CN113030870B - IFF mode 5 signal blind identification method based on time domain characteristics - Google Patents

IFF mode 5 signal blind identification method based on time domain characteristics Download PDF

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CN113030870B
CN113030870B CN202110562041.XA CN202110562041A CN113030870B CN 113030870 B CN113030870 B CN 113030870B CN 202110562041 A CN202110562041 A CN 202110562041A CN 113030870 B CN113030870 B CN 113030870B
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CN113030870A (en
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易显富
张寰宇
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Chengdu Hewei Times Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention provides an IFF mode 5 signal blind identification method based on time domain characteristics, which comprises the following steps: 1) carrying out analog-to-digital conversion on a received signal to obtain a 70M intermediate frequency sampling signal, dividing the sampling signal into two paths, carrying out digital down-conversion processing on the first path, and carrying out MSK real-time incoherent demodulation on the second path; 2) after the first path of sampling signals are subjected to digital down-conversion processing, threshold-crossing detection is carried out on the obtained output signals, and pulse description words are extracted; 3) after the second path of sampling signals are subjected to MSK real-time incoherent demodulation, whether the obtained demodulation information meets the zero-crossing integral multiple rule is judged; 4) inputting the processing results of the two paths of sampling signals into a pulse frame for searching, and realizing the leading pulse sorting of the mode 5 signals; 5) and after the mode 5 leading pulse is sorted out, carrying out optimal sampling point detection on the data pulse of the sampling signal, and finally outputting a mode 5 signal identification result.

Description

IFF mode 5 signal blind identification method based on time domain characteristics
Technical Field
The invention relates to a signal identification method, in particular to an IFF mode 5 signal blind identification method based on time domain characteristics.
Background
The current Mark XIIA system is an upgraded version of Mark XII. Mark XIIA was developed at the end of the 20 th century, adding Pattern 5 to Mark XII, which is the core that MarkXIIa systems must have. The mode 5 encryption identification system still follows the basic principle of a secondary radar system, and makes great changes on the prior technologies of a secondary radar, a mode 4 and the like, such as the adoption of an MSK modulation technology, a spread spectrum technology, a data link transmission technology, a modern computer encryption technology and the like.
Mode 5 preamble binary sequence: 0000111100010001001010, there is a complex electromagnetic environment interference due to practical application. When the receiver carries out real-time differential demodulation for leading identification, error codes can be generated by demodulation code words. Affecting the recognition result of the mode 5 signal.
Disclosure of Invention
The invention provides an IFF mode 5 signal blind identification method based on time domain characteristics, which solves the problem that error codes of a mode 5 leading pulse binary sequence cannot be correctly compared with the mode 5 leading pulse binary sequence due to real-time differential demodulation, and adopts the following technical scheme:
a method for IFF mode 5 signal blind identification based on time domain characteristics comprises the following steps:
s100: performing analog-to-digital conversion on a received signal to obtain a 70M intermediate frequency sampling signal, dividing the sampling signal into two paths, wherein the first path of sampling signal is subjected to digital down-conversion processing, and the second path of sampling signal is subjected to MSK real-time incoherent demodulation;
s200: after the first path of sampling signal is subjected to digital down-conversion processing, an output signal is obtained
Figure 100132DEST_PATH_IMAGE001
Threshold-crossing detection is carried out, and pulse description words are extracted;
s300: after the second path of sampling signals are subjected to MSK real-time incoherent demodulation, whether the obtained demodulation information meets the zero-crossing integral multiple rule is judged;
s400: inputting the processing results of the two paths of sampling signals into a pulse frame for searching, and realizing the leading pulse sorting of the mode 5 signals;
s500: and after the mode 5 leading pulse is sorted out, carrying out optimal sampling point detection on the data pulse of the sampling signal, and finally outputting a mode 5 signal identification result.
Further, in step S100, the processing of the first path of sampling signal through digital down conversion includes the following steps:
s11: providing a carrier wave which keeps the same frequency and phase with the carrier wave of the first path of sampling signal, and setting the first path of sampling signal as
Figure 248216DEST_PATH_IMAGE002
Then local carrier wave
Figure 69542DEST_PATH_IMAGE003
Is composed of
Figure 215352DEST_PATH_IMAGE004
；
Wherein m (t) refers to the amplitude,
Figure 918866DEST_PATH_IMAGE005
it is referred to as the frequency of the frequency,
Figure 73904DEST_PATH_IMAGE006
refers to the initial phase of the first sampling signal,
Figure 484157DEST_PATH_IMAGE007
the intermediate frequency signal of the first path of sampling signal is referred to,
Figure 66448DEST_PATH_IMAGE008
refers to the time;
Figure 257258DEST_PATH_IMAGE009
is the local carrier frequency and is,
Figure 684828DEST_PATH_IMAGE010
is the local carrier initial phase;
s12: multiplying the sampling signal by a carrier to obtain
Figure 277483DEST_PATH_IMAGE011
Figure 765096DEST_PATH_IMAGE012
S13: high frequency component
Figure 380885DEST_PATH_IMAGE013
Is filtered by a low-pass filter, so
Figure 674464DEST_PATH_IMAGE014
Of the output
Figure 59308DEST_PATH_IMAGE015
Neglect, so the multiplied output signal
Figure 983402DEST_PATH_IMAGE016
：
Figure 883225DEST_PATH_IMAGE017
Wherein,
Figure 918177DEST_PATH_IMAGE018
is the gain of the low pass filter;
s14: if the coherence condition is satisfied, i.e.
Figure 423108DEST_PATH_IMAGE019
、
Figure 314840DEST_PATH_IMAGE020
Then output the signal
Figure 905222DEST_PATH_IMAGE021
The output of (c) is:
Figure 478286DEST_PATH_IMAGE022
。
further, in step S200, the pulse description word includes the extracted pulse width, pulse amplitude, pulse arrival time, and pulse interval.
Further, in step S100, the MSK real-time non-coherent demodulation of the second path of sampling signal includes the following steps:
s111: setting the second path of sampling signal as
Figure 100372DEST_PATH_IMAGE023
，
Then will be
Figure 366269DEST_PATH_IMAGE024
And is delayed by
Figure 240684DEST_PATH_IMAGE025
And shift the phase
Figure 351859DEST_PATH_IMAGE026
Is/are as follows
Figure 831382DEST_PATH_IMAGE027
After multiplication, the result is obtained after incoherent demodulation:
Figure 64917DEST_PATH_IMAGE028
wherein,
Figure 629891DEST_PATH_IMAGE029
is the time-varying envelope of the signal,
Figure 544757DEST_PATH_IMAGE030
is a noise or a disturbance,
Figure 878787DEST_PATH_IMAGE031
it is referred to as the frequency of the frequency,
Figure 283223DEST_PATH_IMAGE032
refers to the initial phase of the second path of sampled signals,
Figure 69913DEST_PATH_IMAGE033
refers to the intermediate frequency signal of the second path of sampling signal,
Figure 850788DEST_PATH_IMAGE034
refers to the time;
Figure 39324DEST_PATH_IMAGE035
means that the signal is delayed
Figure 552344DEST_PATH_IMAGE036
A time-varying envelope of;
Figure 888648DEST_PATH_IMAGE037
is delayed by
Figure 410896DEST_PATH_IMAGE038
And shift the phase
Figure 453938DEST_PATH_IMAGE026
Is/are as follows
Figure 403440DEST_PATH_IMAGE039
A signal;
s112: filtering the signal after incoherent demodulation by a low-pass filter, wherein the filtered signal is as follows:
Figure 227039DEST_PATH_IMAGE040
wherein,
Figure 21820DEST_PATH_IMAGE041
；
according to the MSK signal characteristics
Figure 981686DEST_PATH_IMAGE042
，
Figure 367668DEST_PATH_IMAGE043
Is an integer, then the filtered signal
Figure 350667DEST_PATH_IMAGE044
；
S113：
Figure 11456DEST_PATH_IMAGE045
Has the polarity of
Figure 291740DEST_PATH_IMAGE046
In view of the polarity of
Figure 317465DEST_PATH_IMAGE047
，
When the transmission data of the signal is 1,
Figure 850077DEST_PATH_IMAGE048
positive, when the transmission data is 0,
Figure 517819DEST_PATH_IMAGE048
is negative; thereby being able to make a judgment
Figure 389960DEST_PATH_IMAGE048
Positive and negative definite transmissionDemodulation information of the data of (1).
Further, in step S300, it is determined whether the obtained demodulation information satisfies a zero-crossing integer multiple rule, that is, the demodulation information is subjected to zero-crossing detection, and if the demodulation information is an MSK modulation signal, an interval between zero-crossing points is exactly an integer multiple of a code rate of a mode 5 signal.
Further, in step S400, the process of pulse frame search includes the following steps:
s41: marking the pulse detection time of the sampling signal which accords with the pulse width and meets the zero-crossing integral multiple rule;
s42: judging whether 4 pulses exist in the sampling pulse signal within a set time;
s43: judging whether the relative relation of 4 pulses is met;
s44: after MSK differential demodulation, the identification result of the pulse signal is output, and the leading pulse sorting of the mode 5 signal is realized.
Further, in step S500, performing optimal sampling point detection on the data pulse of the sampling signal, and when extracting data pulse information of the sampling signal, determining a first local maximum point as an optimal point; from this point back, every 10 points are the next best point.
Furthermore, in an actual working environment, the amplitude values of the 9 th, 10 th and 11 th points need to be judged at intervals between the points, and the maximum amplitude point is found and marked.
The IFF mode 5 signal blind identification method based on the time domain features is based on the extraction of the leading pulse description word and the condition that the demodulation result of the leading pulse meets the integral multiple rule of the signal zero crossing point code rate, and realizes the blind identification of the mode 5 signal through the searching of a leading pulse frame.
Drawings
FIG. 1 is an interrogation signal format for mode 5 IFF;
FIG. 2 is a reply signal format for Level 1 for mode 5;
FIG. 3 is a reply signal format for Level 2 for mode 5;
FIG. 4 is a block diagram of a mode 5 blind recognition flow;
FIG. 5 is a schematic diagram of the zero crossing detection of the MSK demodulation result;
FIG. 6 is a framework search flow diagram;
FIG. 7 is a diagram of MSK demodulation result decision optimal sampling points;
FIG. 8 is a diagram illustrating the deviation of the optimal sampling point of the MSK demodulation result;
FIG. 9A is a time domain diagram of a mode 5 interrogation signal;
FIG. 9B is a frequency domain plot of a mode 5 interrogation signal;
FIG. 9C is a graph of the envelope of the mode 5 interrogation signal;
fig. 10 is a diagram illustrating the detection and demodulation correspondence of mode 5.
Detailed Description
Mode 5 is described below:
fig. 1 shows an interrogation signal format of the IFF mode 5, which is composed of 4 synchronization pulses (P1, P2, P3, P4), 2 sidelobe suppression pulses (l1, l2), and 11 data pulses (D1 to D11). The pulse signal modulation mode is MSK, and the modulation code rate is 16 Mb/s.
FIG. 2 shows the format of a Level 1 response signal in mode 5. The reply signal format consists of two synchronization pulses (P1, P2) and one data long pulse (9 characters D1-D9). The synchronous pulse and the data long pulse adopt MSK modulation, and the modulation code rate is 16 Mb/s.
FIG. 3 shows the format of a Level 2 response signal in mode 5. The reply signal format consists of 4 synchronization pulses (P1, P2, P3, P4) and one data long pulse (33 characters D1-D33). In the data long pulse, each character contains 4 bits, 33 characters contain 132 bits, and the data long pulse is formed by encoding and expanding an encryption message (108 bit) field into 132 bits through (11,9,1) RS codes and then modulating the 132 bits. The pulse modulation mode is MSK modulation, and the modulation code rate is 16 Mb/s.
As can be seen from the above description, the mode 5 signal not only has a pulse time domain characteristic different from other friend or foe identification signals, but also has a MSK modulation mode, and the leading pulse has a fixed width and a fixed code word. Therefore, the invention provides an IFF mode 5 signal blind identification method based on time domain characteristics, which is a real-time identification method based on leading pulse description word extraction, leading pulse demodulation results meeting the integral multiple rule of signal zero crossing point code rate and leading pulse frame search. The method firstly preliminarily judges the leading monopulse of the suspected mode 5, and then combines the pulse frame search to realize the blind identification of the mode 5 signal.
As shown in fig. 4, the present invention comprises the steps of:
s100: after analog-to-digital conversion is carried out on the received signals, sampling signals of 70M intermediate frequency are obtained, the sampling signals are divided into two paths, one path of the sampling signals is subjected to digital down-conversion processing, and the other path of the sampling signals is subjected to MSK real-time incoherent demodulation;
s200: the first path of sampling signal is subjected to threshold-passing detection to extract a pulse description word;
s300: the second path of sampling signals is subjected to MSK real-time incoherent demodulation, and whether a demodulation result meets a zero-crossing integral multiple rule or not is judged;
s400: inputting the judgment results of the two sampling signals into a pulse frame for searching, and realizing the leading pulse sorting of the mode 5 signals;
s500: and after the leading pulse of the mode 5 is sorted out, carrying out optimal sampling point detection on the pulse of the sampling signal, and finally outputting a mode 5 signal identification result.
In step S100, the received signal is subjected to analog-to-digital conversion to obtain a 70M intermediate frequency sampling signal, and the sampling signal is divided into two paths, where the first path of the sampling signal is subjected to digital down-conversion processing.
Firstly, a carrier wave which keeps the same frequency and phase with the carrier wave of the intermediate frequency sampling signal is provided locally, the sampling signal is used as an input signal, and a first path of input signal is set to be
Figure 648903DEST_PATH_IMAGE049
Wherein m (t) refers to the amplitude,
Figure 872074DEST_PATH_IMAGE050
it is referred to as the frequency of the frequency,
Figure 812348DEST_PATH_IMAGE051
refers to the initial phase of the first sampling signal,
Figure 601313DEST_PATH_IMAGE052
the intermediate frequency signal of the first path of sampling signal is referred to,
Figure 499998DEST_PATH_IMAGE053
refers to time.
Then the local carrier wave
Figure 944886DEST_PATH_IMAGE054
Is composed of
Figure 485589DEST_PATH_IMAGE055
Wherein,
Figure 597902DEST_PATH_IMAGE056
is the local carrier frequency and is,
Figure 136330DEST_PATH_IMAGE057
is the local carrier initial phase.
At this time, the sampled signal is multiplied by the carrier wave to obtain
Figure 130831DEST_PATH_IMAGE058
Should be output as
Figure 678487DEST_PATH_IMAGE059
Figure 379727DEST_PATH_IMAGE060
Figure 620215DEST_PATH_IMAGE061
Due to high frequencyIs divided into
Figure 836433DEST_PATH_IMAGE062
Is filtered by a low-pass filter, so
Figure 922201DEST_PATH_IMAGE063
Of the output
Figure 540264DEST_PATH_IMAGE064
Can be ignored, so the multiplied output signal
Figure 951654DEST_PATH_IMAGE065
Can be represented as follows:
Figure 592851DEST_PATH_IMAGE066
wherein, the formula is the gain of the low-pass filter. If the coherence condition is satisfied, i.e.
Figure 279047DEST_PATH_IMAGE067
、
Figure 240966DEST_PATH_IMAGE068
Then output the signal
Figure 557678DEST_PATH_IMAGE069
The output of (c) is:
Figure 748488DEST_PATH_IMAGE070
visible, output signal
Figure 441637DEST_PATH_IMAGE071
And amplitude m (t).
In step S200, the first path of sampling signal is processed by digital down-conversion to obtain an output signal that is baseband data
Figure 706396DEST_PATH_IMAGE071
And then performing threshold-crossing detection to extract the pulse description word. Wherein, the threshold-crossing detection means that a signal is detected
Figure 459589DEST_PATH_IMAGE071
And (3) extracting the time domain parameters of the signal which is subjected to the medium threshold, wherein the time domain parameters comprise:
(1) when the input signal is carried out to be 8 times larger than the noise bottom, marking the arrival Time (TOA) of the pulse;
(2) when the noise reduction bottom of the input signal is less than 8 times, marking the pulse disappearance time;
(3) pulse interval = difference between pulse arrival time and pulse disappearance time;
(4) pulse Width (PW) = pulse disappearance time-pulse arrival time;
(5) and averaging all sample points of the Pulse Width (PW) to obtain the Pulse Amplitude (PA).
Thereby extracting a pulse description word which comprises the extracted Pulse Width (PW), Pulse Amplitude (PA), pulse arrival Time (TOA) and pulse interval isochronal parameters.
The threshold crossing detection method is to perform noise bottom energy statistics on baseband data after digital down conversion, and when signal energy in the baseband data is 8 times greater than the noise bottom energy, the occurrence of a signal is detected.
In step S100, the second channel of sampling signals is subjected to MSK real-time incoherent demodulation, and if the second channel of sampling signals is subjected to analog-to-digital conversion, the obtained 70M intermediate frequency second channel of sampling signals is set as the intermediate frequency
Figure 137695DEST_PATH_IMAGE072
Wherein,
Figure 368956DEST_PATH_IMAGE073
is the time-varying envelope of the signal,
Figure 19380DEST_PATH_IMAGE074
is a noise or a disturbance,
Figure 474632DEST_PATH_IMAGE075
it is referred to as the frequency of the frequency,
Figure 577717DEST_PATH_IMAGE076
refers to the initial phase of the second path of sampled signals,
Figure 612669DEST_PATH_IMAGE077
refers to the intermediate frequency signal of the second path of sampling signal,
Figure 179917DEST_PATH_IMAGE078
refers to time.
Figure 743754DEST_PATH_IMAGE079
And is delayed by
Figure 599714DEST_PATH_IMAGE080
And shift the phase
Figure 235095DEST_PATH_IMAGE081
Is/are as follows
Figure 328953DEST_PATH_IMAGE082
After multiplication, obtained after noncoherent demodulation
Figure 594849DEST_PATH_IMAGE083
Figure 469264DEST_PATH_IMAGE084
Means that the signal is delayed
Figure 111598DEST_PATH_IMAGE080
A time-varying envelope of;
Figure 325542DEST_PATH_IMAGE085
is delayed by
Figure 559077DEST_PATH_IMAGE080
And shift the phase
Figure 124050DEST_PATH_IMAGE081
Is/are as follows
Figure 35987DEST_PATH_IMAGE082
A signal;
after passing through a low-pass filter, the signal becomes
Figure 370017DEST_PATH_IMAGE086
In the formula
Figure 774453DEST_PATH_IMAGE087
According to the MSK signal characteristics
Figure 561143DEST_PATH_IMAGE088
（
Figure 279701DEST_PATH_IMAGE089
Is an integer) of
Figure 530554DEST_PATH_IMAGE090
Because of the fact that
Figure 43574DEST_PATH_IMAGE091
And
Figure 379878DEST_PATH_IMAGE092
is the envelope of the signal, always positive, so
Figure 902126DEST_PATH_IMAGE093
Has the polarity of
Figure 945168DEST_PATH_IMAGE094
Of (c) is used. In view of
Figure 894670DEST_PATH_IMAGE095
When the transmission data in the received signal is 1,
Figure 452690DEST_PATH_IMAGE096
positive, when the transmission data is 0,
Figure 513050DEST_PATH_IMAGE096
is negative. Thereby passing the judgment
Figure 472916DEST_PATH_IMAGE096
Positive and negative of (d) determines the demodulation information of the transmitted data. In conclusion, the demodulation information of the transmission data can be used for zero-crossing detection judgment.
In step S300, it is determined whether the demodulation result satisfies the zero-crossing integer multiple rule, that is, zero-crossing detection is performed on the demodulation information.
As shown in fig. 5, in case of MSK modulated signal, the interval between zero-crossing points is exactly an integer multiple of the code rate of the mode 5 signal. In fig. 5, the abscissa represents the time axis and the ordinate represents the signal amplitude.
From the figure, the first zero-crossing point is X1:6496, and the second zero-crossing point is X2: 6537. Directly subtract X1 points from X2 points, the difference between them is 41 points. Considering the influence of the actual working environment of the friend or foe identification system, the interval between the zero crossing points is deviated, and a certain margin is considered. Therefore, the zero crossing point interval meets the similar value of the oversampling integral multiple and is summarized into the pulse of the suspected mode 5; and the intervals between the following zero-crossings are similar. Through multiple zero-crossing judgment, pulse signals adopting an MSK modulation mode can be screened out.
In step S400, the results of the two sampling signal determinations are input into a pulse frame search, so as to implement the leading pulse sorting of the mode 5 signals.
As shown in fig. 6, the pulse frame search is performed, including the following steps:
(1) marking the pulse detection time of the sampling signal which accords with the pulse width and meets the zero-crossing integral multiple rule;
for a sampling signal, firstly, a pulse description word (pulse width (PW), Pulse Amplitude (PA), pulse arrival Time (TOA) and pulse interval) can be extracted through threshold detection, and secondly, the sampling signal can be subjected to MSK real-time incoherent demodulation and an MSK modulation mode is adopted. Such a sampling signal is a pulse signal that is screened out to conform to the pattern 5 signal format.
The purpose of such screening is to separate the pulses that may be the mode 5 signal from the other modes of the friend or foe identification signal pulses by a Pulse Width (PW) pulse signal. However, at this time, a large number of other pulses are mixed in the leading pulse of the suspected pattern 5, and how to sort the leading pulse of the true pattern 5 signal depends on the MSK demodulation zero-crossing detection result.
For the sampling signal meeting the requirements, marking is carried out on the pulse of the sampling signal from the first pulse, the following pulse is recorded, and the pulse detection time is marked;
(2) judging whether 4 pulses exist in the sampling pulse signal within a set time;
certain margin value should be considered in combination with the mode 5 signal specification data and the actual working environment of the friend or foe identification system. The pulse width margin value of the mode 5 signal may be set to 0.1 mus and the pulse interval margin value may be set to 0.125 mus. The preliminary judgment that the pulse width is in the range of 0.9675-1.475 mu s is probably the leading pulse of the mode 5 signal. Therefore, referring to FIG. 1, the time between the four pulses P1-P4 is 40.375 μ S, and then it can be determined whether there are 4 pulses within 40.375 μ S + S1 considering the margin S1.
(3) Judging whether the relative relation of 4 pulses is met;
for the pulse signals with 4 pulses, judging whether the pulse signals conform to the relative relation of the 4 pulses, finding out P4 pulses, and extracting jitter values S1, S2 and S3;
(4) and further carrying out MSK differential demodulation on the sampling signals, outputting the identification result of the pulse signals, realizing the sorting of the leading pulses of the mode 5 signals, and extracting the data pulses D1-D11.
In this way, the content code value carried by the sampling signal is extracted, and then the result of identifying the sampling signal is output.
Step S500: and after the mode 5 leading pulse is sorted out, carrying out optimal sampling point detection on the data pulses D1-D11 of the sampling signals, extracting the data pulse information of the sampling signals, and finally outputting a mode 5 signal identification result.
After the pulse frame search, the sort determination mode 5 interrogation signal, delayed 10.5us backward from the P4 pulse in fig. 3, began the best sample point detection within the Pulse Width (PW) time. Theoretically, the first local maximum point is the optimum point. As shown in fig. 7, wherein the abscissa in fig. 7 represents the time axis and the ordinate represents the signal amplitude.
From this point back, every 10 points are the next best point. But considering the magnitude effect that the filter has on the first point in a real working environment of the friend or foe identification system, the second sweet spot and his distance after the first local maximum point are not 10 points. As shown in fig. 8, wherein the abscissa in fig. 8 represents the time axis and the ordinate represents the signal amplitude.
As can be seen from the figure, the first point where the energy rises significantly (6525 points), 10 sample points spaced back, is not the optimal sample point. Thus, first, a point with a significant increase in energy is found and it is checked on the left and right (two points on either side) whether this point is the first optimum point (which is locally highest in amplitude). Is point 6525 of figure 8. Secondly, extracting amplitude values of the 9 th, 10 th and 11 th points from the first optimal point (6525 point) backwards respectively, finding out an amplitude maximum value point and marking. The demodulated data is extracted every 10 points from the mark point. Finally, complete demodulation data information is obtained and a mode 5 signal identification result is output.
In the invention, blind identification of the mode 5 leading pulse is the key point of the invention. And blind identification of the mode 5 signal is carried out by combining a pulse description word, a pulse modulation type and a preamble frame search. The recognition algorithm performs pipeline processing in the time domain as shown in fig. 6, and extracts whether the Pulse Width (PW), the Pulse Amplitude (PA), the pulse arrival Time (TOA), the pulse interval, the pulse modulation type, and the preamble frame are satisfied.
Under the conditions of the signal-to-noise ratio of 20 dB and the sampling rate of 160 MHz, a mode 5 signal is generated through Matlab simulation, and the identification simulation of the mode 5 signal is completed. Fig. 9A, 9B, and 9C are time domain, frequency domain, and envelope plots, respectively, of a mode 5 interrogation signal. In fig. 9A, the abscissa represents the time axis, and the ordinate represents the signal amplitude; in fig. 9B, the abscissa represents frequency and the ordinate represents signal amplitude; in fig. 9C, the abscissa represents the time axis, and the ordinate represents the signal amplitude.
Mode 5 interrogation signals are generated in the laboratory by signal source simulation, and mode 5 signal detection and demodulation are achieved through VHDL language. The FPGA chip is Xilinx XC7Z045, the sampling rate is 160 MHz, and the detection of leading pulses of the mode 5 interrogation signals P1, P2, P3 and P4 by the FPGA blind identification algorithm is shown in FIG. 10 through ModelSim simulation. And (4) from simulation result analysis, extracting pulse width, pulse amplitude, pulse arrival time and pulse interval on a time domain, and searching in a leading frame to identify the leading pulse of the mode 5 by combining with the pulse modulation type. A method of blind identification of the pattern 5 signal is feasible without the need to align the pattern 5 preamble binary sequences.
The IFF mode 5 signal blind identification method based on the time domain features is based on the extraction of the leading pulse description word and the condition that the demodulation result of the leading pulse meets the integral multiple rule of the signal zero crossing point code rate, and realizes the blind identification of the mode 5 signal through the searching of a leading pulse frame.

Claims (8)

1. A method for IFF mode 5 signal blind identification based on time domain characteristics is characterized by comprising the following steps:
s100: performing analog-to-digital conversion on a received signal to obtain a 70M intermediate frequency sampling signal, dividing the sampling signal into two paths, wherein the first path of sampling signal is subjected to digital down-conversion processing, and the second path of sampling signal is subjected to MSK real-time incoherent demodulation;
s200: after the first path of sampling signals are subjected to digital down-conversion processing, threshold-crossing detection is carried out on the obtained output signals, and pulse description words are extracted;
s300: after the second path of sampling signals are subjected to MSK real-time incoherent demodulation, whether the obtained demodulation information meets the zero-crossing integral multiple rule is judged;
s400: inputting the processing results of the two paths of sampling signals into a pulse frame for searching, and realizing the leading pulse sorting of the mode 5 signals;
s500: and after the mode 5 leading pulse is sorted out, carrying out optimal sampling point detection on the data pulse of the sampling signal, and finally outputting a mode 5 signal identification result.
2. The method for blind identification of IFF mode 5 signals based on time domain features according to claim 1, wherein: in step S100, the first path of sampling signal is processed by digital down conversion, which includes the following steps:
s11: providing a carrier wave which keeps the same frequency and phase with the carrier wave of the first path of sampling signal, and setting the first path of sampling signal as
Figure 764278DEST_PATH_IMAGE001
Then local carrier wave
Figure 275506DEST_PATH_IMAGE002
Is composed of
Figure 873978DEST_PATH_IMAGE003
；
Wherein m (t) refers to the amplitude,
Figure 688350DEST_PATH_IMAGE004
it is referred to as the frequency of the frequency,
Figure 245233DEST_PATH_IMAGE005
refers to the initial phase of the first sampling signal,
Figure 715529DEST_PATH_IMAGE006
is an intermediate frequency signal of a first path sampling signalThe number of the mobile station is,
Figure 180008DEST_PATH_IMAGE007
refers to the time;
Figure 52149DEST_PATH_IMAGE008
is the local carrier frequency and is,
Figure 514355DEST_PATH_IMAGE009
is the local carrier initial phase;
s12: multiplying the sampling signal by a carrier to obtain
Figure 534263DEST_PATH_IMAGE010
Figure 740117DEST_PATH_IMAGE011
S13: high frequency component
Figure 466764DEST_PATH_IMAGE012
Is filtered by a low-pass filter, so
Figure 162188DEST_PATH_IMAGE013
Of the output
Figure 607076DEST_PATH_IMAGE014
Neglect, so the multiplied output signal
Figure 351041DEST_PATH_IMAGE015
：
Figure 463353DEST_PATH_IMAGE016
Wherein,
Figure 64099DEST_PATH_IMAGE017
is the gain of the low pass filter;
s14: if the coherence condition is satisfied, i.e.
Figure 261862DEST_PATH_IMAGE018
、
Figure 543939DEST_PATH_IMAGE019
Then output the signal
Figure 307496DEST_PATH_IMAGE020
The output of (c) is:
Figure 282405DEST_PATH_IMAGE021
。
3. the method for blind identification of IFF mode 5 signals based on time domain features according to claim 2, wherein: in step S200, the pulse descriptor includes the extracted pulse width, pulse amplitude, pulse arrival time, and pulse interval.
4. The method for blind identification of IFF mode 5 signals based on time domain features according to claim 1, wherein: in step S100, the MSK real-time incoherent demodulation of the second channel of sampling signals includes the following steps:
s111: setting the second path of sampling signal as
Figure 701885DEST_PATH_IMAGE022
，
Then will be
Figure 849969DEST_PATH_IMAGE023
And is delayed by
Figure 671295DEST_PATH_IMAGE024
And shift the phase
Figure 814176DEST_PATH_IMAGE025
Is/are as follows
Figure 720952DEST_PATH_IMAGE023
After multiplication, the result is obtained after incoherent demodulation:
Figure 407148DEST_PATH_IMAGE026
wherein,
Figure 348559DEST_PATH_IMAGE027
is the time-varying envelope of the signal,
Figure 399692DEST_PATH_IMAGE028
is a noise or a disturbance,
Figure 590502DEST_PATH_IMAGE029
it is referred to as the frequency of the frequency,
Figure 283651DEST_PATH_IMAGE030
refers to the initial phase of the second path of sampled signals,
Figure 813990DEST_PATH_IMAGE031
refers to the intermediate frequency signal of the second path of sampling signal,
Figure 567182DEST_PATH_IMAGE032
refers to the time;
Figure 245288DEST_PATH_IMAGE033
means that the signal is delayed
Figure 210970DEST_PATH_IMAGE034
A time-varying envelope of;
Figure 861394DEST_PATH_IMAGE035
is delayed by
Figure 582226DEST_PATH_IMAGE034
And shift the phase
Figure 685311DEST_PATH_IMAGE036
Is/are as follows
Figure 720263DEST_PATH_IMAGE037
A signal;
s112: filtering the signal after incoherent demodulation by a low-pass filter, wherein the filtered signal is as follows:
Figure 21931DEST_PATH_IMAGE038
wherein,
Figure 851347DEST_PATH_IMAGE039
；
according to the MSK signal characteristics
Figure 707308DEST_PATH_IMAGE040
，
Figure 77109DEST_PATH_IMAGE041
Is an integer, then the filtered signal
Figure 436546DEST_PATH_IMAGE042
；
S113：
Figure 499180DEST_PATH_IMAGE043
Has the polarity of
Figure 576858DEST_PATH_IMAGE044
In view of the polarity of
Figure 953612DEST_PATH_IMAGE045
，
When the transmission data of the signal is 1,
Figure 229873DEST_PATH_IMAGE046
positive, when the transmission data is 0,
Figure 663741DEST_PATH_IMAGE047
is negative; thereby being able to make a judgment
Figure 963135DEST_PATH_IMAGE047
Positive and negative of (d) determines the demodulation information of the transmitted data.
5. The method for blind identification of IFF mode 5 signals based on time domain features according to claim 1, wherein: in step S300, it is determined whether the obtained demodulation information satisfies the zero-crossing integer multiple rule, that is, the demodulation information is subjected to zero-crossing detection, and if the demodulation information is an MSK modulation signal, the interval between zero-crossing points is exactly an integer multiple of the code rate of the mode 5 signal.
6. The method for blind identification of IFF mode 5 signals based on time domain features according to claim 1, wherein: in step S400, the process of pulse frame search includes the following steps:
s41: marking the pulse detection time of the sampling signal which accords with the pulse width and meets the zero-crossing integral multiple rule;
s42: judging whether 4 pulses exist in the sampling pulse signal within a set time;
s43: judging whether the relative relation of 4 pulses is met;
s44: after MSK differential demodulation, the identification result of the pulse signal is output, and the leading pulse sorting of the mode 5 signal is realized.
7. The method for blind identification of IFF mode 5 signals based on time domain features according to claim 1, wherein: in step S500, performing optimal sampling point detection on the data pulse of the sampling signal, and when extracting data pulse information of the sampling signal, determining a first local maximum point as an optimal point; from this point back, every 10 points are the next best point.
8. The method for blind identification of IFF mode 5 signals based on time domain features of claim 7, wherein: in an actual working environment, the amplitude values of the 9 th, 10 th and 11 th points need to be judged at intervals between the points, and the maximum amplitude point is found and marked.
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