CN115882972B - Communication signal time-frequency difference extraction method, device and medium - Google Patents

Abstract

The present invention discloses a communication signal time-frequency difference extraction method, device and medium, belonging to the field of radio monitoring, including the following steps: performing FFT parallel calculation after segmenting signal data, calculating correlation, determining the preliminary value of the time-frequency difference, and completing the rough measurement of the time-frequency difference; then interpolating and refining the frequency difference within a known frequency range, obtaining a fine frequency difference value, and then according to the definition of the mutual fuzzy function, obtaining the precise value of the time-frequency difference within the selected time and frequency dimensions. The present invention realizes the high-precision time-frequency difference rapid extraction of communication signals.

Description

Communication signal time-frequency difference extraction method, device and medium

Technical Field

The present invention relates to the field of radio monitoring, and more specifically, to a method, device and medium for extracting time-frequency differences of communication signals.

Background Art

Traditional time difference extraction algorithms usually use correlation processing, which only considers the delay characteristics of the signal in the time domain. In scenarios where the relative motion between the observation platform and the observed object is not very fast, correlation processing is a good technical choice in engineering. However, for high-speed moving platforms, the difference in signals received by the primary and secondary stations is no longer reflected only in the delay characteristics of the time domain, but more in the frequency shift characteristics in the frequency domain, namely the Doppler frequency difference. When the Doppler frequency difference is large or even if the Doppler frequency difference is small but the accumulation time is long, the time difference correlation peak is difficult to form, resulting in the failure of the time difference measurement.

Summary of the invention

The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method, device and medium for extracting time-frequency differences of communication signals, thereby achieving high-precision and rapid extraction of time-frequency differences of communication signals.

The object of the present invention is achieved through the following solutions:

A communication signal time-frequency difference extraction method comprises the following steps:

After segmenting the signal data, perform FFT parallel calculation, calculate the correlation, determine the preliminary value of the time-frequency difference, and complete the rough measurement of the time-frequency difference;

The frequency difference within the known frequency range is then interpolated and refined to obtain a precise frequency difference value, and then the precise value of the time-frequency difference is obtained within the selected time and frequency dimensions according to the definition of the mutual fuzzy function.

Furthermore, the segmenting of the signal data and performing FFT parallel correlation calculation to calculate the correlation comprises the steps of:

Assume that the two received signal data are x ₁ (t) and x ₂ (t), then:

Where, s(t) is the complex envelope of the radiation source, τ is the relative time difference TDOA, _fd is the relative Doppler frequency difference FDOA, _fc is the signal carrier frequency, t is time, _n1 (t) is the noise in the first signal, and _n2 (t) is the noise in the second signal;

The mutual ambiguity function of the two signal data is defined as: Where T is the correlation accumulation time, and the mutual ambiguity function is a two-dimensional correlation spectrum obtained by Fourier transform of the conjugate point multiplication of the two-station signal. Let r _m (n) = X1(n)X2*(n+m), where X1(n) is the frequency domain data of the first signal, X2*(n+m) is the conjugate of the frequency domain data of the second signal, and n represents the nth point. The discretized mutual ambiguity function is expressed as follows:

Among them, m represents the offset in the time dimension, k represents the offset in the frequency dimension, and L represents the data length;

When CAF(m,k) reaches its maximum value, the exact value of the time-frequency difference can be determined.

Furthermore, the interpolating and refining the frequency difference within the known frequency range includes the steps of: interpolating and refining the frequency difference within the known frequency range using a CZT method.

Furthermore, the method of obtaining the precise value of the time-frequency difference in the selected time and frequency dimensions according to the definition of the mutual fuzzy function comprises the steps of:

Let n = pN + l, p = 0 ... M-1, l = 0 ... N-1, which means that the data of length L is divided into M data blocks, each data block contains N data; then:

By decimating CAF(m,k) by M times, we get:

In the formula, l represents a number between 0, 1, and N-1.

Furthermore, the FFT is Fourier transform.

Furthermore, the signal data is segmented into 1 segment, 10 segments, 50 segments, and 100 segments.

Furthermore, the signal data is two channels of related communication signal data.

Furthermore, it includes an application step of applying the communication signal time-frequency difference extraction method to a device or system that runs a frequency-frequency difference joint estimation algorithm.

A computer device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, the method described in any one of the above items is executed.

A readable storage medium stores a computer program, wherein the computer program is loaded by a processor and executes any of the above methods.

The beneficial effects of the present invention include:

The technical solution of the embodiment of the present invention greatly improves the speed of joint estimation of time-frequency difference and reduces the amount of calculation by changing the traditional two-dimensional time-frequency difference search algorithm to fast Fourier transform of the inner product of the signal, dividing a long segment of data into multiple segments for parallel processing. At the same time, the method of coarse search first and then fine search is adopted to improve the accuracy of time difference extraction.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

FIG1 is a schematic diagram of segmented calculation of time-frequency difference in an embodiment of the present invention.

DETAILED DESCRIPTION

All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and/or steps, can be combined and/or expanded or replaced in any manner.

In view of the problems in the background, the time difference Doppler frequency difference joint estimation algorithm is generally used at present. The joint estimation algorithm is based on the mutual fuzzy function, which is a generalization of the cross-correlation function. The mutual fuzzy function can be used to calculate the arrival time difference and arrival frequency difference of the radiation source target. This calculation can simultaneously give the arrival time difference and arrival frequency difference observed by two sensors. However, the time difference Doppler frequency difference joint estimation algorithm based on the mutual fuzzy function is theoretically feasible. After creative analysis and thinking, the inventor of the present invention believes that at least the following technical difficulties seriously affect the engineering application of the algorithm:

a) The original definition of the mutual fuzzy function is computationally inefficient;

b) It is necessary to perform two-dimensional search in the time difference dimension and the Doppler frequency difference dimension. Especially when the time difference and frequency difference are large, the search operation volume is huge;

c) In principle, the calculation of the mutual fuzzy function can only be performed after the original data of the accumulation time length is received, which affects the real-time processing;

d) The integration time and the time difference Doppler frequency difference search range are tightly coupled, and a larger integration time at least means a wider time difference search range;

e) To obtain a time difference with higher accuracy means a longer accumulation time, which will greatly increase the amount of calculation.

In order to solve the above technical problems, the technical solution of the present invention provides a communication signal time difference extraction technical solution, which aims to realize fast extraction of high-precision time-frequency difference of communication signals. Specifically, the concept of the technical solution of the present invention includes: (1) In view of the shortcomings of the existing time difference extraction method, which has large amount of calculation and poor real-time processing, the traditional two-dimensional time-frequency difference search algorithm is transformed into a fast Fourier transform of the inner product of the signal by appropriately transforming the calculation formula of the mutual fuzzy function, and a long segment of data is divided into multiple segments of data for parallel processing, which greatly improves the speed of joint estimation of time-frequency difference and reduces the amount of calculation; (2) In view of the problem that the existing time difference extraction accuracy is not high, the method of coarse search first and then fine search is adopted to gradually improve the time difference extraction accuracy.

In further conception, the technical solution of the present invention is divided into two steps: rough measurement and precise measurement of time-frequency difference when extracting time difference. The rough measurement mainly uses a segmented calculation method to quickly determine the preliminary value of the time-frequency difference. The precise measurement is guided by the rough measurement of the time-frequency difference value, and the CZT method is used to interpolate and refine the frequency difference within a known frequency range to obtain a precise frequency difference value. Then, according to the definition of the mutual fuzzy function, the precise value of the time-frequency difference is obtained within a certain time and frequency dimension. Specifically, it includes the following steps:

Assume that the two received signals are x ₁ (t) and x ₂ (t), then:

Where s(t) is the complex envelope of the radiation source, τ is the relative time difference TDOA, _fd is the relative Doppler frequency difference FDOA, _fc is the signal carrier frequency, t is time, _n1 (t) is the noise in the first signal, and _n2 (t) is the noise in the second signal.

The mutual ambiguity function of the two signals is defined as:

In the above formula, T is the correlation accumulation time, and the mutual ambiguity function is actually a two-dimensional correlation spectrum obtained by the Fourier transform of the conjugate point product of the two-station signals.

Let r _m (n) = X1 (n) X2 * (n + m)

X1(n) is the frequency domain data of the first signal, X2*(n+m) is the conjugate of the frequency domain data of the second signal, and n is;

The discretized mutual fuzzy function can be expressed as follows:

Among them, m represents the offset in the time dimension, k represents the offset in the frequency dimension, and L represents the data length.

Let n = pN + l, p = 0 ... M-1, l = 0 ... N-1, which means that the data of length L is divided into M data blocks, each of which contains N data.

By decimating the above data CAF(m,k) by M times, we can get:

In the formula, l represents a number between 0, 1, and N-1.

Therefore, the mutual ambiguity function value of the two signals can be obtained by first performing FFT operation on each data block of length N, and then adding the data of the same frequency point of multiple data blocks. The amount of calculation is reduced by M times compared to directly calculating the mutual ambiguity function value of L-point data.

In the process of processing actual signals, the technical solution of the present invention can greatly reduce the operation time, and is verified in positioning processing, thus better meeting the needs of engineering applications.

The application examples are as follows:

1)Signal modulation style: BPSK;

2) Signal carrier frequency: 20KHz;

3) Signal bandwidth: 25KHz;

4)Signal sampling rate: 96Ksps;

5) Data time length: 1s;

6) Signal time difference: 0.0047917s, signal frequency difference: 5KHz;

7) Main station signal signal-to-noise ratio: 10dB, neighbor station signal signal-to-noise ratio: -20dB.

The data is divided into 1 segment, 10 segments, 50 segments, and 100 segments for time-frequency difference extraction. The extracted signal time-frequency difference and the time used are shown in Table 1 below:

Table 1 Calculation results of time-frequency difference for different data segments

Number of data segments 1 10 50 100 Remark Time difference(s) 0.0047917 0.0047917 0.0047917 0.0047917 Frequency difference(KHz) 5 5 5 5 Processing time(s) 240.759 43.514 34.655 32.514

It can be seen from the above table that, under the condition of the same number of sampling points, both the segmented data processing and the non-segmented data processing algorithms can accurately estimate the time difference and frequency difference of the arriving signals. However, the time consumed by the segmented data processing is much less than that of the non-segmented data processing. As the number of segments increases to a certain extent, the processing time is not significantly reduced. Therefore, it is believed that the method of the present invention is very effective in the high-precision and rapid extraction of time-frequency differences.

It should be noted that within the scope of protection defined in the claims of the present invention, the following embodiments can be combined and/or expanded or replaced in any logical way from the above specific implementation methods, such as disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

Example 1

A communication signal time-frequency difference extraction method comprises the following steps:

After segmenting the signal data, perform FFT parallel calculation, calculate the correlation, determine the preliminary value of the time-frequency difference, and complete the rough measurement of the time-frequency difference;

The frequency difference within the known frequency range is then interpolated and refined to obtain a precise frequency difference value, and then the precise value of the time-frequency difference is obtained within the selected time and frequency dimensions according to the definition of the mutual fuzzy function.

Example 2

On the basis of Example 1, the signal data is segmented and then FFT parallel correlation calculation is performed to calculate the correlation, including the steps of:

Assume that the two received signal data are x ₁ (t) and x ₂ (t), then:

Where, s(t) is the complex envelope of the radiation source, τ is the relative time difference TDOA, _fd is the relative Doppler frequency difference FDOA, _fc is the signal carrier frequency, t is time, _n1 (t) is the noise in the first signal, and _n2 (t) is the noise in the second signal;

The mutual ambiguity function of the two signal data is defined as: Where T is the correlation accumulation time, and the mutual ambiguity function is a two-dimensional correlation spectrum obtained by Fourier transform of the conjugate point multiplication of the two-station signal. Let r _m (n) = X1(n)X2*(n+m), where X1(n) is the frequency domain data of the first signal, X2*(n+m) is the conjugate of the frequency domain data of the second signal, and n represents the nth point. The discretized mutual ambiguity function is expressed as follows:

Among them, m represents the offset in the time dimension, k represents the offset in the frequency dimension, and L represents the data length;

When CAF(m,k) reaches its maximum value, the exact value of the time-frequency difference can be determined.

Example 3

On the basis of Example 1, the interpolation and refinement of the frequency difference within the known frequency range includes the steps of: interpolating and refining the frequency difference within the known frequency range using the CZT method.

Example 4

On the basis of Example 2, the method of obtaining the precise value of the time-frequency difference in the selected time and frequency dimensions according to the definition of the mutual fuzzy function comprises the following steps:

Let n = pN + l, p = 0 ... M-1, l = 0 ... N-1, which means that the data of length L is divided into M data blocks, each data block contains N data; then:

By decimating CAF(m,k) by M times, we get:

In the formula, l represents a number between 0, 1, and N-1.

Example 5

Based on Example 1, the FFT is Fourier transform.

Example 6

Based on Example 1, the signal data is segmented into 1 segment, 10 segments, 50 segments, and 100 segments.

Example 7

Based on Example 1, the signal data is two channels of related communication signal data.

Example 8

On the basis of Example 1, the present invention includes an application step of applying the communication signal time-frequency difference extraction method to a device or system that runs a frequency-frequency difference joint estimation algorithm.

Embodiment 9

A computer device comprises a processor and a memory, wherein a computer program is stored in the memory. When the computer program is loaded by the processor and executed, the method described in any one of Embodiments 1 to 8 is performed.

Example 10

A readable storage medium stores a computer program, wherein the computer program is loaded by a processor and executes the method described in any one of Embodiments 1 to 8.

The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

According to one aspect of an embodiment of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program includes a computer instruction, and the computer instruction is stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method provided in the above various optional implementations.

As another aspect, an embodiment of the present invention further provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

The parts not involved in the present invention are the same as the prior art or can be implemented by using the prior art.

The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the application methods and principles disclosed in the present invention, and it is not limited to the method described in the above specific implementation mode of the present invention. Therefore, the method described above is only preferred and does not have a restrictive meaning.

In addition to the above examples, those skilled in the art may obtain other embodiments based on the above disclosure or by using the knowledge or technology in the relevant field to make changes. The features of each embodiment may be interchangeable or replaced. The changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention and should be within the scope of protection of the claims attached to the present invention.

Claims (7)

1. A method for extracting time-frequency difference of communication signals, characterized in that it comprises the following steps: After segmenting the signal data, perform FFT parallel calculation, calculate the correlation, determine the preliminary value of the time-frequency difference, and complete the rough measurement of the time-frequency difference; Then, the frequency difference within the known frequency range is interpolated and refined to obtain a precise frequency difference value, and then, according to the definition of the mutual fuzzy function, the precise value of the time-frequency difference is obtained within the selected time and frequency dimensions; The method of performing FFT parallel correlation calculation on the signal data after segmentation to calculate the correlation comprises the steps of: Assume that the two received signal data are x ₁ (t) and x ₂ (t), then: Where, s(t) is the complex envelope of the radiation source, τ is the relative time difference TDOA, _fd is the relative Doppler frequency difference FDOA, _fc is the signal carrier frequency, t is time, _n1 (t) is the noise in the first signal, and _n2 (t) is the noise in the second signal; The mutual ambiguity function of the two signal data is defined as: Where T is the correlation accumulation time, and the mutual ambiguity function is a two-dimensional correlation spectrum obtained by Fourier transform of the conjugate point multiplication of the two-station signal. Let r _m (n) = X1(n)X2*(n+m), where X1(n) is the frequency domain data of the first signal, X2*(n+m) is the conjugate of the frequency domain data of the second signal, and n represents the nth point. The discretized mutual ambiguity function is expressed as follows: Among them, m represents the offset in the time dimension, k represents the offset in the frequency dimension, and L represents the data length; When CAF(m,k) reaches its maximum value, the exact value of the time-frequency difference can be determined; The method of obtaining the precise value of the time-frequency difference in the selected time and frequency dimensions according to the definition of the mutual fuzzy function comprises the following steps: Let n = pN + l, p = 0 ... M-1, l = 0 ... N-1, which means that the data of length L is divided into M data blocks, each data block contains N data; then: By decimating CAF(m,k) by M times, we get: In the formula, l represents a number between 0, 1, and N-1; The signal data is segmented into 1 segment, 10 segments, 50 segments, and 100 segments. 2. The communication signal time-frequency difference extraction method according to claim 1 is characterized in that the interpolation and refinement of the frequency difference within a known frequency range includes the steps of: interpolating and refining the frequency difference within a known frequency range using a CZT method. 3. The communication signal time-frequency difference extraction method according to claim 1 is characterized in that the FFT is Fourier transform. 4. The communication signal time-frequency difference extraction method according to claim 1 is characterized in that the signal data is two-way related communication signal data. 5. The communication signal time-frequency difference extraction method according to claim 1 is characterized in that it includes an application step of applying the communication signal time-frequency difference extraction method to a device or system that runs a frequency difference joint estimation algorithm. 6. A computer device, characterized in that the computer device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor and executes the method according to any one of claims 1 to 5. 7. A readable storage medium, characterized in that a computer program is stored in the readable storage medium, and the computer program is loaded by a processor to execute the method according to any one of claims 1 to 5.