CN114201994B

CN114201994B - A satellite signal frequency tracking method based on correlation evaluation

Info

Publication number: CN114201994B
Application number: CN202111525124.8A
Authority: CN
Inventors: 战永盛; 余安东; 王鹏珍
Original assignee: Fifth Research Institute Of Telecommunications Technology Co ltd
Current assignee: Fifth Research Institute Of Telecommunications Technology Co ltd
Priority date: 2021-12-14
Filing date: 2021-12-14
Publication date: 2025-06-24
Anticipated expiration: 2041-12-14
Also published as: CN114201994A

Abstract

The invention relates to a satellite signal frequency tracking method based on correlation evaluation, which comprises the following steps of S1, extracting each signal out-connection and disappearance event, forming sequence data by event types and corresponding time stamps, carrying out time sequence correlation analysis on signals which are not overlapped in out-connection time and have different frequencies, S2, acquiring signal pairs with the correlation degree larger than a threshold limit1 according to correlation analysis results, sequencing according to the correlation degree, and outputting the signal pairs as final results, wherein the corresponding frequencies of each pair of signals are different communication frequencies of the same object. The invention improves the problem of weak noise resistance by converting the sequence into the broken line, and avoids various problems of exponential increase of calculation complexity caused by high sampling rate and inaccurate results caused by original data distortion when the sampling rate is low by calculating the whole direction of each line segment in the broken line and calculating by using other correlation formulas after resampling.

Description

Satellite signal frequency tracking method based on correlation evaluation

Technical Field

The invention relates to the technical field of communication, in particular to a satellite signal frequency tracking method based on correlation evaluation.

Background

With the rapid development of aerospace technology in recent years, the number of transmitting satellites is rapidly increased, the number of acquired signals is also increased, on the other hand, the rapid development of computer industry is realized, and the improvement of software and hardware environments provides a good environment for the realization of complex algorithms. Therefore, the computer is used for automatically mining valuable information from large-scale satellite signal data, and the development direction of the trend is adopted. For a certain object to be identified, the object to be identified can transmit signals at different frequencies at different times, and the acquired data are multiple pieces of independent and unrelated data. How to find out communication data of different frequencies, which may come from the same object, from a large amount of independent data is of great importance for the identification of signals and objects.

The existing correlation analysis method mainly aims at solving the modes of cosine similarity, pearson correlation coefficient and the like, and all the existing correlation analysis method is required to have aligned and enough sample points for representing the change trend. For satellite signal time series, the occurrence and disappearance events have randomness, the time stamps of two different signals are almost impossible to align, and the signal event has only two values, so that the direct indication of the change trend can be affected by serious noise.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, provides a satellite signal frequency tracking method based on correlation evaluation, and solves the defects of the prior method.

The invention aims at realizing the following technical scheme that the satellite signal frequency tracking method based on the relevance evaluation comprises the following steps:

S1, extracting each signal outgoing and disappearing event, forming sequence data by the event type and the corresponding time stamp, and carrying out time sequence correlation analysis on the signals which are not overlapped in outgoing time and have different frequencies;

s2, acquiring signal pairs with the correlation degree larger than a threshold limit1 according to the correlation analysis result, and outputting the signal pairs as final results after sequencing according to the correlation degree, wherein the corresponding frequencies of the signals of each pair are different communication frequencies of the same object.

Extracting each signal outgoing and disappearing event, forming sequence data by the event type and the corresponding time stamp, and carrying out time sequence correlation analysis on the signals which are not overlapped in outgoing time and have different frequencies, wherein the time sequence correlation analysis comprises the following steps:

s11, inquiring the sequence data of all signals in a set time window, and sorting the sequence data corresponding to each signal in an ascending order according to the time stamp;

S12, judging whether the sequenced sequence data has the condition of continuous identical event types, if so, only one event with the smallest time stamp is reserved in the continuous event types;

s13, calculating the correlation degree between every two signals, and setting the correlation degree to be-1 if the two signals have overlapping or same frequency in the out-connection time.

The calculating the correlation degree between every two signals comprises the following steps:

a1, setting an out-connection event as 1, setting a vanishing event as 0, and converting original sequence data into a digital sequence consisting of 1 and 0, wherein each digital corresponds to a time stamp;

a2, subtracting the starting time stamp of each sequence from all time stamps of each sequence, and resetting the starting time of each sequence to zero;

A3, establishing a value in a digital sequence on a y axis, and using an x axis as a coordinate system of a time stamp, and representing each data in the sequence as a point in the coordinate system and connecting adjacent time stamp data points to obtain a line graph taking x=0 as a starting point;

A4, setting the line segment direction from small to large in the y-axis value as upward and the line segment direction from large to small as downward;

A5, processing the A, B two signal sequences according to the line diagram to obtain an A2 signal sequence and a B2 signal sequence with the same time stamp;

a6, calculating a correlation S according to the respective directions of each same time interval in the processed A2 and B2 signal sequences, and adding the S into a result set S;

A7, judging the sequence length of the original signal A, B, if the sequence length of the A signal is larger than that of the B signal, discarding the first point of the A signal, subtracting the time stamp of the new first point of the A signal from the time stamp of all points of the A signal, keeping the B signal unchanged, recalculating the correlation degree S of the two signal sequences, and adding a result set S;

A8, repeating the step A7 until the end point time stamp of the signal sequence B is larger than the end point time stamp of the signal sequence A, and selecting the value with the largest absolute value in the result set S as the correlation degree of the signals A and B.

Processing the A, B two signal sequences according to the line diagram to obtain an A2 signal sequence and a B2 signal sequence with the same time stamp, wherein the processing comprises the following steps:

A51, setting signal sequences A and B, judging the sequence length of the signal sequences, cutting off a long signal sequence through a last time stamp of the short signal sequence, and discarding the second half part of the cut-off long signal sequence to obtain signal sequences A1 and B1;

a52, obtaining the time stamps of the signal sequences A1 and B1, and performing repeated elimination after merging to obtain a merging sequence T2;

A53, calculating the value on the corresponding folding line of each time stamp in T2, and expanding the signal sequences A1 and B1 to obtain expanded signal sequences A2 and B2, wherein at the moment, A2 and B2 are sequences with the same time stamp, and the time range is R.

The threshold limit 1=0.8.

The invention has the following advantages:

1. The probability that different signals belong to the same object is evaluated from the perspective of signal correlation, positive correlation indicates that the objects can communicate with different frequencies at similar communication rhythms, and negative correlation indicates that the objects can switch different frequencies for continuous communication, so that the correlation is a reasonable evaluation angle.

2. By providing a new algorithm for the data characteristics, the correlation degree of the sequence which consists of binary event types and has sparse time dimension is effectively measured. By calculating the whole direction of each line segment in the broken line, various problems of inaccurate results and the like caused by high-time calculation complexity index increase of sampling rate and low-time original data distortion caused by calculation by using other correlation formulas after resampling are avoided.

Drawings

FIG. 1 is a schematic flow chart of the present invention;

FIG. 2 is a schematic diagram of the out-of-line time overlap condition;

FIG. 3 is a schematic diagram of sequence truncation and expansion;

FIG. 4 is a schematic diagram of repeated discarding of the first point of the original longer sequence.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Accordingly, the following detailed description of the embodiments of the application, as presented in conjunction with the accompanying drawings, is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application. The application is further described below with reference to the accompanying drawings.

The invention particularly relates to a satellite signal frequency tracking method based on correlation evaluation, which comprises the steps of extracting a union event and a disappearance event from each signal, forming sequence data by the event type and a corresponding timestamp, carrying out time sequence correlation analysis on signals which are not overlapped in union time and have different frequencies, obtaining signal pairs with the correlation degree from high to low according to the result, wherein the corresponding frequencies of each pair of signals are different communication frequencies which are possible to the same object, and automatically mining out different frequency signals which are possible to belong to the same object through signal time sequence characteristics.

As shown in fig. 1, the method specifically comprises the following steps:

A. And inquiring sequences formed by respective outgoing and disappearing events and corresponding time stamps of all signals of a specified time window, and sorting the sequences corresponding to the signals according to the time stamps in an ascending order, wherein if continuous outgoing or continuous disappearing events occur after sorting, only one event with the smallest time stamp is reserved.

B. The degree of correlation is calculated for all signals between each other. If there is overlap or the frequencies are the same between the two signals, the correlation degree is directly set to-1, wherein the overlap condition of the outgoing time is shown in fig. 2. If no outgoing time has overlapping or the same frequency, continuing the following steps:

C. setting the out-connection event to the number 1 and the vanishing event to the number 0, the original sequence data becomes a digital sequence consisting of 1 and 0, each digital corresponding to a time stamp.

D. the starting time stamp of each sequence is subtracted from all the time stamps of each sequence, i.e. the starting time of each sequence is normalized to 0.

E. Further representing the data as a graph, i.e. establishing a coordinate system, the y-axis being the values in the digital sequence and the x-axis being the time stamps, each data in the sequence may be represented as a point in the coordinate axis. Connecting adjacent timestamp data points results in a line graph starting at x=0. The sequence-to-line diagram process is shown in fig. 3.

F. The line segment direction from small to large in y-axis value is set to be upward, and from large to small is set to be downward.

G. Two signals A and B are provided, the longer sequence is truncated by the end time stamp of the shorter sequence, the second half of the truncated longer sequence is discarded, and the signal sequences A1 and B1 are obtained. The A1, B1 timestamp union is then taken, deduplicated, and the union sequence is set to T2. The signals A1 and B1 are respectively expanded according to T2, that is, the value on the corresponding folding line corresponding to each timestamp in T2 is obtained, and the intercepting and expanding process is shown in fig. 3. The extended sequences are designated as A2 and B2, where A2 and B2 are sequences having the same time stamp, and the time range is designated as R.

H. And calculating a correlation S according to the respective directions of each identical time interval in A2 and B2, and adding S into a result set S.

The calculation formula is as follows:

Wherein n is the total number of time intervals, t _k is the length of the kth time interval, R is the total time range, d _k is the direction relation mark of the kth time interval, the same direction is 1, and the reverse direction is-1.

The formula evaluates the correlation from the whole angle of each broken line segment, so that the problems of efficiency, noise and the like caused by the fact that other algorithms use point sequences to calculate and sample the original data are avoided. When one signal is rising at a time, the other signal is rising at a large part, and when the signal is falling at a time, the other signal is falling at a large part, and the signals have positive correlation, the calculated result is close to 1 in the corresponding formula, otherwise, the calculated result is close to-1 in the corresponding formula. Where t _k/is the weight of each segment correlation. When one signal is in a certain direction, the other signal is in the same direction and opposite to the other signal, which means that the two signals have no correlation, and the calculated result in the corresponding formula is close to 0.

I. if the lengths of the sequence A and the sequence B of the original signals are inconsistent, the sequence A is assumed to be longer than the sequence B, the first point of the sequence A is discarded, and the time stamp of the new first point A is subtracted from the time stamp of all points of the sequence A. The shorter sequence is unchanged. The correlation S is recalculated with the two sequences and added to the result set S.

J. And (3) repeating the step I until the end point time stamp of the sequence B is larger than the end point time stamp of the sequence A, and selecting the value with the largest absolute value in the result set S at the moment, namely the correlation degree of the signals A and B, as shown in the figure 4.

K. and taking signal pairs with the correlation degree larger than a threshold value, and sorting the signal pairs in descending order according to the correlation degree, and outputting the signal pairs as a final result.

The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.

Claims

1. A satellite signal frequency tracking method based on correlation evaluation, characterized in that: the tracking method comprises:

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. A satellite signal frequency tracking method based on correlation evaluation according to claim 1, characterized in that: the processing of the two signal sequences A and B according to the line graph to obtain the A2 and B2 signal sequences with the same timestamps comprises:

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 timestamp union of signal sequences A1 and B1 and remove the duplicates to obtain a union sequence T2;

A53. Calculate the value on the broken line corresponding to each timestamp in T2 to expand 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 timestamp, and the time range is R.

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