tracking method based on correlation evaluation, which solves the deficiencies existing in the prior methods.

[0005] The purpose of the present invention is achieved through the following technical solutions: A satellite signal frequency tracking method based on correlation evaluation, the tracking method includes:

[0006] S1. Extract the appearance and disappearance events of each signal, form sequence data by combining the event type and the corresponding timestamp, and perform time series correlation analysis between any two signals with non-overlapping appearance times and different frequencies;

[0007] S2. Obtain signal pairs with a correlation degree greater than the threshold limit1 according to the correlation analysis results, sort them according to the correlation degree, and output them as the final result. The corresponding frequencies of each pair of signals are the different communication frequencies of the same object.

[0008] The extraction of the appearance and disappearance events of each signal, forming sequence data by combining the event type and the corresponding timestamp, and performing time series correlation analysis between any two signals with non-overlapping appearance times and different frequencies includes:

[0009] S11. Query the sequence data of all signals within the set time window, and sort the sequence data corresponding to each signal in ascending order according to the timestamps.

[0010] S12. Determine whether there are cases of consecutive events of the same type in the sorted sequence data. If so, only retain the event with the smallest timestamp in this consecutive same-type event.

[0011] S13. Calculate the correlation degree between all pairs of signals. If there is an overlap in the connection time or the same frequency between two signals, set their correlation degree to -1.

[0012] The calculation of the correlation degree between all pairs of signals includes:

[0013] A1. Set the connection event as 1 and the disappearance event as 0. The original sequence data is transformed into a digital sequence composed of 1s and 0s, and each digit corresponds to a timestamp.

[0014] A2. Subtract the starting timestamp of each sequence from all timestamps of each sequence to zero the starting time of each sequence.

[0015] A3. Establish a coordinate system with the values in the digital sequence on the y-axis and the timestamps on the x-axis. Represent each data in the sequence as a point in the coordinate system, and connect the adjacent timestamp data points to obtain a broken line graph starting from x = 0.

[0016] A4. Set the direction of the line segment with the y-axis value increasing from small to large as upward, and the direction of the line segment with the y-axis value decreasing from large to small as downward.

[0017] A5. Process the signal sequences A and B according to the broken line graph to obtain the signal sequences A2 and B2 with the same timestamps.

[0018] A6. Calculate the correlation degree s according to the respective directions of each same-time interval in the processed signal sequences A2 and B2, and add s to the result set S.

[0019] A7. Determine the sequence lengths of the original signals A and B. If the length of the A signal sequence is greater than that of the B signal, discard the first point of the A signal sequence, subtract the timestamp of the new first point of the A signal sequence from all timestamps of the A signal sequence, keep the B signal sequence unchanged, and recalculate the correlation degree s of the two signal sequences and add it to the result set S.

[0020] A8. Repeat step A7 until the end timestamp of the B signal sequence is greater than the end timestamp of the A signal sequence, and select the value with the largest absolute value in the result set S as the correlation degree between signals A and B.

[0021] The processing of the two signal sequences A and B according to the line graph to obtain the signal sequences A2 and B2 with the same timestamps includes:

[0022] A51. Set the signal sequences A and B, and judge their sequence lengths. Truncate the long signal sequence through the end timestamp of the short signal sequence, and discard the truncated latter half of the long signal sequence to obtain the signal sequences A1 and B1;

[0023] A52. Obtain the union of the timestamps of the signal sequences A1 and B1 and remove duplicates to obtain the union sequence T2;

[0024] A53. Calculate the values corresponding to each timestamp in T2 on the line graph, realize the expansion of the signal sequences A1 and B1, and obtain the expanded signal sequences A2 and B2. At this time, A2 and B2 are sequences with the same timestamps, and the time range is R.

[0025] The threshold limit1 = 0.8.

[0026] The present invention has the following advantages:

[0027] 1. Evaluate the possibility that different signals belong to the same object from the perspective of signal correlation. Positive correlation indicates that the object may communicate at different frequencies with a similar communication rhythm, and negative correlation indicates that the object may switch to different frequencies for continuous communication. Therefore, correlation is a relatively reasonable evaluation angle.

[0028] 2. By proposing a new algorithm for data characteristics, an effective measurement of the correlation degree of a sequence composed of binary event types and sparse in the time dimension is achieved. By converting the sequence into a line graph, the problem of weak anti-noise ability is improved; by calculating the overall direction of each line segment in the line graph, various problems such as the exponential increase in computational complexity when the sampling rate is high and the inaccurate results caused by the distortion of the original data when the sampling rate is low during the calculation using other correlation degree formulas after resampling are avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic flowchart of the present invention;

[0030] Figure 2 is a schematic diagram of the overlapping situation of the outgoing connection time;

[0031] Figure 3 is a schematic diagram of sequence truncation and expansion;

[0032] Figure 4 is a schematic diagram of repeatedly discarding the starting point of the original longer sequence. DETAILED DESCRIPTION OF THE INVENTION

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only some of the embodiments of this application, rather than all the embodiments. The components of the embodiments of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of this application provided below with reference to the accompanying drawings is not intended to limit the protection scope of the claimed application, but only represents the selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts belong to the protection scope of this application. The following further describes the present invention with reference to the accompanying drawings.

[0034] The present invention specifically relates to a satellite signal frequency tracking method based on correlation evaluation. By extracting the connection and disappearance events for each signal, a sequence data is formed by combining the event type and the corresponding timestamp. Time series correlation analysis is performed pairwise on signals with non-overlapping connection times and different frequencies. Based on this result, signal pairs are obtained with the correlation degree sorted from high to low. The corresponding frequencies of each pair of signals are the possible different communication frequencies of the same object; it can automatically discover different frequency signals that may belong to the same object through the signal time series characteristics.

[0035] As Figure 1 shown, it specifically includes the following steps:

[0036] A. Query the sequences formed by the connection and disappearance events and the corresponding timestamps of all signals in a specified time window. The sequence corresponding to each signal is sorted in ascending order of the timestamp. After sorting, if there are consecutive connection or consecutive disappearance events, only keep the event with the smallest timestamp among them.

[0037] B. Calculate the correlation degree pairwise for all signals. If there is an overlap in the connection time or the frequencies are the same between two signals, directly set their correlation degree to -1, where the overlap condition of the connection time is as Figure 2 shown. If there is no overlap in the connection time or the frequencies are the same, continue with the following steps:

[0038] C. Set the connection event as the number 1 and the disappearance event as the number 0, then the original sequence data becomes a digital sequence composed of 1 and 0, and each digit corresponds to a timestamp.

[0039] D. Subtract the starting timestamp of each sequence from all timestamps of each sequence, that is, set the starting time of each sequence to 0.

[0040] E. Further represent the data as a graph, that is, establish a coordinate system where the y-axis represents the values in the digital sequence and the x-axis represents the timestamps. Then each data in the sequence can be represented as a point on the coordinate axes. Connect the data points with adjacent timestamps, and a line graph starting from x = 0 is obtained. The process of converting the sequence into a line graph is as Figure 3 shown.

[0041] F. Assume that the direction of the line segment with the y-axis value increasing from small to large is upward, and from large to small is downward.

[0042] G. Suppose there are two signals A and B. Truncate the longer sequence with the timestamp of the end point of the shorter sequence, and discard the latter half of the longer sequence that is truncated. At this time, signal sequences A1 and B1 are obtained. Then take the union of the timestamps of A1 and B1, remove duplicates, and set this union sequence as T2. Expand signals A1 and B1 according to T2 respectively, that is, find the values corresponding to the respective fold lines for each timestamp in T2. The truncation and expansion processes are as Figure 3 shown. The expanded sequences are set as A2 and B2. At this time, A2 and B2 are sequences with the same timestamps, and their time range is set as R.

[0043] H. Calculate the correlation degree s according to the respective directions of each same time interval in A2 and B2, and add s to the result set S.

[0044] Its calculation formula is:

[0045] where: n is the total number of time intervals; t k is the length of the k-th time interval; R is the total time range; d k is the direction relationship identifier of the k-th time interval, with the same direction being 1 and the opposite direction being -1.

[0046] This formula evaluates the correlation from the overall perspective of each broken line segment, avoiding problems such as efficiency and noise caused by using point sequences for calculation in other algorithms during the sampling of the original data. When one signal is mostly rising while the other signal is also mostly rising each time it is rising, and mostly falling while the other signal is also mostly falling each time it is falling, it indicates that they have positive correlation, corresponding to a calculation result close to 1 in the formula. Conversely, they have negative correlation, corresponding to a calculation result close to -1 in the formula. Among them, t k / is equivalent to the weight of the correlation of each small segment. When one signal is in a certain direction, and the other signal is sometimes in the same direction as it and sometimes in the opposite direction, it means that the two signals have no correlation, corresponding to a calculation result close to 0 in the formula.

[0047] I. If the lengths of the original signal A and B sequences are inconsistent, assume that the length of sequence A is greater than that of sequence B. Discard the first point of sequence A, and subtract the timestamp of the new first point of sequence A from the timestamps of all points in sequence A. The shorter sequence remains unchanged. Recalculate the correlation degree s with these two sequences and add it to the result set S.

[0048] J. Repeat step I until the timestamp of the end point of sequence B is greater than that of the end point of sequence A. As shown Figure 4 in the figure, at this time, select the value with the largest absolute value in the result set S, which is the correlation degree between signals A and B.

[0049] K. Select signal pairs with a correlation degree greater than the threshold, and sort each signal pair in descending order according to the correlation degree, and output it as the final result.

[0050] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. Any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A satellite signal frequency tracking method based on correlation evaluation, characterized in that: The tracking method includes: S1. Extract the outgoing and disappearing events of each signal, combine the event type and the corresponding timestamp into sequence data, and perform time series correlation analysis between signals with non-overlapping outgoing times and different frequencies; S2. According to the correlation analysis results, obtain the signal pairs whose correlation degree is greater than the threshold value limit1 and sort them according to the correlation degree and output them as the final result. The corresponding frequency of each pair of signals is the different communication frequency of the same object. The extracting of each signal outgoing and disappearing event, combining the event type and the corresponding timestamp into sequence data, and performing time series correlation analysis between signals with non-overlapping outgoing and disappearing times and different frequencies include: S11, querying the sequence data of all signals in the set time window, and sorting the sequence data corresponding to each signal in ascending order according to the timestamp; S12, determining whether there are consecutive events of the same type in the sorted sequence data, and if so, retaining only the event with the smallest timestamp among the consecutive events of the same type; S13, calculating the correlation between all signals, if the outgoing time between two signals overlaps or the frequency is the same, the correlation is set to -1; The calculation of the correlation between all signals comprises: A1. Set the outgoing event to 1 and the disappearing event to 0. The original sequence data is converted into a digital sequence consisting of 1 and 0, and each number corresponds to a timestamp. A2. Subtract the starting time stamp of each sequence from all its time stamps to reset the starting time of each sequence to zero; A3. Create a coordinate system with the y-axis representing the value in the digital sequence and the x-axis representing the timestamp. Represent each data point in the sequence as a point in the coordinate system and connect adjacent timestamp data points to obtain a line graph starting from x=0. A4. Set the direction of the line segment from small to large on the y-axis to be upward, and the direction of the line segment from large to small on the y-axis to be downward; A5, processing the two signal sequences A and B according to the line graph to obtain signal sequences A2 and B2 with the same timestamp; A6. Calculate the correlation s according to the respective directions of each identical time interval in the processed signal sequences A2 and B2, and add s to the result set S; A7. Determine the sequence lengths of the original signals A and B. If the length of the A signal sequence is greater than that of the B signal sequence, discard the first point of the A signal sequence, and subtract the timestamp of the new first point of the A signal sequence from the timestamps of all points of the A signal sequence. The B signal sequence remains unchanged, and recalculate the correlation s of the two signal sequences and add them to the result set S. A8. Repeat step A7 until the timestamp of the end point of signal sequence B is greater than the timestamp of the end point of signal sequence A, and select the value with the largest absolute value in the result set S as the correlation between signals A and B.

2. The satellite signal frequency tracking method based on correlation evaluation according to claim 1, wherein: The processing of the two signal sequences A and B according to the line graph to obtain the signal sequences A2 and B2 with the same timestamp includes: A51, set signal sequences A and B, and determine their sequence lengths, truncate the long signal sequence by the end timestamp of the short signal sequence, discard the truncated second half of the long signal sequence, and obtain signal sequences A1 and B1; A52. Obtain the union of the timestamps of signal sequences A1 and B1 and remove duplicates to obtain the union sequence T2; A53. Calculate the values on the broken line corresponding to each timestamp in T2 to realize the expansion of signal sequences A1 and B1, and obtain the expanded signal sequences A2 and B2. At this time, A2 and B2 are sequences with the same timestamps, and the time range is R.

3. The satellite signal frequency tracking method based on correlation evaluation according to claim 1, wherein: The threshold limit1 = 0.8.

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