CN112332968B - Short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics - Google Patents
Short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics Download PDFInfo
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Abstract
The invention discloses a short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics, which utilizes high-resolution frequency spectrum information and combines time domain characteristics to realize the rapid identification and detection of various specific signals under broadband frequency spectrum. The method comprises the steps of firstly, receiving original frequency domain data with a preset frame length, and storing the original frequency domain data into a sliding window storage space; performing first-stage rapid capture and classification on the obtained original frequency domain data to obtain the number of signal types and the frequency domain classification of the signals in the current state; splitting the original frequency domain signal according to the category to obtain a plurality of independent single-type frequency domain signal data; and performing secondary fine feature identification on each independent single type of frequency domain signal data, judging the frequency domain signals meeting specific feature indexes, and returning an identification result. The invention has the advantages of short signal detection time, small calculated amount, easy engineering realization, engineering application and capability of forming automatic detection and control of the broadband specific signal.
Description
Technical Field
The invention relates to the field of short wave broadband signal identification, in particular to a short wave broadband automatic reconnaissance identification and control method based on multi-dimensional features.
Background
At present, due to the fact that signal types are various under the environment of a short wave channel, multipath fading and other interferences exist in signals transmitted through the short wave channel, after the signals are mixed, intercepted signals are more severely distorted, and the requirement of a modern reconnaissance system for specific signal broadband reconnaissance and identification is difficult to meet.
In addition, the traditional short-wave reconnaissance and identification are carried out on a certain type of short-wave signals in a narrow-band mode. One channel or fixed equipment is specially used for identifying one signal, the mode has extremely low efficiency and large equipment usage amount, and the identification efficiency is low, so that the quick and simultaneous identification of single equipment cannot be finished.
Disclosure of Invention
The invention provides a universal automatic broadband detection and identification algorithm aiming at multiple short-wave signals simultaneously, aiming at solving the problems that the conventional short-wave signal detection and identification method is low in efficiency, limited in application range and incapable of realizing broadband detection and identification.
Aiming at short wave frequency bands, the invention realizes the rapid identification and detection of specific signals by utilizing high-resolution frequency spectrum fine characteristics and combining time domain information, and identifies various types of signals by utilizing information such as frequency spectrum bandwidth, pilot frequency, burst period interval and the like.
A short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics comprises the following steps:
s1, receiving original frequency domain data with a preset frame length, storing the original frequency domain data into a sliding window storage space, and updating frequency spectrum data in the sliding window storage space in real time;
s2, performing first-stage rapid capture and classification on the original frequency domain data in the sliding window obtained in the step S1 to obtain the number of signal types and the frequency domain classification of the signals in the current state;
s3, splitting the original frequency domain signal according to the first-stage quick capture and classification result and the classification to obtain a plurality of independent single-type frequency domain signal data;
and S4, performing secondary fine feature recognition on each independent single type of frequency domain signal data, judging the frequency domain signals meeting specific feature indexes, and returning recognition results.
Receiving the original frequency domain data with the preset frame length in step S1 specifically includes: and setting the frequency spectrum resolution of the signal to be not lower than a specific resolution, and setting the carrier frequency of the sampling signal to cover the whole short wave frequency band.
The first-stage fast capturing and classifying in step S2 specifically includes: carrying out average processing on the frequency spectrum data stored in the sliding window storage space to obtain accumulated frequency spectrum data; calculating a noise floor of the accumulated spectrum data; signal detection is completed by utilizing the accumulated frequency spectrum and the noise bottom information, and the number, frequency point, bandwidth, signal-to-noise ratio and amplitude information of signals are obtained; carrying out burst signal detection on the frequency spectrum data of the sliding window storage space to obtain an accumulated pulse period parameter;
the burst signal detection is to detect the peak value of the frequency spectrum in the frequency domain, the judgment is based on the fact that the peak value of the frequency spectrum is larger than a preset threshold, the value of the peak spectral line is larger than the difference or the ratio of the spectral lines at the preset intervals at the left side and the right side of the peak spectral line, pulse signals meeting the two conditions are accumulated and counted once, the detection is continuously carried out in the sliding process of a sliding window, the occurrence time points of the pulses meeting the conditions are recorded, and pulse period parameters are obtained through the difference of the time points.
The calculating of the noise floor of the accumulated spectrum data specifically comprises the following steps:
s201, performing open-loop operation on the accumulated frequency spectrum x, and performing closed-loop operation on an open-loop operation result to obtain a processing result xco;
s202, performing closed-loop operation on the accumulated frequency spectrum x, and performing open-loop operation on a closed-loop operation result to obtain a processing result xoc;
s203, performing an average operation on the processing results of steps S201 and S202, where xmean is (xco + xoc)/2;
s204, performing two open-loop operations on xmean to obtain a noise background xoo of the current spectrum data;
the open-loop calculation means that the calculation result does not participate in feedback iteration, and the closed-loop calculation means that the calculation result participates in feedback iteration.
The open loop operation has the calculation formula as follows:
wherein m iskRepresenting the open-loop calculation of the sliding window data of the k-th frame, diCalculating a weight coefficient, x, for open loopiIs the ith cumulative spectrum value in the sliding window, and M is the length of the sliding window.
The closed loop operation has the calculation formula as follows:
wherein m isiOpen-loop calculation of the temporally closest i-th frame sliding window data, cjCalculating a weight coefficient, n, for the closed loopjAnd performing sliding calculation on all sliding window data to obtain a final closed-loop operation result xco, wherein the closed-loop operation result is the result of the closed-loop operation of the j frame before the i frame.
The signal detection is completed by using the accumulated frequency spectrum and the noise floor information, and the method specifically comprises the following steps:
s211, comparing the accumulated frequency spectrum x with a noise bottom xoo, and detecting spectral lines higher than a noise bottom detection threshold minSigThd, wherein the detection threshold minSigThd is set according to task requirements;
s212, merging the spectral lines detected in the step S211 according to the minimum signal interval minSigDist, wherein a detection threshold minSigDist is set according to task requirements;
the merging processing refers to that a plurality of spectral lines with time intervals lower than minSigDist are considered to belong to the same system signal, and the maximum spectral line is selected from the plurality of spectral lines or the plurality of spectral lines are summed.
And S213, calculating the frequency point, bandwidth, signal-to-noise ratio and amplitude information of the signals detected in the step S212, and counting the number of the signals. The sampling rate is fs, the frequency point value of the frequency domain signal obtained by N-point Fourier transform follows the stepping rule of [ -fs/2: fs/N: fs/2-fs/N ], frequency point information is obtained by calculating the position of a spectral line, the signal bandwidth is obtained by calculating the difference value between the frequency point of the stop spectral line of the signal with the same system and the frequency point of the initial spectral line of the signal, the difference value between the spectral line power of the detected signal and the bottom noise power is calculated, the ratio of the difference value to the bottom noise is taken as the signal-to-noise ratio, and the amplitude information is the frequency domain amplitude value of the signal spectral line.
The second-stage fine feature identification in step S4 is, for the multi-carrier modulation transmission system communication signal with the multi-tone feature, specifically:
s401, firstly, detecting a signal with a signal bandwidth meeting a specific interval range;
s402, a specific number of spectral lines exist in a signal spectrum, and signals with specific bandwidths exist at specific frequencies at equal intervals on the left or right of the spectral lines;
at S403, a signal satisfying both the conditions at S401 and S402 is detected.
The second-stage fine feature identification in step S4 is, for the narrowband pulse transmission system communication signal, specifically:
s411, firstly, detecting a signal with a signal bandwidth smaller than a specific threshold;
s412, the signal has a pulse period;
in step S413, a signal satisfying the conditions in steps S411 and S412 is detected.
The second-stage fine feature identification in step S4 is specifically, for the communication signal of the multi-tone modulation half-duplex transmission system:
s421, firstly, detecting the signal with the signal bandwidth satisfying the specific interval range;
s422, a specific number of spectral lines exist in the signal spectrum, and the spectral line spacing is a specific value;
s423, the signal has a pulse period, and the pulse period is a specific value;
s424, a signal satisfying the conditions in steps S421, S422, and S423 is detected.
The second-stage fine feature identification in step S4 is specifically, for a communication signal of a multi-tone modulation full-duplex transmission system:
s431, first detecting a signal whose signal bandwidth satisfies a specific interval range;
s432, a pulse period exists in the signal, and the pulse period is a specific value;
at S433, a signal satisfying both the conditions at steps S431 and S432 is detected.
The second-stage fine feature identification in step S4 is specifically, for a communication signal of a multiple tone frequency shift keying transmission system:
s441, firstly, detecting a signal with a signal bandwidth meeting a specific interval range;
s442, a specific number of envelopes exist on the signal spectrum;
in step S443, a signal satisfying the conditions in steps S441 and S442 is detected.
The returning of the recognition result in step S4 specifically includes: and reporting the identification result, wherein the identification result comprises a specific signal pattern, frequency, signal-to-noise ratio, amplitude information and the like.
The invention has the beneficial effects that:
1) the invention can quickly detect specific signals in a large amount of monitoring frequency spectrum signals and realize the detection of the specific signals in a frequency domain, has short signal detection time, small calculated amount and easy engineering realization, can be applied in engineering and forms the automatic reconnaissance and control capability of the broadband specific signals;
2) the invention changes the original method that only can search signals from energy, direction and bandwidth parameters, realizes automatic broadband search of the signals by using multidimensional parameters and protocol characteristics of the signals, and solves the problem that the signals are difficult to find in a complex electromagnetic environment; the form filtering method is utilized to realize the signal detection of the self-adaptive threshold, and the reliable detection of the signal under the wide-open channel is completed; the method realizes automatic guidance and control of the counterweight target signals by using specific signal type detection, forms a target situation, and synchronously records the reconnaissance result.
Drawings
FIG. 1 is a flow chart of the short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics;
FIG. 2 is a flow chart of a spectral noise floor calculation process;
fig. 3 is a flow chart of a wideband signal detection process.
Detailed Description
For a better understanding of the present disclosure, an example is given here.
A short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics, FIG. 1 is a flow chart of the short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics, the invention includes the following steps:
s1, receiving original frequency domain data with a preset frame length, storing the original frequency domain data into a sliding window storage space, and updating frequency spectrum data in the sliding window storage space in real time;
s2, performing first-stage rapid capture and classification on the original frequency domain data in the sliding window obtained in the step S1 to obtain the number of signal types and the frequency domain classification of the signals in the current state;
s3, splitting the original frequency domain signal according to the first-stage quick capture and classification result and the classification to obtain a plurality of independent single-type frequency domain signal data;
and S4, performing secondary fine feature recognition on each independent single type of frequency domain signal data, judging the frequency domain signals meeting specific feature indexes, and returning recognition results.
Receiving the original frequency domain data with the preset frame length in step S1 specifically includes: and setting the frequency spectrum resolution of the signal to be not lower than a specific resolution, and setting the carrier frequency of the sampling signal to cover the whole short wave frequency band.
The first-stage fast capturing and classifying in step S2 specifically includes: carrying out average processing on the frequency spectrum data stored in the sliding window storage space to obtain accumulated frequency spectrum data; calculating a noise floor of the accumulated spectrum data; signal detection is completed by utilizing the accumulated frequency spectrum and the noise bottom information, and the number, frequency point, bandwidth, signal-to-noise ratio and amplitude information of signals are obtained; carrying out burst signal detection on the frequency spectrum data of the sliding window storage space to obtain an accumulated pulse period parameter;
the burst signal detection is to detect the peak value of the frequency spectrum in the frequency domain, the judgment is based on the fact that the peak value of the frequency spectrum is larger than a preset threshold, the value of the peak spectral line is larger than the difference or the ratio of the spectral lines at the preset intervals at the left side and the right side of the peak spectral line, pulse signals meeting the two conditions are accumulated and counted once, the detection is continuously carried out in the sliding process of a sliding window, the occurrence time points of the pulses meeting the conditions are recorded, and pulse period parameters are obtained through the difference of the time points.
Fig. 2 is a flow chart of spectrum noise floor calculation processing, and specifically, the method includes:
s201, performing open-loop operation on the accumulated frequency spectrum x, and performing closed-loop operation on an open-loop operation result to obtain a processing result xco;
s202, performing closed-loop operation on the accumulated frequency spectrum x, and performing open-loop operation on a closed-loop operation result to obtain a processing result xoc;
s203, performing an average operation on the processing results of steps S201 and S202, where xmean is (xco + xoc)/2;
s204, performing two open-loop operations on xmean to obtain a noise background xoo of the current spectrum data;
the open-loop calculation means that the calculation result does not participate in feedback iteration, and the closed-loop calculation means that the calculation result participates in feedback iteration.
The open loop operation has the calculation formula as follows:
wherein m iskRepresenting the open-loop calculation of the sliding window data of the k-th frame, diCalculating a weight coefficient, x, for open loopiIs the ith cumulative spectrum value in the sliding window, and M is the length of the sliding window.
The closed loop operation has the calculation formula as follows:
wherein m isiOpen-loop calculation of the temporally closest i-th frame sliding window data, cjCalculating a weight coefficient, n, for the closed loopjAnd performing sliding calculation on all sliding window data to obtain a final closed-loop operation result xco, wherein the closed-loop operation result is the result of the closed-loop operation of the j frame before the i frame.
The signal detection is completed by using the accumulated frequency spectrum and the noise floor information, and the method specifically comprises the following steps:
s211, comparing the accumulated frequency spectrum x with a noise bottom xoo, and detecting spectral lines higher than a noise bottom detection threshold minSigThd, wherein the detection threshold minSigThd is set according to task requirements;
s212, merging the spectral lines detected in the step S211 according to the minimum signal interval minSigDist, wherein a detection threshold minSigDist is set according to task requirements;
the merging processing refers to that a plurality of spectral lines with time intervals lower than minSigDist are considered to belong to the same system signal, and the maximum spectral line is selected from the plurality of spectral lines or the plurality of spectral lines are summed.
And S213, calculating the frequency point, bandwidth, signal-to-noise ratio and amplitude information of the signals detected in the step S212, and counting the number of the signals. The sampling rate is fs, the frequency point value of the frequency domain signal obtained by N-point Fourier transform follows the stepping rule of [ -fs/2: fs/N: fs/2-fs/N ], frequency point information is obtained by calculating the position of a spectral line, the signal bandwidth is obtained by calculating the difference value between the frequency point of the stop spectral line of the signal with the same system and the frequency point of the initial spectral line of the signal, the difference value between the spectral line power of the detected signal and the bottom noise power is calculated, the ratio of the difference value to the bottom noise is taken as the signal-to-noise ratio, and the amplitude information is the frequency domain amplitude value of the signal spectral line.
Fig. 3 is a flow chart of a wideband signal detection process. The second-stage fine feature identification in step S4 is, for the multi-carrier modulation transmission communication system communication signal with the multi-tone feature, specifically:
s401, firstly, detecting a signal with a signal bandwidth meeting a specific interval range;
s402, a certain number of spectral lines exist in a signal frequency spectrum, and signals with specific bandwidths exist at specific frequencies at equal intervals on the left or right of the spectral lines;
at S403, a signal satisfying both the conditions at S401 and S402 is detected.
The second-stage fine feature identification in step S4 is, for the narrowband pulse transmission system communication signal, specifically:
s411, firstly, detecting a signal with a signal bandwidth smaller than a specific threshold;
s412, the signal has a pulse period;
in step S413, a signal satisfying the conditions in steps S411 and S412 is detected.
The second-stage fine feature identification in step S4 is specifically, for the communication signal of the multi-tone modulation half-duplex transmission system:
s421, firstly, detecting the signal with the signal bandwidth satisfying the specific interval range;
s422, a specific number of spectral lines exist in the signal spectrum, and the spectral line spacing is a specific value;
s423, the signal has a pulse period, and the pulse period is a specific value;
s424, a signal satisfying the conditions in steps S421, S422, and S423 is detected.
The second-stage fine feature identification in step S4 is specifically, for a communication signal of a multi-tone modulation full-duplex transmission system:
s431, first detecting a signal whose signal bandwidth satisfies a specific interval range;
s432, a pulse period exists in the signal, and the pulse period is a specific value;
at S433, a signal satisfying both the conditions at steps S431 and S432 is detected.
The second-stage fine feature identification in step S4 is specifically, for a communication signal of a multiple tone frequency shift keying transmission system:
s441, firstly, detecting a signal with a signal bandwidth meeting a specific interval range;
s442, a specific number of envelopes exist on the signal spectrum;
in step S443, a signal satisfying the conditions in steps S441 and S442 is detected.
The returning of the recognition result in step S4 specifically includes: and reporting the identification result, wherein the identification result comprises a specific signal pattern, frequency, signal-to-noise ratio, amplitude information and the like.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (6)
1. A short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics is characterized by comprising the following steps:
s1, receiving original frequency domain data with a preset frame length, storing the original frequency domain data into a sliding window storage space, and updating frequency spectrum data in the sliding window storage space in real time;
s2, performing first-stage rapid capture and classification on the original frequency domain data in the sliding window obtained in the step S1 to obtain the number of signal types and the frequency domain classification of the signals in the current state;
s3, splitting the original frequency domain signal according to the first-stage quick capture and classification result and the classification to obtain a plurality of independent single-type frequency domain signal data;
s4, performing secondary fine feature recognition on each independent single type of frequency domain signal data, judging the frequency domain signals meeting specific feature indexes, and returning recognition results;
receiving the original frequency domain data with the preset frame length in step S1 specifically includes: setting the frequency spectrum resolution of the signal to be not lower than a specific resolution, and setting the carrier frequency of the sampling signal to cover the whole short wave frequency band;
the first-stage fast capturing and classifying in step S2 specifically includes: carrying out average processing on the frequency spectrum data stored in the sliding window storage space to obtain accumulated frequency spectrum data; calculating a noise floor of the accumulated spectrum data; signal detection is completed by utilizing the accumulated frequency spectrum and the noise bottom information, and the number, frequency point, bandwidth, signal-to-noise ratio and amplitude information of signals are obtained; carrying out burst signal detection on the frequency spectrum data of the sliding window storage space to obtain an accumulated pulse period parameter;
the burst signal detection is to detect a spectrum peak value in a frequency domain, and the judgment is based on the fact that the spectrum peak value is larger than a preset threshold, the value of the peak spectral line is larger than the difference or the ratio of the spectrum spectral lines at preset intervals at the left side and the right side of the peak spectral line, pulse signals meeting the two conditions are accumulated and counted once, detection is continuously performed in the sliding process of a sliding window, the occurrence time points of the pulses meeting the conditions are recorded, and pulse period parameters are obtained through the difference of the time points;
the calculating of the noise floor of the accumulated spectrum data specifically comprises the following steps:
s201, performing open-loop operation on the accumulated frequency spectrum x, and performing closed-loop operation on an open-loop operation result to obtain a processing result xco;
s202, performing closed-loop operation on the accumulated frequency spectrum x, and performing open-loop operation on a closed-loop operation result to obtain a processing result xoc;
s203, performing an average operation on the processing results of steps S201 and S202, where xmean is (xco + xoc)/2;
s204, performing two open-loop operations on xmean to obtain a noise background xoo of the current spectrum data;
the open loop operation has the calculation formula as follows:
wherein m iskRepresenting the open-loop calculation of the sliding window data of the k-th frame, diCalculating a weight coefficient, x, for open loopiIs the ith cumulative frequency spectrum value in the sliding window, and M is the length of the sliding window;
the closed loop operation has the calculation formula as follows:
wherein m isiOpen-loop calculation of the temporally closest i-th frame sliding window data, cjCalculating a weight coefficient, n, for the closed loopjAnd performing sliding calculation on all sliding window data to obtain a final closed-loop operation result xco, wherein the closed-loop operation result is the result of the closed-loop operation of the j frame before the i frame.
2. The short-wave broadband automatic reconnaissance, identification and control method based on multidimensional characteristics as claimed in claim 1, wherein the signal detection is accomplished by using accumulated frequency spectrum and noise floor information, specifically:
s211, comparing the accumulated frequency spectrum x with a noise bottom xoo, and detecting spectral lines higher than a noise bottom detection threshold minSigThd, wherein the detection threshold minSigThd is set according to task requirements;
s212, merging the spectral lines detected in the step S211 according to the minimum signal interval minSigDist, wherein a detection threshold minSigDist is set according to task requirements;
the merging processing refers to that a plurality of spectral lines with time intervals lower than minSigDist are considered to belong to the same system signal, and the maximum spectral line is selected from the plurality of spectral lines or the plurality of spectral lines are summed;
s213, calculating the frequency point, bandwidth, signal-to-noise ratio and amplitude information of the signal detected in the step S212, and counting the number of the signal; the sampling rate is fs, the frequency point value of the frequency domain signal obtained by N-point Fourier transform follows the stepping rule of [ -fs/2: fs/N: fs/2-fs/N ], frequency point information is obtained by calculating the position of a spectral line, the signal bandwidth is obtained by calculating the difference value between the frequency point of the stop spectral line of the signal with the same system and the frequency point of the initial spectral line of the signal, the difference value between the spectral line power of the detected signal and the bottom noise power is calculated, the ratio of the difference value to the bottom noise is taken as the signal-to-noise ratio, and the amplitude information is the frequency domain amplitude value of the signal spectral line.
3. The short-wave broadband automatic reconnaissance, identification and control method based on multidimensional characteristics as claimed in claim 1, wherein the second-stage fine characteristic identification in step S4 is specifically, for the multi-tone characteristic multi-carrier modulation transmission system communication signal:
s401, firstly, detecting a signal with a signal bandwidth meeting a specific interval range;
s402, a specific number of spectral lines exist in a signal spectrum, and signals with specific bandwidths exist at positions with specific frequencies spaced left or right by the spectral lines;
at S403, a signal satisfying both the conditions at S401 and S402 is detected.
4. The short-wave broadband automatic reconnaissance, identification and control method based on multidimensional characteristics as claimed in claim 1, wherein the second-stage fine characteristic identification in step S4 is specifically for the communication signals of the narrowband pulse transmission system:
s411, firstly, detecting a signal with a signal bandwidth smaller than a specific threshold;
s412, the signal has a pulse period;
in step S413, a signal satisfying the conditions in steps S411 and S412 is detected.
5. The short-wave broadband automatic reconnaissance, identification and control method based on multidimensional characteristics as claimed in claim 1, wherein the second-stage fine characteristic identification in step S4 is specifically for communication signals of a multi-tone modulation half-duplex transmission system:
s421, firstly, detecting the signal with the signal bandwidth satisfying the specific interval range;
s422, a specific number of spectral lines exist in the signal spectrum, and the spectral line spacing is a specific value;
s423, the signal has a pulse period, and the pulse period is a specific value;
s424, a signal satisfying the conditions in steps S421, S422, and S423 is detected.
6. The short-wave broadband automatic reconnaissance, identification and control method based on multidimensional characteristics as claimed in claim 1, wherein the second-stage fine characteristic identification in step S4 is specifically for communication signals of a multi-tone modulation full-duplex transmission system:
s431, first detecting a signal whose signal bandwidth satisfies a specific interval range;
s432, a pulse period exists in the signal, and the pulse period is a specific value;
at S433, a signal satisfying both the conditions at steps S431 and S432 is detected.
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| CN105162531A (en) * | 2015-08-14 | 2015-12-16 | 成都中安频谱科技有限公司 | Ultra-short wave broadband monitoring system and method |
| CN105207965A (en) * | 2015-08-14 | 2015-12-30 | 成都中安频谱科技有限公司 | Automatic VHF/UHF frequency range modulation identification method |
| CN109981186A (en) * | 2019-04-10 | 2019-07-05 | 成都华日通讯技术有限公司 | Ultrashort wave full frequency band signal sorting method |
| CN111814777A (en) * | 2020-09-15 | 2020-10-23 | 湖南国科锐承电子科技有限公司 | Modulation pattern recognition method based on characteristic quantity grading |
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