CN116975747B - Radio signal fuze rapid detection and sorting method - Google Patents

Radio signal fuze rapid detection and sorting method Download PDF

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CN116975747B
CN116975747B CN202310881595.5A CN202310881595A CN116975747B CN 116975747 B CN116975747 B CN 116975747B CN 202310881595 A CN202310881595 A CN 202310881595A CN 116975747 B CN116975747 B CN 116975747B
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signals
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frequency modulation
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CN116975747A (en
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常仁
朱玉鹏
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Institute of Systems Engineering of PLA Academy of Military Sciences
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/883—Radar or analogous systems specially adapted for specific applications for missile homing, autodirectors
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/021—Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/288—Coherent receivers
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/288—Coherent receivers
    • G01S7/2883—Coherent receivers using FFT processing
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/356—Receivers involving particularities of FFT processing
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2431—Multiple classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24317—Piecewise classification, i.e. whereby each classification requires several discriminant rules
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2123/00—Data types
    • G06F2123/02—Data types in the time domain, e.g. time-series data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Abstract

The invention relates to a rapid detection and separation method for a radio signal fuse, belonging to the field of signal processing. The method of the invention firstly improves the signal sensitivity in a mode of combining the broad band and the narrow band processing, and realizes the detection processing of different signals; and then, carrying out coarse sorting by utilizing parameters such as carrier frequency, pulse width, continuous wave identification and the like, and finally, determining identification characteristic quantities from time domain, frequency domain and time-frequency domain according to the characteristics of the fuzes, designing different decision tree classifier structures in consideration of system instantaneity, and carrying out layer-by-layer judgment to classify and identify the fuzes of various systems. The invention realizes the effective reconnaissance and sorting of the radio fuzes.

Description

Radio signal fuze rapid detection and sorting method
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a rapid detection and separation method for a radio signal fuse.
Background
The radio fuze is a proximity fuze (simply called a radio fuze) which is used for acquiring target information by radio waves, and has a greatly improved killing efficiency compared with a triggering fuze, and is widely used in war. The principle of the radio fuze is that electromagnetic waves are emitted to the space through an antenna, the electromagnetic waves return after encountering a target, the electromagnetic waves are received by a receiver in the fuze, the information such as the distance and the speed of the target is extracted through a signal processing circuit in the fuze, when the target enters a dynamic killing area of a warhead of a projectile, the fuze information judging circuit judges, and the fuze is started on a preset distance, so that the killing effect of the warhead is maximized. The radio fuse has the following characteristics:
(1) the bandwidth is processed with a bandwidth that is narrow. The radio fuze occupies very wide working bandwidth and is suitable for processing radio signals with various wide and narrow band systems. Because the available bandwidth is large, a complex working mode (such as a spread spectrum working mode) can be utilized, so that an adversary cannot accurately know the working frequency of the fuze system, and the fuze system is more difficult to intercept quickly.
(2) The anti-interference capability is strong. In actual work, as the action mechanism of the fuze is short-distance action, complex anti-interference measures can be added, so that the anti-interference capability of the fuze is enhanced.
(3) The signal system is various. By analyzing foreign documents, the fuze system of the main world state is gradually changed from the single system to a plurality of complex new systems with stronger anti-interference capability, and the working system is fully expanded.
(4) The acting distance is short. Because the radio fuze belongs to the terminal control device, the working distance of the radio fuze is short, and the influence of atmospheric attenuation can be basically ignored, the characteristic ensures that the transmitting power of the radio fuze can be very small, and the concealment of the radio fuze in working is enhanced.
(5) The working time is short, and the working time is only tens of seconds or even shorter due to the special use requirement of the fuze. Therefore, a simple and reliable structure of the radio fuse is required to avoid system failure.
Taken together, these features of radio fuses require that the fuze countermeasure equipment have the ability to quickly detect the diversity signal under low signal-to-noise conditions of complex electromagnetic environments. In view of this, a fast detection and sorting method for radio signals is proposed herein.
Disclosure of Invention
First, the technical problem to be solved
The invention aims to provide a rapid detection and separation method for a radio signal fuze, which aims to solve the problem that fuze countermeasure equipment can rapidly detect and separate signals under the condition of low signal-to-noise ratio in a complex electromagnetic environment.
(II) technical scheme
In order to solve the above technical problems, the present invention provides a method for quickly detecting and sorting a radio signal fuse, which includes:
s01, signal detection: the signal detection sensitivity is improved in a mode of combining broad and narrow band processing, so that the detection processing of different signals is realized; for a narrow-band signal, processing the signal by utilizing FFT operation with large points to obtain frequency information of the signal, and for a broadband signal, processing by utilizing autocorrelation among the signals to obtain parameter information of the signal;
s03, signal sorting: the method is divided into coarse sorting and fine sorting; coarse sorting is carried out by using carrier frequency, pulse width and continuous wave identification parameters, and communication signals and known radar signals are removed, so that the calculation amount of signal processing in the later period is reduced; aiming at the characteristics of the fuse, the recognition characteristic quantity is determined from a time domain, a frequency domain and a time-frequency domain, and the decision tree classifier structures aiming at different signal patterns are designed in consideration of the real-time performance of the system; the fuzes of various systems are classified and identified through the combination of coarse sorting and fine sorting by layer-by-layer judgment.
Further, for a narrowband signal, the signal is processed by FFT operation with a large number of points, and the frequency information of the signal is obtained, including: firstly, converting signals from analog narrowband acquisition data into digital signals by utilizing AD acquisition, then inputting the signals into a large-point FFT operation program for frequency accumulation, extracting the frequency of the signals by utilizing a parameter measurement method after the accumulation is finished, and calculating the signal information at the moment.
Further, for wideband signals, processing by using autocorrelation between signals, and obtaining parameter information of the signals includes: firstly, carrying out autocorrelation processing on the received acquisition data, after the autocorrelation is finished, judging whether a target exists on a broadband signal through detecting a spectrum peak value, and judging signal period information through the correlated peak value information so as to be used for identifying the target.
Further, the autocorrelation processing is completed in the time domain, and the time domain sliding window accumulates the sample signals in the time domain to achieve the effect of correlation accumulation, which specifically includes: firstly, selecting data with a fixed length N as a reference sample frame, then, taking another section of data with the length N as a data frame, and carrying out cyclic convolution on the reference sample frame and the data frame by a point-by-point sliding method to obtain time domain related data.
Further, the autocorrelation processing is performed in a frequency domain, the frequency domain sliding window is accumulated in a frequency spectrum for processing, the conjugate multiplication in the frequency domain is equivalent to a time domain sliding window, and the processing flow comprises: firstly, carrying out large-point FFT operation on input data to obtain frequency domain data, firstly selecting frequency domain data with the length of N as a reference sample frame, then taking another section of data with the length of N as a data frame, aligning the reference sample frame with the data frame, multiplying the aligned reference sample frame with the data frame to obtain frequency domain product data, and carrying out large-point IFFT operation on the frequency domain product data to obtain final time domain related data.
Further, on the basis of obtaining the information of communication, broadcast television and radar environment radiation sources in the working frequency range, the rough sorting is carried out by utilizing carrier frequency, pulse width and continuous wave identification parameters, and communication signals and known radar signals are removed.
Further, aiming at the characteristics of the fuses, the recognition characteristic quantity is determined from the time domain, the frequency domain and the time-frequency domain, and the decision tree classifier structure is adopted to classify and recognize the fuses of various systems through layer-by-layer judgment in consideration of the real-time performance of the system.
Further, the decision tree classifier structure comprises: judging whether the detected signal is a pulse train or a continuous wave, and if the detected signal is the continuous wave, further judging whether the detected signal is a single-frequency signal or a frequency modulation signal; if the signal is a single frequency signal, further judging whether the signal is a continuous wave Doppler fuze or a pseudo code phase modulation fuze; if the signal is a frequency modulation signal, further judging whether the signal is a single frequency signal or a composite frequency modulation signal; if the signal is a single frequency signal, further judging whether the signal is a linear frequency modulation fuze or a sinusoidal frequency modulation fuze; if the composite frequency modulation signal is the composite frequency modulation signal, further judging whether the composite frequency modulation signal is a pseudo code composite linear frequency modulation fuse or a pseudo code composite sinusoidal frequency modulation fuse.
Further, the burst signal and continuous wave signal identification includes:
assuming that the detected signal is s [ n ], the envelope waveform is calculated by:
A s [n]＝|s[n]|
when the signal is noiseless, whether the signal is a pulse train or not is easily judged; however, when the signal is doped with noise, the envelope thereof fluctuates within a certain range, and the threshold value P is set d To determine whether the signal is a pulse train signal, the specific process is:
when the signal-to-noise ratio is low, A s1 [n]Irregular hops are unavoidable, resulting in recognition errors. In order to avoid the situation, continuously observing a plurality of groups of waveform values after the waveform is hopped, if the continuous multipoint always keeps the value after the hopping, considering the hopping position as a pulse edge, otherwise, considering the hopping position as the hopping caused by noise; the specific process can be represented by the following formula:
for A s2 [n]Calculating the duty cycle delta of the signal, when the duty cycle is larger than the threshold delta d And judging the signal as a continuous wave signal, and judging the signal as a pulse train signal if the signal is not the continuous wave signal.
Further, the frequency signal and frequency modulated signal identification includes:
the energy concentration of the histogram of normalized instantaneous frequencies is defined as:
where hist (f) is a histogram of instantaneous frequency, length (f) is the number of instantaneous frequencies, f is instantaneous frequency, f= (0, 1, … N) fs, fs is sampling frequency;
the vast majority of energy of the normalized instantaneous frequency histogram of the single-frequency signal is concentrated near the normalized carrier frequency, the energy concentration of the normalized instantaneous frequency histogram thereof is thatLarger, while most of the energy of the normalized instantaneous frequency histogram of the frequency modulated signal is statistically distributed at a plurality of frequencies, the energy concentration of the normalized instantaneous frequency histogram is>Small relative to single frequency signals; thus, an appropriate threshold is set>To determine whether it is a single frequency signal or a frequency modulated signal; when->When the signal is judged to be a single-frequency signal, otherwise, the signal is judged to be a frequency modulation signal;
continuous wave Doppler and pseudocode phase modulation signal identification includes:
standard deviation sigma of zero center instantaneous phase nonlinear component dp ：
Wherein the method comprises the steps ofIs a zero-center instantaneous phase nonlinear component, N is the total number of nonlinear components in the signal; for continuous wave Doppler signals, no phase change information is contained, so sigma dp Is a smaller constant, and PSK contains phase change information, so sigma dp Is larger, so a threshold t (sigma dp ) Judging whether the phase modulation signal or the continuous wave Doppler signal;
identification of single and complex frequency modulation includes:
set the instantaneous frequency sequence { f i I=1,.. i ＝|f i+1 -f i I, 1 < i.ltoreq.N-1, and further binarization, i.e. if fc i > t (fc), get fc i =1, otherwise fc i =0; for fc i The smoothness of the curve obtained by summation is:
the pseudo code phase modulation composite frequency modulation signal has a time-frequency distribution diagram, and because of the pseudo code phase modulation, a kick occurs at the position where the pseudo code pattern changes, so S f Larger than the general FM signal without kick S f Smaller; so by setting an appropriate threshold t (S f ) Judging whether the signal is a single signal or a pseudo code composite frequency modulation signal; when S is f ＞t(S f ) When the pseudo code composite frequency modulation signal is judged, otherwise, the pseudo code composite frequency modulation signal is a single frequency modulation signal;
identification of chirped and sinusoidal frequency modulated signals includes:
set the instantaneous frequency sequence { f i I=1,.. i )＝kt i +f 0 Performing least square fitting on the obtained product, and obtaining corresponding k and f 0 The method comprises the following steps of:
the corresponding fitting mean square error is:
if it is a chirp signal, sigma f Is very small, a threshold sigma is set d When sigma f ＜σ d The time is a linear frequency modulation signal, otherwise, the time is a sinusoidal frequency modulation signal; also, for composite fm signals, σ f ＜σ d The pseudo-code composite linear frequency modulation signal is conversely the pseudo-code composite sinusoidal frequency modulation signal
(III) beneficial effects
The invention provides a rapid detection and sorting method for a radio signal fuze, which has the following beneficial effects:
the rapid detection and separation method for the radio signals improves the signal sensitivity by a processing mode combining the wide and narrow band processing, and realizes the detection and the processing of the wide and narrow band signals with different systems; and then, aiming at the self characteristics of fuses of different systems, different decision tree classifier structures are designed by a method of combining thickness sorting and fineness sorting, and the fuses of various systems are classified and identified by layer-by-layer judgment, so that effective reconnaissance and sorting of radio fuses are realized.
Drawings
FIG. 1 is a schematic block diagram of the detection of narrowband signals of the present invention;
FIG. 2 is an output spectrum of a narrowband signal of the present invention;
FIG. 3 is a schematic block diagram of wideband signal detection in accordance with the present invention;
FIG. 4 is a schematic block diagram of a time domain sliding window processing flow in accordance with the present invention;
FIG. 5 is a schematic block diagram of a frequency domain sliding window processing flow in accordance with the present invention;
fig. 6 is a fuse sorting identification flow of the present invention.
Detailed Description
To make the objects, contents and advantages of the present invention more apparent, the following detailed description of the present invention will be given with reference to the accompanying drawings and examples.
The invention discloses a rapid detection and separation method for radio fuze signals, which comprises the steps of firstly, improving signal sensitivity in a mode of combining broad-band and narrow-band processing to realize detection and treatment of different signals; and then, carrying out coarse sorting by utilizing parameters such as carrier frequency, pulse width, continuous wave identification and the like, and finally, determining identification characteristic quantities from time domain, frequency domain and time-frequency domain according to the characteristics of the fuzes, designing different decision tree classifier structures in consideration of system instantaneity, and carrying out layer-by-layer judgment to classify and identify the fuzes of various systems. The invention can be applied in the field of signal processing.
In order to overcome at least one defect or deficiency in the prior art, the invention provides a rapid detection and separation method for radio signals, which firstly improves the signal sensitivity in a mode of combining broad and narrow band processing to realize the detection and separation of different signals; and then, carrying out coarse sorting by utilizing parameters such as carrier frequency, pulse width, continuous wave identification and the like, and finally, determining identification characteristic quantities from time domain, frequency domain and time-frequency domain according to the characteristics of the fuzes, designing different decision tree classifier structures in consideration of system instantaneity, and carrying out layer-by-layer judgment to classify and identify the fuzes of various systems.
In order to achieve the beneficial effects, the technical scheme of the invention comprises the following steps:
1. signal detection
Because of the special close range usage of the radio fuse, the radiation power of the fuse is extremely low, which makes the detection of signals extremely difficult. Therefore, in the design of the signal reconnaissance process and algorithm, the high-sensitivity detection of the signal is considered first.
And (3) carrying out conventional FFT operation on the narrowband signal to complete the detection and analysis of the signal.
For wideband signals, due to the wideband characteristics and the large time-width characteristics, the power is distributed in a wideband frequency spectrum range, the power detection (such as wideband direct detection) method adopted in the prior art is difficult to meet the requirements, and the detection sensitivity is further reduced due to the fact that the processing bandwidth of the narrowband detection based on channelizing is mismatched with the instantaneous bandwidth of the signals.
The method improves the traditional method and provides a method for combining the wide band and the narrow band. The narrow-band signal is mainly detected by adopting a mode of large-point FFT; for broadband signals, the signal to noise ratio is improved through digital channelization, and the signal detection sensitivity is further improved through autocorrelation accumulation. The purpose of rapidly detecting signals is achieved by a processing mode of combining wide bands and narrow bands.
2. Signal sorting
When signal sorting is performed in a complex and dense signal environment, background signals (such as communication signals and pulse signals with fixed frequencies) must be sorted and removed, and the types of fuzes are more and more, so that in order to ensure the interference effectiveness, the system should have sorting recognition capability for different signals.
In order to improve the sorting recognition speed, the two steps of coarse sorting and fine sorting are adopted, firstly parameters such as carrier frequency, pulse width, continuous wave identification and the like are mainly utilized for coarse sorting, communication signals, known radar signals and the like are removed, and the calculation amount of signal processing in the later period is reduced. Then further subdividing the signals after rough sorting, determining identification characteristic quantities from time domain, frequency domain and time-frequency domain according to the characteristics of the fuzes, designing decision tree classifier structures according to different signal patterns in consideration of system instantaneity, and classifying and identifying the fuzes of various systems through layer-by-layer judgment.
Example 1:
the invention will now be described in detail by way of example with reference to the accompanying drawings.
1. Signal detection
(1) Detection of narrowband signals
For the target fuze, the narrowband signal can adopt a frequency domain detection mode to detect the signal because the signal energy is relatively concentrated in the frequency domain. Meanwhile, because the signal radiation power is low, the large-point FFT operation is adopted, the signal energy is accumulated in the frequency domain, and then the signal detection is completed, and the schematic block diagram is shown in figure 1: firstly, converting signals from analog narrowband acquisition data into digital signals by utilizing AD acquisition, then inputting the signals into a large-point FFT operation program for frequency accumulation, extracting the frequency of the signals by utilizing a parameter measurement method after the accumulation is finished, and calculating the signal information at the moment. The spectrum of a common single frequency signal is shown in fig. 2.
(2) Detection of wideband signals
Because of the spread spectrum phenomenon of the broadband signal, the effective signal cannot be extracted from the noise floor in the frequency domain. Thus, for wideband signals, by using their autocorrelation properties, and by long-time autocorrelation accumulation, a reconnaissance of wideband fuze targets is achieved, the schematic block diagram of which is shown in fig. 3. Firstly, performing autocorrelation processing on the received acquisition data (acquiring and receiving a section of signal, storing the section of signal as a reference sample frame, updating the section of signal in real time in a pipeline processing mode, and performing correlation processing on the reference sample frame and the received signal), after the autocorrelation is finished, judging whether a target exists on a broadband signal by detecting a spectrum peak value, and judging information such as a signal period and the like by the peak value information after correlation so as to be used for identifying the target.
The autocorrelation process may be performed in the time domain or in the frequency domain. The time domain sliding window accumulates the sample signals in the time domain, so as to achieve the effect of relevant accumulation, and the processing flow diagram is shown in fig. 4: firstly, selecting data with a fixed length (length is N) as a reference sample frame, then, taking another section of data with the length of N as a data frame, and carrying out cyclic convolution on the reference sample frame and the data frame by a point-by-point sliding method to obtain time domain related data. The frequency domain sliding window is accumulated in the frequency spectrum for processing, and the conjugate multiplication in the frequency domain is equivalent to the time domain sliding window, and the processing flow diagram is shown in fig. 5: firstly, carrying out large-point FFT operation on input data to obtain frequency domain data, firstly, selecting frequency domain data with the length of N as a reference sample frame, then, taking another section of data with the length of N as a data frame, aligning the reference sample frame with the data frame, multiplying the aligned reference sample frame with the data frame to obtain frequency domain product data, and then, carrying out large-point IFFT operation on the frequency domain product data to obtain final time domain related data.
2. Signal sorting
When engineering is realized, the rapid detection of the target signal is finished according to the process. In the process of the attack of the shells, the distance between the shells and the equipment is more and more close, and the signal to noise ratio of the target signals which can be detected by the system is higher and more, so that the sorting of the target signals can be completed under the guidance of the signal detection result.
In order to improve the sorting recognition speed, two steps of coarse sorting and fine sorting are adopted.
On the basis of obtaining the information of the environment radiation sources such as communication, broadcast television, radar and the like in the working frequency range, the rough sorting is mainly carried out by utilizing parameters such as carrier frequency, pulse width, continuous wave identification and the like, and communication signals, known radar signals and the like are removed, so that the calculation amount of signal processing in the later period is reduced.
The signals after coarse sorting are further subdivided for fine sorting. Aiming at the characteristics of the fuses, the identification characteristic quantity is determined from the time domain, the frequency domain and the time-frequency domain, a complex classification and identification structure is not suitable to be designed in consideration of the real-time performance of the system, a decision tree classifier structure is adopted, the fuses of various systems are classified and identified through layer-by-layer judgment, and the fuse separation and identification flow is shown in figure 6.
(1) Pulse train signal and continuous wave signal identification
Assuming that the detected signal is s [ n ], the envelope waveform can be calculated by:
A s [n]＝|s[n]|
when the signal is noiseless, whether the signal is a pulse train or not is easily judged; however, when the signal is doped with noise, the envelope will fluctuate within a certain range, and the threshold value P can be set d To determine whether the signal is a pulse train signal, the specific process is:
when the signal-to-noise ratio is low, A s1 [n]Irregular hops are unavoidable, resulting in recognition errors. In order to avoid the occurrence of the situation, a plurality of groups of waveform values after the hopping can be continuously observed after the waveform is hopped, if the continuous multipoint always keeps the value after the hopping, the hopping position is considered to be a pulse edge, otherwise, the hopping position is considered to be hopping caused by noise. The specific process can be represented by the following formula:
for A s2 [n]Calculating the duty cycle delta of the signal, when the duty cycle is larger than the threshold delta d The continuous wave signal is determined at that time (since the duty cycle of the burst signal is generally smaller than 0.5, the threshold value may be set to be larger than 0.5, for example, set to be 0.6), otherwise the burst signal is determined.
(2) Single frequency signal and frequency modulated signal identification
The energy concentration of the histogram of normalized instantaneous frequencies is defined as:
where hist (f) is a histogram of instantaneous frequency, length (f) is the number of instantaneous frequencies, f is instantaneous frequency, f= (0, 1, … N) ×fs, fs is sampling frequency.
The vast majority of energy of the normalized instantaneous frequency histogram of the single-frequency signal is concentrated near the normalized carrier frequency, the energy concentration of the normalized instantaneous frequency histogram thereof is thatLarger, while most of the energy of the normalized instantaneous frequency histogram of the frequency modulated signal is statistically distributed at a plurality of frequencies, the energy concentration of the normalized instantaneous frequency histogram is>Small relative to the single frequency signal. Thus, an appropriate threshold can be set>To determine whether it is a single frequency signal or a frequency modulated signal. When->In this case, it is possible to determine whether the signal is a single-frequency signal or a frequency-modulated signal.
(3) Continuous wave Doppler and pseudo code phase modulation signal identification
Zero center instantaneous phaseStandard deviation sigma of nonlinear component dp ：
Wherein the method comprises the steps ofIs the zero-center instantaneous phase nonlinear component, N is the total number of nonlinear components in the signal. For continuous wave Doppler signals, no phase change information is contained, so sigma dp Is a smaller constant, and PSK contains phase change information, so sigma dp Larger, a threshold t (sigma dp ) To determine whether a phase modulated signal or a continuous wave doppler signal.
(4) Identification of single and complex frequency modulation
Set the instantaneous frequency sequence { f i I=1,.. i ＝|f i+1 -f i I, 1 < i.ltoreq.N-1, and further binarization, i.e. if fc i > t (fc), get fc i =1, otherwise fc i =0. For fc i The smoothness of the curve obtained by summation is:
the pseudo code phase modulation composite frequency modulation signal has a time-frequency distribution diagram, and because of the pseudo code phase modulation, a kick occurs at the position where the pseudo code pattern changes, so S f Larger than the general FM signal without kick S f Smaller. So that the method can be carried out by setting an appropriate threshold t (S f ) To determine whether a single signal or a pseudo code complex frequency modulated signal. When S is f ＞t(S f ) And when the frequency modulation signal is the pseudo code composite frequency modulation signal, the frequency modulation signal is judged to be the single frequency modulation signal.
(5) Identification of chirp and sinusoidal frequency modulation signals
Set the instantaneous frequency sequence { f i I=1,.. i )＝kt i +f 0 Performing least square fitting on the obtained product, and obtaining corresponding k and f 0 The method comprises the following steps of:
the corresponding fitting mean square error is:
obviously, if chirped, σ f A threshold value sigma can be set small d When sigma f ＜σ d And is a chirp signal, whereas is a sinusoidal frequency modulation signal. Also, for composite fm signals, σ f ＜σ d And the pseudo-code composite linear frequency modulation signal is the pseudo-code composite sinusoidal frequency modulation signal, and vice versa.
Example 2:
a rapid detection and sorting method for a radio fuse signal; comprising the following steps:
and (3) signal detection: the signal detection sensitivity is improved in a mode of combining broad and narrow band processing, so that the detection processing of different signals is realized;
signal sorting: by combining coarse sorting and fine sorting, different decision tree classifier structures are designed, and fuses of various systems are classified and identified through layer-by-layer judgment.
Further, the method comprises the steps of,
s01, processing the narrow-band signal by utilizing FFT operation with large points to obtain information such as frequency of the signal;
s02, processing the broadband signals by utilizing the autocorrelation among the signals to obtain the parameter information of the signals;
the autocorrelation processing of the wideband signal is further divided into time domain processing and frequency domain processing. The time domain processing method is to multiply the time domain related data by a point-by-point sliding window; the method of frequency domain processing is to perform FFT conversion on signals into frequency domain data, and then perform IFFT operation after frequency domain multiplication to obtain related data.
S03, signal separation is divided into coarse separation and fine separation. The coarse sorting is to utilize parameters such as carrier frequency, pulse width, continuous wave identification and the like to perform coarse sorting, reject communication signals, known radar signals and the like, and reduce the calculation amount of signal processing in the later stage. The classification is to determine the identification characteristic quantity from time domain, frequency domain and time-frequency domain aiming at the characteristics of the fuse, and design decision tree classifier structures aiming at different signal patterns in consideration of the real-time performance of the system. The fuzes of various systems are classified and identified through the combination of coarse sorting and fine sorting by layer-by-layer judgment.
The invention has the following beneficial effects:
the rapid detection and separation method for the radio signals improves the signal sensitivity by a processing mode combining the wide and narrow band processing, and realizes the detection and the processing of the wide and narrow band signals with different systems; then, aiming at the self characteristics of the fuses of different systems, different decision tree classifier structures are designed by a method of combining thickness sorting and fineness sorting, and the fuses of various systems are classified and identified by layer-by-layer judgment, so that the effective reconnaissance and sorting of the radio fuses are solved.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (7)

1. A method for rapid detection and sorting of a radio signal fuse, the method comprising:
s01, signal detection: the signal detection sensitivity is improved in a mode of combining broad and narrow band processing, so that the detection processing of different signals is realized; for a narrow-band signal, processing the signal by utilizing FFT operation with large points to obtain frequency information of the signal, and for a broadband signal, processing by utilizing autocorrelation among the signals to obtain parameter information of the signal;
s02, signal sorting: the method is divided into coarse sorting and fine sorting; coarse sorting is carried out by using carrier frequency, pulse width and continuous wave identification parameters, and communication signals and known radar signals are removed, so that the calculation amount of signal processing in the later period is reduced; aiming at the characteristics of the fuse, the recognition characteristic quantity is determined from a time domain, a frequency domain and a time-frequency domain, and the decision tree classifier structures aiming at different signal patterns are designed in consideration of the real-time performance of the system; classifying and identifying the fuses of various systems by combining coarse sorting and fine sorting through layer-by-layer judgment;
wherein,
for a narrow-band signal, the signal is processed by utilizing FFT operation with large points, and the frequency information of the signal is obtained, wherein the method comprises the following steps: firstly, converting signals from analog narrowband acquisition data into digital signals by utilizing AD acquisition, inputting the signals into a large-point FFT operation program for frequency accumulation, extracting the frequency of the signals by utilizing a parameter measurement method after the accumulation is finished, and calculating the signal information at the moment;
for wideband signals, processing by using autocorrelation among signals, and obtaining parameter information of the signals comprises: firstly, carrying out autocorrelation processing on the received acquisition data, after the autocorrelation is finished, judging whether a target exists on a broadband signal through detecting a spectrum peak value, and judging signal period information through the peak value information after the correlation so as to be used for identifying the target;
the decision tree classifier structure comprises: judging whether the detected signal is a pulse train or a continuous wave, and if the detected signal is the continuous wave, further judging whether the detected signal is a single-frequency signal or a frequency modulation signal; if the signal is a single frequency signal, further judging whether the signal is a continuous wave Doppler fuze or a pseudo code phase modulation fuze; if the signal is a frequency modulation signal, further judging whether the signal is a single frequency signal or a composite frequency modulation signal; if the signal is a single frequency signal, further judging whether the signal is a linear frequency modulation fuze or a sinusoidal frequency modulation fuze; if the composite frequency modulation signal is the composite frequency modulation signal, further judging whether the composite frequency modulation signal is a pseudo code composite linear frequency modulation fuse or a pseudo code composite sinusoidal frequency modulation fuse.
2. The method of claim 1, wherein the autocorrelation process is performed in a time domain, and a sliding window of the time domain accumulates the sample signals sliding over the time domain to achieve the correlation accumulation effect, and the method specifically comprises: firstly, selecting data with a fixed length N as a reference sample frame, then, taking another section of data with the length N as a data frame, and carrying out cyclic convolution on the reference sample frame and the data frame by a point-by-point sliding method to obtain time domain related data.
3. The method of claim 1, wherein the autocorrelation process is performed in a frequency domain, the frequency domain sliding window accumulation is performed in a frequency spectrum, the conjugate multiplication in the frequency domain is equivalent to a time domain sliding window, and the processing flow includes: firstly, carrying out large-point FFT operation on input data to obtain frequency domain data, firstly selecting frequency domain data with the length of N as a reference sample frame, then taking another section of data with the length of N as a data frame, aligning the reference sample frame with the data frame, multiplying the aligned reference sample frame with the data frame to obtain frequency domain product data, and carrying out large-point IFFT operation on the frequency domain product data to obtain final time domain related data.
4. The rapid detection and separation method of the radio signal fuze according to claim 1, wherein the coarse separation is performed by using carrier frequency, pulse width and continuous wave identification parameters on the basis of obtaining the information of communication, broadcast television and radar environment radiation sources in the working frequency range, and the communication signals and the known radar signals are removed.
5. The rapid detection and sorting method of radio signal fuses according to claim 4, wherein the feature quantity is determined from time domain, frequency domain and time-frequency domain by a detailed sorting for the characteristics of the fuses, and the fuses of various systems are sorted and identified by layer-by-layer judgment by adopting a decision tree classifier structure in consideration of the real-time performance of the system.
6. The method of claim 1, wherein the burst signal and continuous wave signal identification comprises:
assuming that the detected signal is s [ n ], the envelope waveform is calculated by:
A s [n]＝|s[n]|
when the signal is noiseless, whether the signal is a pulse train or not is easily judged; however, when the signal is doped with noise, the envelope thereof fluctuates within a certain range, and the threshold value P is set d To determine whether the signal is a pulse train signal, the specific process is:
when the signal-to-noise ratio is low, A s1 [n]Irregular jumping points are inevitably generated, so that identification errors are caused; in order to avoid the situation, continuously observing a plurality of groups of waveform values after the waveform is hopped, if the continuous multipoint always keeps the value after the hopping, considering the hopping position as a pulse edge, otherwise, considering the hopping position as the hopping caused by noise; the specific process can be represented by the following formula:
for A s2 [n]Calculating the duty cycle delta of the signal, when the duty cycle is larger than the threshold delta d And judging the signal as a continuous wave signal, and judging the signal as a pulse train signal if the signal is not the continuous wave signal.
7. The method of claim 6, wherein the identifying the frequency signal and the frequency modulated signal comprises:
the energy concentration of the histogram of normalized instantaneous frequencies is defined as:
where hist (f) is a histogram of instantaneous frequency, length (f) is the number of instantaneous frequencies, f is instantaneous frequency, f= (0, 1, … N) fs, fs is sampling frequency;
the vast majority of energy of the normalized instantaneous frequency histogram of the single-frequency signal is concentrated near the normalized carrier frequency, the energy concentration of the normalized instantaneous frequency histogram thereof is thatLarger, but most of the energy of the normalized instantaneous frequency histogram of the frequency modulated signal is statistically distributed uniformly at multiple frequencies, the energy concentration of the normalized instantaneous frequency histogram isSmall relative to single frequency signals; thus, an appropriate threshold is set>To determine whether it is a single frequency signal or a frequency modulated signal; when->When the signal is judged to be a single-frequency signal, otherwise, the signal is judged to be a frequency modulation signal;
continuous wave Doppler and pseudocode phase modulation signal identification includes:
standard deviation sigma of zero center instantaneous phase nonlinear component dp ：
Wherein the method comprises the steps ofIs a zero-center instantaneous phase nonlinear component, N is the total number of nonlinear components in the signal; for continuous wave Doppler signals, noContains phase change information, thus sigma dp Is a smaller constant, and PSK contains phase change information, so sigma dp Is larger, so a threshold t (sigma dp ) Judging whether the phase modulation signal or the continuous wave Doppler signal;
identification of single and complex frequency modulation includes:
set the instantaneous frequency sequence { f i I=1,.. i ＝|f i+1 -f i I, 1 < i.ltoreq.N-1, and further binarization, i.e. if fc i > t (fc), get fc i =1, otherwise fc i =0; for fc i The smoothness of the curve obtained by summation is:
the pseudo code phase modulation composite frequency modulation signal has a time-frequency distribution diagram, and because of the pseudo code phase modulation, a kick occurs at the position where the pseudo code pattern changes, so S f Larger than the general FM signal without kick S f Smaller; so by setting an appropriate threshold t (S f ) Judging whether the signal is a single signal or a pseudo code composite frequency modulation signal; when S is f ＞t(S f ) When the pseudo code composite frequency modulation signal is judged, otherwise, the pseudo code composite frequency modulation signal is a single frequency modulation signal;
identification of chirped and sinusoidal frequency modulated signals includes:
set the instantaneous frequency sequence { f i I=1,.. i )＝kt i +f 0 Performing least square fitting on the obtained product, and obtaining corresponding k and f 0 The method comprises the following steps of:
the corresponding fitting mean square error is:
if it is a chirp signal, sigma f Is very small, a threshold sigma is set d When sigma f <σ d The time is a linear frequency modulation signal, otherwise, the time is a sinusoidal frequency modulation signal; also, for composite fm signals, σ f <σ d And the pseudo-code composite linear frequency modulation signal is the pseudo-code composite sinusoidal frequency modulation signal, and vice versa.
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