CN114296032A - Real-time complex signal identification and parameter estimation method based on FPGA - Google Patents
Real-time complex signal identification and parameter estimation method based on FPGA Download PDFInfo
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Abstract
The invention discloses a real-time complex signal identification and parameter estimation method based on an FPGA (field programmable gate array). the pulse signal is detected by utilizing a digital channelization technology. When the pulse is detected, the intermediate frequency data of the signal is subjected to tracking filtering processing and then input into an intra-pulse analysis FPGA module for water treatment. Firstly, an intra-pulse analysis module calculates signal amplitude of intermediate frequency data input by flowing water, solves signal phase and phase ambiguity, and performs sliding window accumulation on the signal amplitude to obtain the rise time and the fall time of the signal; and carrying out multiple differential operations on the signal phase to obtain real-time instantaneous frequency. And counting the change conditions of instantaneous frequencies at different moments to obtain the characteristics of the carrier frequency, the bandwidth, the modulation frequency, the code element change and the like of the signal, identifying the modulation type of the pulse according to the statistical values of various characteristics after the pulse is ended, and outputting characteristic parameters. And outputting and supplementing the pulse modulation type and the characteristic parameters to the conventional PDW description information in real time, and carrying out FSK signal coding to output complete PDW information.
Description
Technical Field
The invention belongs to the field of electronic reconnaissance, and particularly relates to a real-time complex signal identification and parameter estimation method based on an FPGA.
Background
The modern electromagnetic signal environment is unprecedentedly complex, and the signal environment faced by electronic reconnaissance can be qualitatively summarized as follows: 1) the number of radiation sources is increasing. 2) The radiation source system is many, and radar signal's waveform is complicated and changeable. 3) The range over which the operating frequencies of the radiation sources overlap each other is increasingly wide. 4) More and more signals are present in the same frequency range and more signals are present at the same time. 5) The signals radiated by guided, fire-controlled radar are increasing, so that radar systems must intercept and identify such serious, imminent threat signals with greater efficiency. Sorting and identifying radar signals according to conventional PDW parameters has become very difficult. Therefore, the new generation of electronic countermeasure system needs to have strong radar signal intra-pulse feature analysis capability. Common intra-pulse feature extraction methods mainly include a time domain analysis method, a frequency domain analysis method, a modulation domain analysis method, a spectrum correlation method, a time-frequency domain analysis method and the like, but because the extracted IF features are two-dimensional graphs, manual judgment is easy, automatic classification and identification of a machine are difficult, radar pulse density is high, and real intra-pulse identification is very difficult, the method has not been widely applied to engineering.
Disclosure of Invention
The invention aims to provide a real-time complex modulation signal identification and parameter estimation method based on an FPGA (field programmable gate array). the method mainly identifies conventional pulses, two-phase codes, four-phase codes, linear frequency modulation, nonlinear frequency modulation signals, FSK signals and FSK + LFM/NLFM/BPSK/QPSK combined signals in real time, and simultaneously gives characteristic parameters of the modulation signals, including pulse rising time, falling time, carrier frequency, bandwidth, modulation frequency, code rate and code element information. The problem of slow speed of modulation recognition and parameter estimation under modern complex electromagnetic environment is solved, and besides conventional PDW parameters, a new characteristic is provided for radar sorting.
The technical solution for realizing the invention is as follows: a real-time complex signal identification and parameter estimation method based on FPGA comprises the following steps:
step 1, carrying out real-time digital sampling on the intermediate frequency signal to obtain intermediate frequency data, detecting the signal in the intermediate frequency data by using a digital channelization technology to obtain conventional PDW information of the signal, and turning to step 2.
And 2, receiving sampled intermediate frequency data by flowing water, sequentially performing tracking filtering and data rate reduction processing on the intermediate frequency data according to the conventional signal PDW information to obtain data after tracking filtering, and simultaneously turning to the step 3 and the step 4.
And 3, receiving, tracking and filtering the data by the running water, calculating the signal amplitude, performing sliding window processing on the signal amplitude, calculating the rising time and the falling time of the signal, and turning to the step 13.
And 4, receiving, tracking and filtering the data by the flowing water, solving the signal phase in the data, deblurring the signal phase to obtain the instantaneous phase of the signal, and turning to the step 5.
And 5, performing multiple differential operation on the instantaneous phase of the signal to obtain the smoothed instantaneous frequency, and turning to step 6.
And 6, carrying out feature statistics on the instantaneous frequency, judging and recording the mutation condition of the instantaneous frequency, carrying out statistics on the maximum frequency and the minimum frequency of the instantaneous frequency under the condition of no mutation, accumulating the instantaneous frequency value under the condition of no mutation in real time, and turning to the step 7.
And 7, taking the instantaneous frequency value of the interval K point under the condition of no mutation, calculating a linear fitting result of the instantaneous frequency value in real time, and simultaneously turning to the step 8 and the step 10.
And 8, at the signal ending moment, calculating a carrier frequency value according to the real-time accumulated instantaneous frequency value and the accumulated length, fitting the bandwidth according to the counted maximum frequency and minimum frequency to obtain a signal bandwidth value, and turning to the step 9.
And 9, performing frequency correction on the recorded instantaneous frequency mutation situation according to the carrier frequency value at the signal ending moment, simultaneously removing false mutation information, calculating the width of a code element, and turning to the step 11.
And step 10, generating a final frequency modulation slope value according to the instantaneous frequency linear fitting result calculated in real time at the signal ending moment, and turning to step 11.
And step 11, judging the signal modulation type according to the obtained carrier frequency, bandwidth, frequency modulation slope and instantaneous frequency mutation condition after frequency correction, and turning to step 12.
And step 12, demodulating the code element information of the phase modulation signal according to the signal modulation type, the code element width and the signal length, and turning to step 13.
And step 13, fusing the conventional PDW information, the signal modulation type, the signal carrier frequency, the bandwidth, the frequency modulation slope, the code element width, the code element information, the signal rising time and the signal falling time, inputting the fusion result into the FSK coding module, and outputting the complete PDW information in real time.
Compared with the prior art, the invention has the remarkable advantages that:
(1) the method is realized by adopting FPGA engineering, is executed in real time in a flowing mode, can output results after pulse receiving is finished, and solves the problems of slow modulation identification and parameter estimation speed in the modern complex electromagnetic environment.
(2) The intermediate frequency data is subjected to tracking filtering processing, so that the edge characteristics of the signal are reserved, the signal to noise ratio required by intra-pulse identification is ensured, and the resolution of intra-pulse characteristic parameter measurement is improved.
(3) On the basis of identifying and extracting the conventional intra-pulse characteristics, the FSK coding can be carried out on the conventional PDW information to obtain the result of a complex modulation signal.
Drawings
FIG. 1 is a flow chart of a real-time complex signal identification and parameter estimation method based on FPGA according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present invention.
With reference to fig. 1, the real-time complex signal identification and parameter estimation method based on FPGA of the present invention includes the following steps:
step 1, carrying out real-time digital sampling on the intermediate frequency signal to obtain intermediate frequency data, detecting the signal in the intermediate frequency data by using a digital channelization technology to obtain conventional PDW information of the signal, and turning to step 2.
And 2, receiving sampled intermediate frequency data by flowing water, sequentially performing tracking filtering and data rate reduction processing on the intermediate frequency data according to the conventional signal PDW information to obtain data after tracking filtering, and simultaneously turning to the step 3 and the step 4.
And 2.1, tracking and filtering the input intermediate frequency data according to the signal detection result.
And 2.2, performing data rate reduction processing on the data after tracking and filtering, and outputting an intermediate frequency data rate suitable for FPGA processing under the condition of meeting the video resolution.
And 3, receiving, tracking and filtering the data by the running water, calculating the signal amplitude, performing sliding window processing on the signal amplitude, calculating the rising time and the falling time of the signal, and turning to the step 13.
And 3.1, calculating the signal amplitude in the data after tracking and filtering, performing sliding window accumulation on the signal amplitude in order to reduce the random influence of noise, and judging and obtaining the maximum amplitude of the signal.
And 3.2, determining an amplitude threshold Start1 (maximum amplitude 30%) and a Start2 (maximum amplitude 70%) of the signal rising starting time according to the maximum amplitude of the signal, recording the time when the signal meets the amplitude threshold Start1 and Start2, and recording the time as the rising time.
And 3.3, determining an amplitude threshold End1 (maximum amplitude 70%) and an End2 (maximum amplitude 30%) at the falling starting moment of the signal according to the maximum amplitude of the signal, and recording the time when the signal meets the amplitude thresholds End1 and End2 as the falling time.
And 4, receiving, tracking and filtering the data by the flowing water, solving the signal phase in the data, deblurring the signal phase to obtain the instantaneous phase of the signal, and turning to the step 5.
And 4.1, solving the signal phase theta (n) through arctangent, and unifying the signal phase to [ -pi, + pi ] through the signs of the real part and the imaginary part of the signal.
And 4.2, determining a deblurring threshold T according to the signal frequency range, wherein the signal frequency range is obtained through tracking filtering or channelization.
Step 4.3, according to the ambiguity resolution threshold T, carrying out ambiguity resolution processing on the signal phase theta (theta) to obtain a signal instantaneous phase theta' (n),
θ′(n)＝θ(n)+C(n)
where c (n) represents the phase correction value for the current sample point.
And 5, performing multiple differential operation on the instantaneous phase of the signal to obtain the smoothed instantaneous frequency, and turning to step 6. The 4-fold difference calculation formula for the deblurred phase θ' (n) is as follows:
and 6, carrying out feature statistics on the instantaneous frequency, judging and recording the mutation condition of the instantaneous frequency, carrying out statistics on the maximum frequency and the minimum frequency of the instantaneous frequency under the condition of no mutation, accumulating the instantaneous frequency value under the condition of no mutation in real time, and turning to the step 7.
And 6.1, when phase mutation points exist, the instantaneous frequency is mutated, and the position D (k) and the instantaneous frequency value f (k) of the mutation moment and the number M of the phase mutation points are recorded.
And 6.2, accumulating the instantaneous frequency without mutation in real time to obtain an accumulated value and an accumulated length of the instantaneous frequency in real time.
And 6.3, comparing the instantaneous frequency without mutation in real time, and counting the maximum value and the minimum value of the instantaneous frequency.
And 7, taking the instantaneous frequency value under the condition of no mutation at the K points at equal intervals, calculating a linear fitting result of the instantaneous frequency value in real time, and simultaneously turning to the step 8 and the step 10.
7.1, taking the instantaneous frequency result under the condition of no mutation at the K points at equal intervals, and recording the position x of the instantaneous frequencyiAnd the instantaneous frequency value yiAnd simultaneously recording the number m of the points.
Step 7.2, performing linear fitting on the instantaneous frequency value by adopting a least square estimation method, and calculating the product of the formula in real time
Sum product
Sum of squares
Summing
The result of (1).
And 8, at the signal ending moment, calculating a carrier frequency value according to the real-time accumulated instantaneous frequency value and the accumulated length, fitting the bandwidth according to the counted maximum frequency and minimum frequency to obtain a signal bandwidth value, and turning to the step 9.
And 8.1, dividing the real-time accumulated instantaneous frequency value by the accumulated total length at the signal ending moment to obtain a carrier frequency value after mean value filtering.
And 8.2, calculating the difference between the maximum frequency and the minimum frequency of the statistics to obtain a signal bandwidth value.
And 9, performing frequency correction on the recorded instantaneous frequency mutation situation according to the carrier frequency value at the signal ending moment, simultaneously removing false mutation information, calculating the width of a code element, and turning to the step 11.
Step 9.1, the signal ending time, according to the carrier frequency value calculated in step 8, the instantaneous frequency of the instantaneous frequency mutation information recorded in step 6 is sequentially subjected to frequency correction,
and 9.2, judging the mutation information after the frequency correction, wherein the effective mutation information is obtained when the instantaneous frequency is within the threshold range, storing the effective mutation information, and recording the number M' of the effective mutation information.
And 9.3, calculating the difference between mutation time positions according to the mutation time positions in the effective mutation information to obtain a mutation position first step D' (k):
D′(k)＝D(k+1)-D(k)
wherein k is more than or equal to 1 and less than M'.
And 9.4, calculating the difference between the mutation position first step differences D '(k) obtained in the step 9.2 to obtain mutation position second step differences D' (k):
D″(k)＝D′(k+1)-D′(k)
wherein k is more than or equal to 1 and less than M' -1.
And 9.5, calculating a non-zero minimum value in the first-order difference D '(k) and the second-order difference D' (k) of the mutation position as a real code element width value.
And step 10, generating a final frequency modulation slope value according to the instantaneous frequency linear fitting result calculated in real time at the signal ending moment, and turning to step 11. And (4) calculating the following formula by combining the linear fitting result of the real-time calculated instantaneous frequency value in the step (7):
and obtaining a final frequency modulation slope value k.
And step 11, judging the signal modulation type according to the obtained carrier frequency, bandwidth, frequency modulation slope and instantaneous frequency mutation condition after frequency correction, and turning to step 12.
And step 11.1, when the signal bandwidth is smaller than the bandwidth threshold and no phase mutation exists, judging that the signal modulation type is a conventional signal.
And 11.2, when the signal bandwidth is smaller than the bandwidth threshold and phase mutation exists, judging that the instantaneous frequency corrected in the step 9 completely exceeds the BPSK threshold, judging that the signal modulation type is BPSK, and otherwise, judging that the signal modulation type is QPSK.
And step 11.3, when the signal bandwidth is greater than the bandwidth threshold and no phase mutation exists, judging the LFM signal and the NLFM signal by judging the consistency of the linearity of the maximum frequency, the minimum frequency and the carrier frequency and the fitted linear frequency modulation slope.
And step 12, demodulating the code element information of the phase modulation signal according to the signal modulation type, the code element width and the signal length, and turning to step 13.
And step 12.1, judging the signal modulation type to be BPSK and QPSK, and sequentially reading the phase mutation information after the frequency correction stored in the step 9.
And step 12.2, according to the principle that the initial phase is 0, judging whether phase abrupt change information exists in a next period from the beginning of the signal on a time axis and the width of a time interval code element, if so, turning over the code element value, if not, keeping the current code element value, and outputting the finished code element information until the signal time is finished.
And step 13, fusing the conventional PDW information, the signal modulation type, the signal carrier frequency, the bandwidth, the frequency modulation slope, the code element width, the code element information, the signal rising time and the signal falling time, inputting the fusion result into the FSK coding module, and outputting the complete PDW information in real time.
And step 13.1, integrating the signal modulation type, the signal carrier frequency, the bandwidth, the frequency modulation slope, the code element width, the code element information, the signal rising time and the signal falling time information into the conventional PDW information.
And step 13.2, sending the fused PDW into an FSK coding module, and judging whether the continuously arriving signal is of the FSK type or not according to the information such as the arrival time, the pulse width, the frequency, the signal amplitude, the modulation type and the like.
And step 13.3, meeting the PDW information of the FSK type, fusing pulse width results in the PDW, recording each carrier frequency value, modifying the signal modulation type in the large PDW into FSK or FSK + LFM/NLFM/BPSK/QPSK according to the type identified in the pulse, and outputting complete PDW information in real time.
Claims (9)
1. A real-time complex signal identification and parameter estimation method based on FPGA is characterized by comprising the following steps:
step 1, carrying out real-time digital sampling on the intermediate frequency signal to obtain intermediate frequency data, detecting the signal in the intermediate frequency data by using a digital channelization technology to obtain conventional PDW information of the signal, and turning to step 2;
step 2, receiving sampled intermediate frequency data by flowing water, sequentially performing tracking filtering and data rate reduction processing on the intermediate frequency data according to signal conventional PDW information to obtain data after tracking filtering, and simultaneously turning to step 3 and step 4;
step 3, the running water receives the data after the tracking and filtering, calculates the signal amplitude, performs sliding window processing on the signal amplitude, calculates the rising time and the falling time of the signal, and shifts to step 13;
step 4, the running water receives, tracks and filters the data, solves the signal phase in the data, deblurs the signal phase to obtain the instantaneous phase of the signal, and the step 5 is switched to;
step 5, performing multiple differential operation on the instantaneous phase of the signal to obtain the smoothed instantaneous frequency, and turning to step 6;
step 6, carrying out feature statistics on the instantaneous frequency, judging and recording the mutation condition of the instantaneous frequency, carrying out statistics on the maximum frequency and the minimum frequency of the instantaneous frequency under the condition of no mutation, accumulating the instantaneous frequency value under the condition of no mutation in real time, and turning to step 7;
step 7, taking the instantaneous frequency value of the interval K point under the condition of no mutation, calculating a linear fitting result of the instantaneous frequency value in real time, and simultaneously turning to step 8 and step 10;
step 8, calculating a carrier frequency value according to the real-time accumulated instantaneous frequency value and the accumulated length at the signal ending moment, fitting the bandwidth according to the counted maximum frequency and minimum frequency to obtain a signal bandwidth value, and turning to step 9;
step 9, performing frequency correction on the recorded instantaneous frequency mutation situation according to the carrier frequency value at the signal ending moment, simultaneously eliminating false mutation information, calculating the code element width, and turning to step 11;
step 10, generating a final frequency modulation slope value according to the instantaneous frequency linear fitting result calculated in real time at the signal ending moment, and turning to step 11;
step 11, judging the signal modulation type according to the obtained carrier frequency, bandwidth, frequency modulation slope and instantaneous frequency mutation condition after frequency correction, and turning to step 12;
step 12, demodulating the code element information of the phase modulation signal according to the signal modulation type, the code element width and the signal length, and turning to step 13;
and step 13, fusing the conventional PDW information, the signal modulation type, the signal carrier frequency, the bandwidth, the frequency modulation slope, the code element width, the code element information, the signal rising time and the signal falling time, inputting the fusion result into the FSK coding module, and outputting the complete PDW information in real time.
2. The method for real-time complex signal identification and parameter estimation based on the FPGA of claim 1, wherein in step 3, the rising time and the falling time of the signal are calculated, and the method specifically comprises the following steps:
step 3.1, calculating the signal amplitude in the data after tracking and filtering, performing sliding window accumulation on the signal amplitude, and judging and obtaining the maximum amplitude of the signal;
step 3.2, determining an amplitude threshold Start1 and Start2 of the signal rising starting time according to the maximum amplitude of the signal, recording the time when the signal meets the amplitude threshold Start1 and Start2, and recording the time as the rising time;
3.3, determining amplitude thresholds End1 and End2 at the signal falling starting time according to the maximum amplitude of the signal, recording the time when the signal meets the amplitude thresholds End1 and End2, and recording the time as falling time;
in step 4, solving the data phase and deblurring the phase to obtain the instantaneous phase of the signal, specifically comprising the following steps:
step 4.1, solving the signal phase theta (n) through arctangent, and unifying the signal phase to [ -pi, + pi ] through the signal and the real part and imaginary part symbols;
step 4.2, determining a deblurring threshold T according to a signal frequency range, wherein the signal frequency range is obtained through tracking filtering or channelization;
step 4.3, according to the deblurring threshold T, the signal phase theta (n) is deblurred to obtain the signal instantaneous phase theta' (n),
θ′(n)＝θ(n)+C(n)
wherein, C (n) represents the phase correction value of the current sampling point;
in step 5, multiple difference operation is carried out on the signal instantaneous phase theta' (n) to obtain the instantaneous frequency f of the signalN(n), formulated as follows:
where N represents the phase difference multiplicity and j represents the distance from the current sample point.
3. The real-time complex signal identification and parameter estimation method based on the FPGA according to claim 2, wherein in the step 6, an instantaneous frequency mutation condition is recorded, a maximum frequency and a minimum frequency of an instantaneous frequency under a mutation-free condition are counted, and an instantaneous frequency value under a mutation-free condition is accumulated in real time, specifically comprising the following steps:
step 6.1, when phase mutation points exist, the instantaneous frequency is mutated, and the position D (k) and the instantaneous frequency value f (k) of the mutation moment and the number M of the phase mutation points are recorded;
6.2, accumulating the instantaneous frequency without mutation in real time to obtain an accumulated value and an accumulated length of the instantaneous frequency in real time;
and 6.3, comparing the instantaneous frequency without mutation in real time, and counting the maximum value and the minimum value of the instantaneous frequency.
Step 7, taking the instantaneous frequency value under the condition of no mutation at the interval K points, and calculating the linear fitting result of the instantaneous frequency value in real time, wherein the method specifically comprises the following steps:
7.1, taking the instantaneous frequency result under the condition of no mutation at the K points at equal intervals, and recording the position x of the instantaneous frequencyiAnd the instantaneous frequency value yiSimultaneously recording the number m of the points;
step 7.2, performing linear fitting on the instantaneous frequency value by adopting a least square estimation method, and calculating the product of the formula in real time
Sum product
Sum of squares
Summing
The result of (1).
4. The method according to claim 3, wherein in step 8, the carrier frequency value is calculated according to the instantaneous frequency value and the accumulation length accumulated in real time at the signal ending time, and the bandwidth value is fitted according to the statistical maximum frequency and the statistical minimum frequency to obtain the signal bandwidth value, and specifically comprises the following steps:
8.1, at the signal ending moment, dividing the real-time accumulated instantaneous frequency value by the accumulated total length to obtain a carrier frequency value after mean value filtering;
and 8.2, calculating the difference between the maximum frequency and the minimum frequency of the statistics to obtain a signal bandwidth value.
5. The real-time complex signal identification and parameter estimation method based on the FPGA according to claim 4, wherein in step 9, the frequency correction is performed on the recorded instantaneous frequency mutation situation at the signal ending time, meanwhile, false mutation information is removed, and the symbol width is calculated, specifically comprising the following steps:
step 9.1, at the signal ending moment, sequentially carrying out frequency correction on the instantaneous frequency of the instantaneous frequency mutation information recorded in the step 6 according to the carrier frequency value calculated in the step 8;
step 9.2, judging the mutation information after frequency correction, wherein the mutation information is effective mutation information when the instantaneous frequency is within a threshold range, storing the effective mutation information, and recording the number M' of the effective mutation information;
and 9.3, calculating the difference between mutation time positions according to the mutation time positions in the effective mutation information to obtain a mutation position first step D' (k):
D′(k)＝D(k+1)-D(k)
wherein k is more than or equal to 1 and is less than M';
and 9.4, calculating the difference between the mutation position first step differences D '(k) obtained in the step 9.2 to obtain mutation position second step differences D' (k):
D″(k)＝D′(k+1)-D′(k)
wherein k is more than or equal to 1 and less than M' -1;
and 9.5, calculating a non-zero minimum value in the first-order difference D '(k) and the second-order difference D' (k) of the mutation position as a real code element width value.
6. The method according to claim 5, wherein in step 10, the signal end time generates a final frequency modulation slope value according to a real-time calculated instantaneous frequency linear fitting result, and the method specifically comprises the following steps:
and (4) calculating the following formula by combining the linear fitting result of the real-time calculated instantaneous frequency value in the step (7) at the signal ending moment:
and obtaining a final frequency modulation slope value k.
7. The method according to claim 6, wherein in step 11, the signal modulation type is determined according to the obtained information about instantaneous frequency jump after carrier frequency, bandwidth, chirp rate and frequency correction, and specifically comprises the following steps:
step 11.1, when the signal bandwidth is smaller than the bandwidth threshold and no phase mutation exists, judging that the signal modulation type is a conventional signal;
step 11.2, when the signal bandwidth is smaller than the bandwidth threshold and phase mutation exists, judging that the instantaneous frequency corrected in the step 9 completely exceeds the BPSK threshold, judging that the signal modulation type is BPSK, and otherwise, judging that the signal modulation type is QPSK;
and step 11.3, when the signal bandwidth is greater than the bandwidth threshold and no phase mutation exists, judging the LFM signal and the NLFM signal by judging the consistency of the linearity of the maximum frequency, the minimum frequency and the carrier frequency and the fitted linear frequency modulation slope.
8. The method according to claim 7, wherein the step 12 of demodulating the symbol information of the phase-modulated signal according to the signal modulation type, the symbol width and the signal length comprises the following steps:
step 12.1, judging the signal modulation type to be BPSK and QPSK, and sequentially reading the effective mutation information stored in the step 9;
and step 12.2, starting from the signal start on a time axis according to the principle that the initial phase is 0, judging whether phase abrupt change information exists in the time interval code element width, turning over the code element value if the phase abrupt change information exists, keeping the current code element value if the phase abrupt change information does not exist, and outputting complete code element information until the signal time is finished.
9. The method according to claim 8, wherein in step 13, conventional PDW information, signal modulation type, signal carrier frequency, bandwidth, chirp rate, symbol width, symbol information, signal rise time and fall time are fused, and the fusion result is input to an FSK coding module, so as to output complete PDW information in real time, and specifically comprises the following steps:
step 13.1, integrating the signal modulation type, the signal carrier frequency, the bandwidth, the frequency modulation slope, the code element width, the code element information, the signal rising time and the signal falling time information into the conventional PDW information;
step 13.2, sending the fused PDW into an FSK coding module, and judging whether the continuously arriving signal is of an FSK type or not according to information such as arrival time, pulse width, frequency, signal amplitude, modulation type and the like;
and step 13.3, meeting the PDW information of the FSK type, fusing pulse width results in the PDW, recording each carrier frequency value, modifying the signal modulation type in the large PDW into FSK or FSK + LFM/NLFM/BPSK/QPSK according to the type identified in the pulse, and outputting complete PDW information in real time.
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| CN114944975A (en) * | 2022-04-28 | 2022-08-26 | 中国电子科技集团公司第二十九研究所 | Signal modulation type real-time identification method based on FPGA processing |
| CN115499279A (en) * | 2022-09-06 | 2022-12-20 | 扬州宇安电子科技有限公司 | Phase difference-based intra-pulse modulation type identification method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114944975A (en) * | 2022-04-28 | 2022-08-26 | 中国电子科技集团公司第二十九研究所 | Signal modulation type real-time identification method based on FPGA processing |
| CN114944975B (en) * | 2022-04-28 | 2023-03-14 | 中国电子科技集团公司第二十九研究所 | Signal modulation type real-time identification method based on FPGA processing |
| CN115499279A (en) * | 2022-09-06 | 2022-12-20 | 扬州宇安电子科技有限公司 | Phase difference-based intra-pulse modulation type identification method |
| CN115499279B (en) * | 2022-09-06 | 2023-09-05 | 扬州宇安电子科技有限公司 | Method for identifying type of pulse modulation based on phase difference |
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