CN116186519A - Link4A and Link11 signal detection method, medium and device - Google Patents
Link4A and Link11 signal detection method, medium and device Download PDFInfo
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Abstract
The invention provides a Link4A and Link11 signal detection method, medium and device, the method includes the following steps: s10, electromagnetic signals are detected; s20, generating and enhancing a time-frequency matrix of the detected electromagnetic signals to obtain an enhanced time-frequency matrix; s30, extracting signal time domain features based on the enhanced time-frequency matrix; s40, extracting signal frequency domain features based on the enhanced time-frequency matrix; s50, based on the signal time domain features and the signal frequency domain features, the Link4A and Link11 signal detection is completed. The invention can finish the fine extraction of the signal time domain characteristics and the fine detection of the frequency domain characteristics, and solves the problem that the detected signal characteristics are difficult to acquire under the low signal-to-noise ratio.
Description
Technical Field
The invention relates to the technical field of electronic reconnaissance, in particular to a Link4A and Link11 signal detection method, medium and device.
Background
The tactical data chain is a mode for carrying out information interaction among various platforms in different scenes such as cross-domain combined combat, distributed combat and mosaic combat, is an information transmission and battlefield situation sharing system for transmitting tactical data such as voice, image and text, and Link4A and Link11 are one of the most mature general tactical data chains applied in western countries, and have important value for improving the informatization combat capability of army in how to realize efficient detection and identification of the tactical data chains.
At present, aiming at detection and identification of Link4A, link11 signals, the existing mainstream method mainly adopts single frequency domain analysis or adopts time-frequency characteristic combination to carry out characteristic library matching identification, but under low signal-to-noise ratio, signal characteristics are submerged in noise, the difficulty of extracting time domain characteristics and frequency domain characteristics of Link4A and Link11 data Link signals is high, and accurate detection of Link4A and Link11 signals is difficult;
in recent years, aiming at communication signals such as tactical data chains, radio stations, DEM (digital elevation model) and the like, a deep learning-based intelligent method is provided by a plurality of scholars, but the intelligent method is closely related to factors such as signal environment, data set quantity and quality, and the like, and has high algorithm complexity, poor generalization capability and inconvenience in engineering realization; therefore, how to design a method with low complexity and strong generalization capability to realize rapid and accurate detection of Link4A and Link11 signals under low signal-to-noise ratio has important significance.
Disclosure of Invention
The invention aims to provide a Link4A and Link11 signal detection method, medium and device, so as to finish the fine extraction of signal time domain features and the fine detection of frequency domain features, solve the problem that the detected signal features are difficult to acquire under low signal-to-noise ratio, and can realize the rapid and accurate detection of the Link4A and Link11 signals.
The invention provides a Link4A and Link11 signal detection method, which comprises the following steps:
s10, electromagnetic signals are detected;
s20, generating and enhancing a time-frequency matrix of the detected electromagnetic signals to obtain an enhanced time-frequency matrix;
s30, extracting signal time domain features based on the enhanced time-frequency matrix;
s40, extracting signal frequency domain features based on the enhanced time-frequency matrix;
s50, based on the signal time domain features and the signal frequency domain features, the Link4A and Link11 signal detection is completed.
Further, step S20 includes the following sub-steps:
s21, segmenting the detected electromagnetic signals, and performing FFT (fast Fourier transform) on each segment of electromagnetic signals to obtain a time-frequency matrix of the signals;
s22, setting a proper time sliding window size, and realizing time domain primary enhancement by accumulating time sliding windows of a time-frequency matrix of signals to obtain a time-frequency matrix after the time domain primary enhancement;
s23, setting a proper frequency sliding window size, and accumulating the time-frequency matrix after the primary time-domain enhancement on the frequency sliding window to realize the secondary frequency-domain enhancement, so as to obtain the time-frequency matrix after the secondary frequency-domain enhancement.
Further, the window size of the frequency sliding window must be larger than the window size of the time sliding window.
Further, the step S30 includes the following sub-steps
S31, comparing each row of the time-frequency matrix after frequency domain secondary enhancement with a decision threshold:
if the time domain feature matrix is larger than the judgment threshold, the time domain feature matrix is assigned to be larger than or equal to the judgment threshold;
if the time domain feature matrix is smaller than the judgment threshold, the time domain feature matrix is assigned as the number of lines;
s32, extracting the starting time and the ending time of the signal by traversing the domain feature matrix, thereby obtaining the signal duration; the signal duration is the signal time domain feature.
Further, the process of extracting the signal frequency domain features based on the enhanced time-frequency matrix in step S40 includes:
firstly, finding out a row maxRow and a column maxCol where the maximum value is located in a time-frequency matrix after secondary enhancement;
and then reasonably setting a threshold thre based on frequency resolution setting, searching the maximum value in the range of the rows maxRow+/-thre and maxCol+/-thre of the original time-frequency matrix, and finding out a signal frequency point corresponding to the energy peak value of the original time-frequency matrix, wherein the signal frequency point is the signal frequency domain characteristic.
Further, the method for completing Link4A and Link11 signal detection in step S50 based on the signal time domain features and the signal frequency domain features includes:
and comparing the extracted signal duration and the precisely detected signal frequency point with the frame length and the working frequency of Link4A and Link11 to finish the detection of the Link4A and Link11 signals.
In some embodiments, in step S10, the step of detecting electromagnetic signals refers to detecting electromagnetic signals in a 225-400 MHz frequency range equipped with different platforms and equipment in different domains including air, land and sea.
The invention also provides a computer terminal storage medium which stores computer terminal executable instructions for executing the Link4A and Link11 signal detection methods.
The present invention also provides a computing device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the Link4A and Link11 signal detection methods described above.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the invention can finish the fine extraction of the signal time domain characteristics and the fine detection of the frequency domain characteristics, and solves the problem that the detected signal characteristics are difficult to acquire under the low signal-to-noise ratio. Wherein:
1. the time domain primary enhancement and the frequency domain secondary enhancement greatly improve the difference between signals and noise in the time-frequency diagram, solve the problem of the difference between the signals and the noise under the low signal-to-noise ratio, and obtain a clear time-frequency diagram.
2. The self-adaptive decision threshold is set in the time domain, so that the extraction of the signal duration is simplified, the signal duration is convenient to be matched with the frame length of the Link4A, link11 signal, and the problems of high difficulty and high complexity of the signal extraction start-stop time in the traditional method are solved.
3. The energy peak value is judged through the time-frequency matrix after secondary enhancement on the frequency domain, so that the coarse detection of the signal frequency point is realized; and setting a reasonable threshold based on frequency resolution and searching an original time-frequency matrix, so that accurate detection of signal frequency points is realized, and the detection accuracy of Link4A, link signals is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly describe the drawings in the embodiments, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a Link4A and Link11 signal detection method in an embodiment of the present invention.
Fig. 2 is a flowchart of a secondary enhanced time-frequency matrix according to an embodiment of the present invention.
Fig. 3 is a flowchart of signal duration extraction based on a time-frequency matrix in an embodiment of the present invention.
Fig. 4 is a flowchart of Link4A, link11 detection based on time domain features in an embodiment of the present invention.
Fig. 5 is a flow chart of frequency matching between the enhanced time-frequency matrix and the original time-frequency matrix in the embodiment of the present invention.
Fig. 6 is a diagram of Link11 original time-frequency when the signal-to-noise ratio is-21 dB in the embodiment of the invention.
Fig. 7 is a time-frequency diagram after Link11 enhancement at a signal-to-noise ratio of-21 dB in an embodiment of the present invention.
Fig. 8 is a time-frequency diagram showing the Link4A signal according to an embodiment of the present invention.
Fig. 9 is a time-frequency diagram showing a Link11 signal according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, the present embodiment provides a Link4A and Link11 signal detection method, which includes the following steps:
s10, electromagnetic signals are detected;
the detection of electromagnetic signals refers to the detection of electromagnetic signals in the 225-400 MHz frequency range equipped by different platforms and equipment in different domains including air, land, sea surface and the like, and the electromagnetic signals comprise Link4A, link11 and other types of electromagnetic signals, so that the implementation of subsequent steps is facilitated.
S20, generating and enhancing a time-frequency matrix of the detected electromagnetic signals to obtain an enhanced time-frequency matrix;
the traditional STFT, WVD and time-frequency conversion method is used for generating a time-frequency diagram of a signal, and the hardware implementation complexity is high. And the traditional signal detection method based on the time-frequency diagram has poor detection effect under the condition of low signal-to-noise ratio, so that the time-frequency diagram secondary enhancement method provided by the embodiment can greatly enhance the difference between the signal and the noise, and is convenient for judging the starting time, the ending time and the frequency of the signal. As shown in fig. 2, step S20 includes the following sub-steps:
s21, segmenting the detected electromagnetic signals, performing FFT (fast Fourier transform) on each segment of electromagnetic signals to obtain a time-frequency matrix of the signals, wherein the time-frequency matrix is expressed as:
TFMatrix＝abs(fftshift(fft(signal,n))) (1)
wherein the rows of the time-frequency matrix represent time and the columns represent frequency; signal is the electromagnetic signal that detects, n is FFT point number, TFMatrix is the time-frequency matrix of the signal that FFT after transforming gets.
S22, setting a proper time sliding window size, realizing time domain primary enhancement by accumulating a time-frequency matrix of signals in the time sliding window, and obtaining a time-frequency matrix after the time domain primary enhancement, wherein the time-frequency matrix is expressed as:
TFAugMatrixRow＝sum(TFMatrix(i,j:j+winRow-1)) (2)
wherein, winRow is the window size according to the line time sliding window, i and j respectively represent the row and column subscripts of the time-frequency matrix, and TFAugMatrixRow is the time-frequency matrix after one time enhancement in the time domain. It should be noted that, too large or too small time sliding window arrangement may result in weak time domain enhancement effect of the signal, and the difference between the signal and the noise cannot be increased.
S23, setting a proper frequency sliding window size, and realizing frequency domain secondary enhancement by accumulating the time-frequency matrix after the primary enhancement of the time domain in the frequency sliding window to obtain the time-frequency matrix after the secondary enhancement of the frequency domain, wherein the time-frequency matrix is expressed as:
TFAugMatrix＝sum(TFAugMatrixRow(i,j:j+winCol-1)) (3)
wherein, winCol is the window size of a sliding window according to the column frequency, and TFAugMatrix is a time-frequency matrix after frequency domain secondary enhancement; it should be noted that, too large or too small a frequency sliding window may cause weak enhancement effect of the frequency domain of the signal, resulting in poor discrimination between the useful signal and the noise. In this embodiment, the window size of the frequency sliding window must be larger than the window size of the time sliding window, so that frequency superposition of useful signals on the time-frequency chart can be realized to a greater extent.
S30, extracting signal time domain features based on the enhanced time-frequency matrix; as shown in fig. 3, the method specifically comprises the following substeps:
s31, comparing each row of the time-frequency matrix after frequency domain secondary enhancement with a decision threshold:
if the time domain feature matrix is larger than the judgment threshold, the time domain feature matrix is assigned to be larger than or equal to the judgment threshold;
if the time domain feature matrix is smaller than the judgment threshold, the time domain feature matrix is assigned as the number of lines;
the process of step S31 is expressed as:
max Value＝max(max(TFAugMatrix)) (4)
thr＝δ×maxValue (5)
wherein maxValue is the maximum value of the time-frequency matrix after frequency domain secondary enhancement, rows is the matrix line number, delta is the set threshold factor, thr is the decision threshold, and TimeFeat is the time domain feature matrix.
S32, extracting the starting time and the ending time of the signal by traversing the domain feature matrix, thereby obtaining the signal duration; the signal duration is the signal time domain feature.
S40, extracting signal frequency domain features based on the enhanced time-frequency matrix;
as shown in fig. 5, first, finding the row maxRow and column maxCol where the maximum value is located in the time-frequency matrix after secondary enhancement, so as to implement coarse detection of the signal frequency points; and then reasonably setting a threshold thre based on frequency resolution, searching the maximum value in the range of the rows maxRow+/-thre and maxCol+/-thre of the original time-frequency matrix, thereby finding out the signal frequency point corresponding to the energy peak value of the original time-frequency matrix, realizing the accurate detection of the signal frequency point, and greatly reducing the detection error of the frequency point, namely the signal frequency domain characteristic. This process is expressed as:
mat＝TFMatrix((max Row-thre:max Row+thre-1,max Col-thre:max Col+thre-1)) (7)
max Final＝find(mat＝＝max(max(mat))) (8)
s50, the detection of the Link4A and Link11 signals is completed based on the signal time domain features and the signal frequency domain features. As shown in fig. 4, 6, 7, 8, and 9, specifically:
and comparing the extracted signal duration and the precisely detected signal frequency point with the frame length and the working frequency of Link4A and Link11 to finish the detection of the Link4A and Link11 signals. The Link4A signal is characterized in time domain by that 14ms control signal and 11.2ms response signal are alternately arranged, the time difference between the 14ms control signal and the 11.2ms response signal is random, but the interval between the 14ms control signal and the next 14ms control signal is fixed to be 32ms. But the actual detected signal is 13.8ms instead of 14ms, since 0.2ms at the end of the 14ms control signal is a non-keyed signal, and is temporarily unknown if it can be received at the time of detection, and thus is processed temporarily at 13.8 ms. Similarly, the 0.2ms at the end of the reply signal is also a non-keyed signal, and the actual detected signal is processed at 11 ms. The Link4A signal has a fixed start frame, and the control signal and the reply signal are the same, and are all 1.6 ms-long 1, 0-alternating synchronization header and 0.8ms all 0 protection pulse. The Link4A signal is characterized in the frequency domain by the working frequency band [225MHz,400MHz ], and the frequency interval of the working frequency point is 25kHz. The modulation mode is FSK modulation of 20KHz of working frequency point. Thus, it is shown on the time-frequency plot that the energy peaks alternate at two frequency sampling points separated by about 40 kHz.
The Link11 signal is formed by a plurality of data frames with the duration of 13.33ms or 22ms in the time domain, and the number of frames is determined by the working mode, the signal type and the information frame length. Therefore, the time domain characteristics of Link11 have a larger randomness, and only the characteristic that the frame length of the Link can be determined to meet (6+N) x L can be determined, wherein 6 is a 5-frame preamble and 1-frame phase reference frame shared by all signal systems, N is a random information frame of each signal, and L is the duration (13.33 ms or 22 ms) of each frame.
Furthermore, in some embodiments, a computer terminal storage medium is provided, storing computer terminal executable instructions for performing the Link4A and Link11 signal detection methods as described in the previous embodiments. Examples of the computer storage medium include magnetic storage media (e.g., floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.), or memories such as memory cards, ROMs, or RAMs, etc. The computer storage media may also be distributed over network-connected computer systems, such as stores for application programs.
Furthermore, in some embodiments, a computing device is presented comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the Link4A and Link11 signal detection methods as described in the previous embodiments. Examples of computing devices include PCs, tablets, smartphones, PDAs, etc.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A Link4A and Link11 signal detection method is characterized by comprising the following steps:
s10, electromagnetic signals are detected;
s20, generating and enhancing a time-frequency matrix of the detected electromagnetic signals to obtain an enhanced time-frequency matrix;
s30, extracting signal time domain features based on the enhanced time-frequency matrix;
s40, extracting signal frequency domain features based on the enhanced time-frequency matrix;
s50, based on the signal time domain features and the signal frequency domain features, the Link4A and Link11 signal detection is completed.
2. The Link4A and Link11 signal detection method according to claim 1, wherein step S20 includes the sub-steps of:
s21, segmenting the detected electromagnetic signals, and performing FFT (fast Fourier transform) on each segment of electromagnetic signals to obtain a time-frequency matrix of the signals;
s22, setting a proper time sliding window size, and realizing time domain primary enhancement by accumulating time sliding windows of a time-frequency matrix of signals to obtain a time-frequency matrix after the time domain primary enhancement;
s23, setting a proper frequency sliding window size, and accumulating the time-frequency matrix after the primary time-domain enhancement on the frequency sliding window to realize the secondary frequency-domain enhancement, so as to obtain the time-frequency matrix after the secondary frequency-domain enhancement.
3. The Link4A and Link11 signal detection method according to claim 2, wherein the window size of the frequency sliding window must be larger than the window size of the time sliding window.
4. The Link4A and Link11 signal detection method according to claim 2, wherein step S30 includes the sub-steps of
S31, comparing each row of the time-frequency matrix after frequency domain secondary enhancement with a decision threshold:
if the time domain feature matrix is larger than the judgment threshold, the time domain feature matrix is assigned to be larger than or equal to the judgment threshold;
if the time domain feature matrix is smaller than the judgment threshold, the time domain feature matrix is assigned as the number of lines;
s32, extracting the starting time and the ending time of the signal by traversing the domain feature matrix, thereby obtaining the signal duration; the signal duration is the signal time domain feature.
5. The Link4A and Link11 signal detection method according to claim 4, wherein the process of extracting signal frequency domain features based on the enhanced time-frequency matrix in step S40 includes:
firstly, finding out a row maxRow and a column maxCol where the maximum value is located in a time-frequency matrix after secondary enhancement;
and then reasonably setting a threshold thre based on frequency resolution setting, searching the maximum value in the range of the rows maxRow+/-thre and maxCol+/-thre of the original time-frequency matrix, and finding out a signal frequency point corresponding to the energy peak value of the original time-frequency matrix, wherein the signal frequency point is the signal frequency domain characteristic.
6. The Link4A and Link11 signal detection method according to claim 5, wherein the method for performing Link4A and Link11 signal detection in step S50 based on the signal time domain features and the signal frequency domain features includes:
and comparing the extracted signal duration and the precisely detected signal frequency point with the frame length and the working frequency of Link4A and Link11 to finish the detection of the Link4A and Link11 signals.
7. The Link4A and Link11 signal detection method according to claim 1, wherein in step S10, the detection of the electromagnetic signal refers to detection of electromagnetic signals in a 225-400 MHz frequency range equipped with different platforms and equipment in different domains including air, land and sea.
8. A computer terminal storage medium storing computer terminal executable instructions for performing the Link4A and Link11 signal detection method according to any one of claims 1-7.
9. A computing device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the Link4A and Link11 signal detection method according to any one of claims 1-7.
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| CN117310647A (en) * | 2023-11-28 | 2023-12-29 | 成都九洲迪飞科技有限责任公司 | FPGA-based time domain overlapping target identification signal rapid separation method |
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| CN117310647A (en) * | 2023-11-28 | 2023-12-29 | 成都九洲迪飞科技有限责任公司 | FPGA-based time domain overlapping target identification signal rapid separation method |
| CN117310647B (en) * | 2023-11-28 | 2024-03-22 | 成都九洲迪飞科技有限责任公司 | FPGA-based time domain overlapping target identification signal rapid separation method |
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