CN115567163A - Frequency hopping signal blind detection method, system and equipment - Google Patents
Frequency hopping signal blind detection method, system and equipment Download PDFInfo
- Publication number
- CN115567163A CN115567163A CN202211204842.XA CN202211204842A CN115567163A CN 115567163 A CN115567163 A CN 115567163A CN 202211204842 A CN202211204842 A CN 202211204842A CN 115567163 A CN115567163 A CN 115567163A
- Authority
- CN
- China
- Prior art keywords
- frequency
- time
- signal
- frequency hopping
- hopping signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 claims abstract description 50
- 238000009825 accumulation Methods 0.000 claims abstract description 49
- 230000009466 transformation Effects 0.000 claims abstract description 30
- 238000004891 communication Methods 0.000 claims abstract description 15
- 238000013507 mapping Methods 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims abstract description 6
- 239000006185 dispersion Substances 0.000 claims description 16
- 238000005516 engineering process Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 9
- 238000000354 decomposition reaction Methods 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 3
- 238000001228 spectrum Methods 0.000 abstract description 8
- 230000000694 effects Effects 0.000 abstract description 6
- 230000035508 accumulation Effects 0.000 description 44
- 230000001427 coherent effect Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 235000008694 Humulus lupulus Nutrition 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000819 phase cycle Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0036—Systems modifying transmission characteristics according to link quality, e.g. power backoff arrangements specific to the receiver
- H04L1/0038—Blind format detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/69—Spread spectrum techniques
- H04B1/713—Spread spectrum techniques using frequency hopping
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/69—Spread spectrum techniques
- H04B1/713—Spread spectrum techniques using frequency hopping
- H04B1/715—Interference-related aspects
Abstract
The invention relates to the technical field of communication signal detection, solves the technical problem of poor effect of blind detection of frequency hopping signals caused by strong noise and strong interference, and particularly relates to a frequency hopping signal blind detection method, a system and equipment, wherein the blind detection method comprises the following processes: s1, decomposing a high-speed data stream into a low-speed data stream which can be processed by a digital signal processor in real time, and demultiplexing input data into the low-speed data stream; s2, mapping the low-frequency data stream to a time-frequency domain, and obtaining time-frequency data by adopting spectrogram transformation and incoherent accumulation, wherein the time-frequency data comprises time and frequency data; s3, constructing a frequency sequence according to the time-frequency data, and obtaining time-frequency differential sequences of time-frequency characteristic curve differences of different modulation signals by combining a D-type weighted difference method; and S4, identifying the frequency hopping signal according to the average value of the peak values in the time-frequency difference sequence. The invention can identify the frequency hopping signal in communication from the disordered electromagnetic environment frequency spectrum in a complex communication field.
Description
Technical Field
The invention relates to the technical field of communication signal detection, in particular to a frequency hopping signal blind detection method, a frequency hopping signal blind detection system and frequency hopping signal blind detection equipment.
Background
At present, the detection of frequency hopping signals faces a serious challenge, and the detection is manifested in the aspects of faster and faster hopping speed, wider and wider frequency hopping range, more and more frequency points and the like. Frequency hopping communication devices have been used in large numbers, but the development of frequency hopping signal detection identification technology is relatively slow, so that improvement of frequency hopping signal identification technology is imminent.
In the higher frequency hopping signal, the hopping frequency is higher, and higher hardware technology is required. In order to improve the time efficiency performance of the total probability receiving and the next stage, a digital channel method is adopted to decompose the high-speed data flow into the low-speed data flow. In each channel, the frequency hopping signal is subjected to parallelization detection, but due to strong noise and strong interference, the detection of the amplitude cannot be realized, so that the effect is not good when the frequency hopping signal under the strong noise is detected by blind detection in the prior art, and the detection and identification requirements in the frequency hopping signal with higher frequency cannot be met.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a frequency hopping signal blind detection method, a frequency hopping signal blind detection system and frequency hopping signal blind detection equipment, which solve the technical problem of poor effect of blind detection of frequency hopping signals caused by strong noise and strong interference and can identify the frequency hopping signals in communication from disordered electromagnetic environment frequency spectrums in a complex communication field.
In order to solve the technical problems, the invention provides the following technical scheme: a frequency hopping signal blind detection method comprises the following processes:
s1, decomposing a high-speed data stream into a low-speed data stream which can be processed by a digital signal processor in real time, and decomposing input data into the low-speed data stream in a multi-path manner, so that the real-time rapid processing capacity of the digital signal processor on the high-speed data stream can be improved;
s2, mapping the low-frequency data stream onto a time-frequency domain, and performing spectrogram transform and incoherent accumulation to obtain time-frequency data, wherein the time-frequency data comprises time data and frequency data, and the performance of the quadratic time-frequency transform is superior to that of linear time-frequency transform and the spectrogram performance is outstanding, so that the spectrogram transform and incoherent accumulation are performed, and an IVI-CFAR detector is used for detecting effective signals;
s3, constructing a frequency sequence according to the time-frequency data, and obtaining time-frequency differential sequences of time-frequency characteristic curve differences of different modulation signals by combining a D-type weighted differential method;
and S4, identifying the frequency hopping signal according to the average value of the peak values in the time-frequency difference sequence.
Further, in step S1, the decomposing of the high-speed data stream into a low-speed data stream that can be processed by the digital signal processor in real time includes the steps of:
s11, carrying out digital processing on high-speed data by adopting a high-speed A/D conversion technology;
s12, dividing a receiving bandwidth into a plurality of sub-channels by adopting a low-pass filter;
and S13, performing D-time extraction on the data stream in each sub-channel, wherein the sampling rate is 1/D of the original sampling rate.
Further, in step S2, mapping the low data stream to a time-frequency domain, and obtaining time-frequency data by using spectrogram transform and incoherent accumulation includes the following steps:
s21, carrying out short-time Fourier transform on the low-frequency data stream, wherein the input time-frequency domain signal stream can be changed into a discrete form through the short-time Fourier transform;
s22, carrying out summation operation according to the input signal, the window function type and the window length to obtain a time-frequency amplitude value output by a kth channel at the nth moment;
s23, squaring the time-frequency amplitude to obtain a spectrogram transformation result;
s24, performing incoherent accumulation on the spectrogram transformation result according to the accumulation length and the window function length to obtain data to be detected;
and S25, inputting the data to be detected into an IVI-CFAR detector to detect effective signals.
Further, in step S3, a frequency sequence is constructed according to the time-frequency data, and a difference result of time-frequency characteristic curve differences of different modulation signals in the frequency sequence is obtained by combining a D-type weighted difference method, which specifically includes the following steps:
s31, selecting a fixed frequency signal, an LFM signal and a frequency modulation signal which are commonly used in the communication field;
s32, setting the hop period, the coding rate and the spectrogram conversion window length of the frequency hopping signal;
s33, selecting the maximum value of the frequency amplitude of each time point on the obtained time frequency data to construct a frequency sequence;
and S34, obtaining a difference result of the time-frequency characteristic curve difference after different modulation signals are modulated by adopting a D repetition frequency difference method, namely obtaining a time-frequency difference sequence.
Further, in step S4, the frequency hopping signal is identified according to the average value of the peak values in the time-frequency difference sequence, and the specific process includes the following steps:
s41, counting the number n of peak values in the time-frequency difference sequence by adopting a D repetition frequency difference method;
when the peak number n is less than 0, determining as a fixed frequency signal, and when the peak number is greater than 0, entering step S42;
s42, performing linear fitting on each peak value position in the peak value number n to obtain corresponding fitting dispersion;
s43, setting the fitting dispersion e as a judgment threshold to be 0.1;
s44, judging whether the fitting dispersion of each peak value is smaller than e;
when the fitting dispersion is larger than e, determining that the FSK signal is present;
and when the fitting dispersion is less than e, the step S45 is carried out;
s45, estimating a peak value average value of the time-frequency difference sequence;
when the estimation of the peak value mean value of the time frequency differential sequence is larger than ef, the frequency hopping signal is judged;
when the estimate of the peak-to-average value of the time-frequency differential sequence is smaller than ef, an LFM signal is determined.
The invention also provides another technical scheme, and a system for implementing the frequency hopping signal blind detection method comprises the following steps:
the high-speed data flow decomposition module is used for decomposing the high-speed data flow into a low-speed data flow which can be processed by the digital signal processor in real time;
the time-frequency data obtaining module is used for mapping the low-frequency data stream to a time-frequency domain, and obtaining time-frequency data by adopting spectrogram transformation and incoherent accumulation, wherein the time-frequency data comprises time and frequency data;
the time-frequency differential sequence obtaining module is used for constructing a frequency sequence according to the time-frequency data and obtaining time-frequency differential sequences of time-frequency characteristic curve differences of different modulation signals by combining a D-type weighted differential method;
and the frequency hopping signal identification module is used for identifying the frequency hopping signal according to the average value of the peak values in the time-frequency differential sequence.
The invention also provides another technical scheme, and a device for implementing the frequency hopping signal blind detection method comprises the following steps:
a processor;
a memory;
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs for a computer to perform a method of blind detection of a frequency hopping signal.
By means of the technical scheme, the invention provides a frequency hopping signal blind detection method, a frequency hopping signal blind detection system and frequency hopping signal blind detection equipment, which at least have the following beneficial effects:
1. according to the frequency hopping signal blind detection method provided by the invention, the identification rate of the frequency hopping signal is close to 60% at minus 8dB, the identification rate of the frequency hopping signal is gradually increased along with the increase of the signal-to-noise ratio, when the signal-to-noise ratio reaches minus 4dB, the identification rate of the frequency hopping signal is close to 100%, the identification rate of the frequency hopping signal is stable at 100%, and the identification rate of the frequency hopping signal after adding incoherent accumulation is far better than that without adding incoherent accumulation.
2. According to the invention, through the analysis of a frequency hopping system and the existing parameter estimation algorithm, the frequency hopping signal parameter estimation and identification are carried out by utilizing spectrogram transformation and incoherent accumulation, and the IVI-CFAR detector is used, so that the frequency hopping signal can be effectively identified.
3. On the basis of spectrogram transformation and incoherent accumulation, the method estimates and identifies the parameters of the frequency hopping signal by using methods of extracting effective time-frequency transformation data, generating a time-frequency sequence, a D-repetition frequency difference method, linear fitting, estimating the peak value average value of the time-frequency difference sequence and the like, and can effectively identify the frequency hopping signal.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of simulated electromagnetic environment signal time-frequency in accordance with the present invention;
FIG. 2 is a flow chart of a blind detection method for frequency hopping signals according to the present invention;
FIG. 3 is a diagram of a low pass filter bank dividing the acceptance bandwidth into frequency bands according to the present invention;
FIG. 4 is a schematic diagram of an IVI-CFAR detector of the present invention;
FIG. 5 is a flow chart illustrating logic for identifying a frequency hopping signal according to the present invention;
FIG. 6 is a graph illustrating the recognition rates of various modulation signals according to the present invention;
FIG. 7 is a schematic diagram showing the comparison of the identification rate of the coherent accumulation frequency hopping signal according to the present invention;
fig. 8 is a schematic block diagram of a frequency hopping signal blind detection system of the present invention.
In the figure: 10. a high-speed data stream decomposition module; 20. a time-frequency data obtaining module; 30. a time-frequency difference sequence obtaining module; 40. and a frequency hopping signal identification module.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof. Therefore, the realization process of how to apply technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
For detecting a frequency hopping signal, the prior art mainly includes: channelized detection technology, autocorrelation detection technology and time-frequency analysis detection technology. Improvements to detection methods based on channelized reception include energy detection methods, channel threshold adaptation, and analysis of detection performance for reception bandwidth mismatch problems. And the channelization detection method requires an integer multiple of the receiver integration period over the hop period within each channel.
The autocorrelation detection technology is an autocorrelation domain (ACD) detection method, which considers more signal minutiae than the energy detection rule, and thus has better detection performance. The frequency-frequency analysis detection technology mainly utilizes a time-frequency graph of an intercepted signal to detect a frequency-modulated signal, and the frequency-modulated signal detection method mainly based on time-frequency graph correction can extract the time-frequency graph of a frequency-hopping signal under the condition that signals of various systems (fixed frequency, frequency sweep, pulse and the like) are mixed, so that the aim of detecting the frequency-hopping signal is fulfilled.
In the higher frequency hopping signal, the hopping frequency is higher, and higher hardware technology is required. In order to improve the time efficiency performance of the total probability receiving and the next stage, a digital channel method is adopted to decompose the high-speed data flow into the low-speed data flow. In each channel, the frequency hopping signal is detected in a parallelization manner, but due to strong noise and strong interference, the amplitude detection cannot be realized, so that the effect of the prior art is not good when blind detection is used for detecting the frequency hopping signal under the strong noise.
Frequency hopping communication is a communication method in which the carrier frequencies of transmission signals of both the transmitter and the receiver are discretely converted according to a predetermined rule, that is, the carrier frequencies in communication are controlled by pseudo-random variation codes to perform random hopping. At different times, signals are transmitted on different channels.
As shown in fig. 1, which shows a time-frequency diagram of a scrambled electromagnetic signal, when an original baseband signal is modulated, a plurality of carrier frequencies generated by PN codes are determined by a PN code generator and mixed to obtain a frequency hopping signal. After the following formula modulates the information original code to obtain the initial baseband signal, the frequency synthesizer will generate various carrier frequencies determined by the pseudo code generator, and after mixing the two, a frequency hopping signal, that is, a frequency hopping signal, is obtained. As shown in the following equation:
s(t)＝∑exp[j(2πf n t+φ n )]p(t-nT b )
wherein p (T) is T with time width b Base pulse waveform of (a), f n For a pseudo-randomly generated frequency shift sequence,
the pseudo-random phase sequence when the frequency jump occurs, j is additive noise, and t is signal observation time.
The prior art is not very good in the effect of detecting and identifying the signal-to-noise ratio by using spectrogram transform alone, and in order to improve the anti-noise performance, the embodiment provides a new method for performing signal blind processing by using spectrogram transform and incoherent accumulation.
Referring to fig. 2 to fig. 8, which illustrate a specific implementation manner of this embodiment, in the method for blind detection of a frequency hopping signal provided in this embodiment, the identification rate of the frequency hopping signal is close to 60% at-8 dB, the identification rate of the frequency hopping signal gradually increases with the increase of the signal-to-noise ratio, when the signal-to-noise ratio reaches-4 dB, the identification rate of the frequency hopping signal is close to 100%, and then the identification rate of the frequency hopping signal is stabilized at 100%, and the identification rate of the frequency hopping signal after adding incoherent accumulation is much better than that without adding incoherent accumulation.
Referring to fig. 2, a frequency hopping signal blind detection method is used in the technical field of communication signal detection, constructs a frequency sequence by using spectrogram transform and incoherent accumulation, and identifies a frequency hopping signal sent by a specific sender in a disordered electromagnetic environment spectrum, and includes the following steps:
s1, decomposing a high-speed data stream into a low-speed data stream which can be processed by a digital signal processor in real time, and decomposing input data into the low-speed data stream in a multi-path manner, so that the real-time rapid processing capacity of the digital signal processor on the high-speed data stream can be improved;
in step S1, the decomposition of the high-speed data stream into a low-speed data stream that can be processed by the digital signal processor in real time comprises the following steps:
s11, carrying out digital processing on high-speed data by adopting a high-speed A/D conversion technology;
s12, dividing a receiving bandwidth into a plurality of sub-channels by adopting a low-pass filter;
and S13, performing D-time extraction on the data stream in each sub-channel, wherein the sampling rate is 1/D of the original sampling rate.
The high-speed frequency hopping signal is digitized by high-speed a/D, and the receive bandwidth is divided into several bands, each of which is a channel, using a bank of low-pass filters (see fig. 3). The data stream of each channel is sampled D times, the sampling rate is set to the original 1/D, and the data transmission rate is reduced by the receiving device of the polyphase filter. The specific formula is as follows:
wherein s (n) is the input, y k (m) is the output, k is the number of channels, D is the decimation rate, F is the interpolation rate, m is the data rate, p is the number of accumulations, e jωk(mD-p) For the mixing parameters, h (p) is a low pass filter.
S2, mapping the low-frequency data stream onto a time-frequency domain, and performing spectrogram transform and incoherent accumulation to obtain time-frequency data, wherein the time-frequency data comprises time data and frequency data, and the performance of the quadratic time-frequency transform is superior to that of linear time-frequency transform and the spectrogram performance is outstanding, so that the spectrogram transform and incoherent accumulation are performed, and an IVI-CFAR detector is used for detecting effective signals;
the performance of spectrogram change in secondary time-frequency transformation is more prominent after processing channelized low data stream, as shown in the following formula:
SPEC x (l,k)＝|STFT x (l,k)| 2
SPEC in the above formula x (kl, k) denotes the result of the spectral transformation, where | STFT x (l, k) | represents the short-time Fourier transform, and the specific formula is shown as the following formula:
wherein STFT x (n, k) represents the time-frequency amplitude value of the k channel output at the time n; x (N + i) represents an input time-domain signal stream in a discrete format, where w (i) represents various window lengths, N represents time points, k represents discrete frequency points,
i is a window value as a spectrum transfer amount.
Based on the spectral change, uncorrelated accumulation is used to improve the noise characteristics, as shown in the following equation:
wherein L is i For the accumulation length, L is the window function length, m is the decimation factor, | STFT x (r,k)| 2 The square of the short-time Fourier transform after modulus taking, x is the input of a time-domain signal stream, r is a time point, k is the time-frequency amplitude value of the kth channel output, I i (m, k) is a non-coherent accumulation value.
In the embodiment, through the analysis of a frequency hopping system and the existing parameter estimation algorithm, the frequency hopping signal parameter estimation and identification are carried out by utilizing spectrogram transformation and incoherent accumulation, and the IVI-CFAR detector is used, so that the frequency hopping signal can be effectively identified.
In step S2, mapping the low data stream to a time-frequency domain, and obtaining time-frequency data by using spectrogram transform and incoherent integration includes the following steps:
s21, carrying out short-time Fourier transform on the low-frequency data stream, wherein the input time-frequency domain signal stream can be changed into a discrete form through the short-time Fourier transform;
s22, performing summation operation according to the input signal, the window function type and the window length to obtain a time-frequency amplitude value output by the kth channel at the nth moment;
and multiplying the input signal x (n) by the window function type w (i) and then carrying out summation operation by combining the corresponding window length to obtain the time-frequency amplitude output by the kth channel at the nth moment.
S23, squaring the time-frequency amplitude to obtain a spectrogram transformation result;
s24, performing incoherent accumulation on the result of spectrogram transformation according to the accumulation length and the window function length to obtain data to be detected;
the result of the spectrogram transformation is determined according to the accumulation length L i Accumulate with a window function length L to perform non-coherent accumulation.
S25, inputting the data to be detected into an IVI-CFAR detector to detect effective signals;
the IVI-CFAR detector also needs to set a corresponding threshold to detect the valid signal when detecting the valid signal, and the threshold factor a in the IVI-CFAR detector decreases as the length of the cell window increases.
And (3) spectrum transformation is used, namely the square of a short-time Fourier transform model, incoherent accumulation is adopted on the basis of spectrum transformation to further improve the anti-noise performance, and an IVI-CFAR detector is used for detecting effective signals under a complex clutter background.
The selection of window function types in short-time Fourier transform affects time and frequency resolution at the same time, processing gains of different accumulation window lengths in incoherent accumulation are increased along with the increase of signal length, and a threshold factor a in an IVI-CFAR detector is reduced along with the increase of unit window length.
To detect a valid signal, an IVI-CFAR detector is used for detection (as shown in fig. 4), as shown in the following equation:
wherein x i Representing the echo data after square-law detection with a sliding window a or B,
is the sample mean of the random variables.
And
the sample means for sliding windows a and B are represented, respectively, with n being the half-sliding window length, VI being the variable indicator, and MR being the mean ratio.
S3, constructing a frequency sequence according to the time-frequency data, and obtaining time-frequency differential sequences of time-frequency characteristic curve differences of different modulation signals by combining a D-type weighted difference method;
in step S3, a frequency sequence is constructed according to the time-frequency data, and a difference result of time-frequency characteristic curve differences of different modulation signals in the frequency sequence is obtained by combining a D-type weighted difference method, which specifically comprises the following steps:
s31, selecting a fixed frequency signal, an LFM signal and a frequency modulation signal which are commonly used in the communication field;
s32, setting the hop period, the coding rate and the spectrogram conversion window length of the frequency hopping signal;
setting hop period T of frequency hopping signal H =2ms, the frequency hopping signal of the system is conventional, frequency modulated in FSK format, the coding rate is 5KHz, the spectral conversion window length L =64, and the extraction factor M =8.
S33, selecting the maximum value of the frequency amplitude of each time point on the obtained time frequency data to construct a frequency sequence;
the frequency series is constructed using the maximum vertical axis of maximum time and frequency for each time point in time and frequency data obtained by spectrogram conversion and non-coherent accumulation.
S34, obtaining a difference result of time-frequency characteristic curve differences after different modulation signals are modulated by adopting a D repetition frequency difference method, namely obtaining a time-frequency difference sequence;
on the basis of time-frequency data obtained by spectrogram transformation and incoherent accumulation, a frequency sequence is constructed, and a D-type weighted difference method is combined to obtain the result of difference of time-frequency characteristic curves of different modulation signals.
S4, identifying the frequency hopping signal according to the average value of the peak values in the time-frequency difference sequence;
if the average value of the peak values is less than or equal to a frequency domain resolution, identifying the peak values as frequency hopping signals, otherwise, identifying the peak values as frequency hopping signals, and setting a frequency domain resolution as ef, then:
in the above formula, f s Is the sampling rate and L is the window length.
According to the time-frequency differential sequence and the time-frequency characteristic that the frequency hopping signal is different from other modulation signals, the frequency hopping signal is identified from the complex electromagnetic environment through the average value of the peak values in the time-frequency differential sequence.
On the basis of spectrogram transformation and incoherent accumulation, the method estimates and identifies the frequency hopping signal parameters by using methods of extracting effective time frequency transformation data, generating a time frequency sequence, a D-repeated frequency difference method, linear fitting, estimating the peak mean value of the time frequency difference sequence and the like, and can effectively identify the frequency hopping signal.
S41, counting the number n of peak values in the time-frequency difference sequence by adopting a D repetition frequency difference method;
when the peak number n is less than 0, determining as a fixed frequency signal, and when the peak number is greater than 0, entering step S42;
s42, performing linear fitting on each peak value position in the peak value number n to obtain corresponding fitting dispersion;
s43, setting the fitting dispersion e as a judgment threshold to be 0.1;
s44, judging whether the fitting dispersion of each peak value is smaller than e;
when the fitting dispersion is larger than e, determining that the FSK signal is generated;
and when the fitting dispersion is less than e, the step S45 is carried out;
s45, estimating a peak value average value of the time-frequency difference sequence;
when the estimation of the peak value mean value of the time frequency differential sequence is larger than ef, the frequency hopping signal is judged;
when the estimate of the peak-to-average value of the time-frequency differential sequence is less than ef, the LFM signal is determined.
The embodiment firstly decouples the input high-speed data stream into a digital signal processor, applies the digital signal processor to the low-speed data stream which can be processed in real time in a digital channel, then uses a polyphase filter bank to reduce the transmission rate of data, then uses a detection algorithm based on spectrogram transformation and incoherent accumulation to identify a frequency hopping signal, and finally identifies the frequency hopping signal according to the characteristic difference of time-frequency of the frequency hopping signal and other modulation signals.
The method comprises the steps of utilizing spectrogram transformation and incoherent accumulation and combining with an IVI-CFAR detector to identify frequency hopping signals, extracting effective time frequency transformation data to generate a time frequency difference sequence, counting the number n of peak values by a D-repetition frequency difference method, then determining whether linear fitting is carried out according to the size of n to distinguish FSK signals and non-FSK signals, estimating the peak value average value of the time frequency difference sequence of the non-FSK signals, and judging whether the signals are LFM signals or frequency hopping signals.
In the embodiment, effective time-frequency transformation data are extracted to generate a time-frequency sequence, and a D-repetition frequency difference method, linear fitting, a peak mean value of the time-frequency difference sequence and other methods are adopted, so that the performance of frequency hopping signal parameter estimation and identification under strong noise is effectively improved.
The essence of the frequency hopping signal is that it can hop in a wider band, taking up signal bandwidth between hops, thereby yielding a very wide frequency hopping bandwidth. The duration of a fixed frequency signal, which tends to be longer, is typically much greater than the hop period of a frequency hopping signal. As shown in fig. 5, the method for identifying a frequency hopping signal of the present invention specifically includes the following steps:
(1) And carrying out digital processing on the high-speed data by adopting a high-speed A/D conversion technology, and dividing the receiving bandwidth by adopting a low-pass filter.
(2) And D times of extraction is carried out on the data stream in each channel, and the sampling rate is 1/D of the original sampling rate.
(3) And carrying out short-time Fourier transform on the obtained low data stream, multiplying the input signal x (n) by the window function type w (i), and then carrying out summation operation by combining the corresponding window length to obtain the time-frequency amplitude value output by the nth time and the kth channel.
(4) And taking the square of the short-time Fourier transform module to obtain a spectrogram transform result.
(5) And converting the spectrogram according to the accumulated length L i Accumulate with a window function length L to perform non-coherent accumulation.
(6) And inputting the result obtained by the incoherent accumulation into an IVI-CFAR detector, and setting a corresponding threshold to detect an effective signal.
(7) Selecting common fixed frequency signals, LFM signals and frequency modulation signals, setting the hop period TH =2ms of frequency hopping signals, wherein the fixed frequency signals of the system are conventional, frequency modulation in an FSK format is carried out, the coding rate is 5KHz, the length L =64 of a spectrum conversion window, and the extraction coefficient M =8.
(8) And constructing a frequency sequence by utilizing the maximum vertical axis of the maximum time-frequency domain of each time point in time-frequency domain data obtained through spectrogram conversion and non-coherent accumulation, and solving a difference result of time-frequency curves modulated by different modulation signals by utilizing a D-repeated frequency difference method.
(9) And linearly fitting the peak position, and setting the fitting departure difference e as a judgment threshold to be 0.1. And if the average value of the peak values is less than or equal to one frequency domain resolution ef, identifying the frequency hopping signal, otherwise, identifying the frequency hopping signal.
As shown in fig. 6, the Monte-Carlo simulation is used to analyze the spectrum transformation and the detection and performance estimation of incoherent accumulation, which indicates that the recognition rate of the frequency hopping signal is close to 60% when the signal-to-noise ratio is-8 dB, and the recognition rate of the frequency hopping signal gradually increases with the increase of the signal-to-noise ratio. When the signal-to-noise ratio reaches-4 dB, the recognition rate of the frequency hopping signal is close to 100%. The frequency hopping signal identification rate then settles to 100%. Fig. 7 shows that the frequency hopping signal identification rate after adding the incoherent accumulation is much better than that without adding the incoherent accumulation, and the development and application of military communication will generate more important promotion and bring better social and economic benefits.
In summary, the embodiment can realize blind detection of the frequency hopping signal under strong noise, and can achieve a good identification rate above-4 dB.
Referring to fig. 8, the present embodiment further provides a system for implementing the frequency hopping signal blind detection method, including:
the high-speed data stream decomposition module 10, the high-speed data stream decomposition module 10 is used for decomposing the high-speed data stream into a low-speed data stream which can be processed by the digital signal processor in real time;
the time-frequency data obtaining module 20, the time-frequency data obtaining module 20 is configured to map the low data stream onto a time-frequency domain, and obtain time-frequency data by using spectrogram transform and incoherent accumulation, where the time-frequency data includes time and frequency data;
the time-frequency differential sequence obtaining module 30, the time-frequency differential sequence obtaining module 30 is used for constructing a frequency sequence according to the time-frequency data, and obtaining time-frequency differential sequences of time-frequency characteristic curve differences of different modulation signals by combining a D-type weighted differential method;
and the frequency hopping signal identification module 40, the frequency hopping signal identification module 40 is configured to identify the frequency hopping signal according to the average value of the peak values in the time-frequency difference sequence.
The present embodiment further provides an apparatus for implementing the frequency hopping signal blind detection method, including:
a processor;
a memory;
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs for the computer to perform the method for blind detection of a frequency hopping signal.
According to the frequency hopping signal blind detection method provided by the invention, the identification rate of the frequency hopping signal is close to 60% at minus 8dB, the identification rate of the frequency hopping signal is gradually increased along with the increase of the signal-to-noise ratio, when the signal-to-noise ratio reaches minus 4dB, the identification rate of the frequency hopping signal is close to 100%, the identification rate of the frequency hopping signal is stable at 100%, and the identification rate of the frequency hopping signal after adding incoherent accumulation is far better than that without adding incoherent accumulation.
On the basis of spectrogram transformation and incoherent accumulation, the method estimates and identifies the parameters of the frequency hopping signal by using methods of extracting effective time-frequency transformation data, generating a time-frequency sequence, a D-repeated frequency difference method, linear fitting, estimating the peak mean value of the time-frequency difference sequence and the like, and can effectively identify the frequency hopping signal.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For each of the above embodiments, since they are substantially similar to the method embodiments, the description is simple, and reference may be made to the partial description of the method embodiments for relevant points.
The foregoing embodiments have described the present invention in detail, and the principle and embodiments of the present invention are explained by applying specific examples herein, and the descriptions of the foregoing embodiments are only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (8)
1. A frequency hopping signal blind detection method is characterized by comprising the following processes:
s1, decomposing a high-speed data stream into a low-speed data stream which can be processed by a digital signal processor in real time;
s2, mapping the low-frequency data stream to a time-frequency domain, and obtaining time-frequency data by adopting spectrogram transformation and incoherent accumulation, wherein the time-frequency data comprises time data and frequency data;
s3, constructing a frequency sequence according to the time-frequency data, and obtaining time-frequency differential sequences of time-frequency characteristic curve differences of different modulation signals by combining a D-type weighted differential method;
and S4, identifying the frequency hopping signal according to the average value of the peak values in the time-frequency difference sequence.
2. The blind frequency hopping signal detection method according to claim 1, wherein: in step S1, the decomposition of the high-speed data stream into a low-speed data stream that can be processed by the digital signal processor in real time comprises the following steps:
s11, carrying out digital processing on high-speed data by adopting a high-speed A/D conversion technology;
s12, dividing a receiving bandwidth into a plurality of sub-channels by adopting a low-pass filter;
and S13, performing D-time extraction on the data stream in each sub-channel, wherein the sampling rate is 1/D of the original sampling rate.
3. The blind frequency hopping signal detection method according to claim 1, wherein: in step S2, mapping the low data stream to a time-frequency domain, and obtaining time-frequency data by using spectrogram transform and incoherent integration includes the following steps:
s21, performing short-time Fourier transform on the low-frequency data stream;
s22, performing summation operation according to the input signal, the window function type and the window length to obtain a time-frequency amplitude value output by the kth channel at the nth moment;
s23, squaring the time-frequency amplitude to obtain a spectrogram transformation result;
s24, performing incoherent accumulation on the spectrogram transformation result according to the accumulation length and the window function length to obtain data to be detected;
and S25, inputting the data to be detected into an IVI-CFAR detector to detect effective signals.
4. The blind frequency hopping signal detection method according to claim 1, wherein: in step S3, a frequency sequence is constructed according to the time-frequency data, and a difference result of time-frequency characteristic curve differences of different modulation signals in the frequency sequence is obtained by combining a D-type weighted difference method, which specifically includes the following steps:
s31, selecting a fixed frequency signal, an LFM signal and a frequency modulation signal which are commonly used in the communication field;
s32, setting a hop period, a coding rate and a spectrogram transformation window length of a frequency hopping signal;
s33, selecting the maximum value of the frequency amplitude of each time point on the obtained time frequency data to construct a frequency sequence;
and S34, obtaining a difference result of the time-frequency characteristic curve difference after different modulation signals are modulated by adopting a D repetition frequency difference method, namely obtaining a time-frequency difference sequence.
5. The blind frequency hopping signal detection method according to claim 1, wherein: in step S4, the frequency hopping signal is identified according to the average value of the peak values in the time-frequency difference sequence, and the specific process includes the following steps:
s41, counting the number n of peak values in a time-frequency difference sequence by adopting a D repetition frequency difference method;
when the peak number n is less than 0, determining as a fixed frequency signal, and when the peak number is greater than 0, entering step S42;
s42, performing linear fitting on each peak value position in the peak value number n to obtain corresponding fitting dispersion;
s43, setting the fitting dispersion as a judgment threshold value as e;
s44, judging whether the fitting dispersion of each peak value is smaller than e;
when the fitting dispersion is larger than e, determining that the FSK signal is generated;
and when the fitting dispersion is less than e, the step S45 is carried out;
s45, estimating a peak value average value of the time-frequency difference sequence;
when the estimation of the peak value mean value of the time frequency differential sequence is larger than ef, the frequency hopping signal is judged;
when the estimate of the peak-to-average value of the time-frequency differential sequence is less than ef, the LFM signal is determined.
6. The blind frequency hopping signal detection method according to claim 5, wherein: in step S43, it is determined that the fitting dispersion of the threshold value is e 0.1.
7. A system for implementing the frequency hopping signal blind detection method according to any one of claims 1 to 6, comprising:
the high-speed data flow decomposition module (10), the high-speed data flow decomposition module (10) is used for decomposing the high-speed data flow into a low-speed data flow which can be processed by a digital signal processor in real time;
the time-frequency data obtaining module (20), the time-frequency data obtaining module (20) is used for mapping the low-frequency data stream to a time-frequency domain, and obtaining time-frequency data by adopting spectrogram transformation and incoherent accumulation, wherein the time-frequency data comprises time data and frequency data;
the time-frequency differential sequence obtaining module (30), the time-frequency differential sequence obtaining module (30) is used for constructing a frequency sequence according to the time-frequency data, and the time-frequency differential sequence of the time-frequency characteristic curve difference of different modulation signals is obtained by combining a D-type weighted differential method;
and the frequency hopping signal identification module (40), wherein the frequency hopping signal identification module (40) is used for identifying the frequency hopping signal according to the average value of the peak values in the time-frequency difference sequence.
8. An apparatus for implementing the frequency hopping signal blind detection method according to any one of claims 1 to 6, comprising:
a processor;
a memory;
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs for a computer to perform the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211204842.XA CN115567163A (en) | 2022-09-29 | 2022-09-29 | Frequency hopping signal blind detection method, system and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211204842.XA CN115567163A (en) | 2022-09-29 | 2022-09-29 | Frequency hopping signal blind detection method, system and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115567163A true CN115567163A (en) | 2023-01-03 |
Family
ID=84742205
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211204842.XA Pending CN115567163A (en) | 2022-09-29 | 2022-09-29 | Frequency hopping signal blind detection method, system and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115567163A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786398A (en) * | 2024-02-28 | 2024-03-29 | 杰创智能科技股份有限公司 | Frequency hopping signal characteristic identification method, system, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060140251A1 (en) * | 2004-05-04 | 2006-06-29 | Colin Brown | Frequency hopping communication system |
| WO2007064286A2 (en) * | 2005-12-02 | 2007-06-07 | Telefonaktiebolaget Lm Ericsson (Publ) | Hopping pilot pattern for telecommunications |
| CN107612587A (en) * | 2017-06-20 | 2018-01-19 | 西安电子科技大学 | A kind of method for parameter estimation for being used for Frequency Hopping Signal in frequency hopping non-cooperative communication |
| CN108462509A (en) * | 2018-03-26 | 2018-08-28 | 西安电子科技大学 | Asynchronous frequency hopping net platform method for separating based on time-frequency figure information |
| CN114785379A (en) * | 2022-06-02 | 2022-07-22 | 厦门大学马来西亚分校 | Underwater sound JANUS signal parameter estimation method and system |
-
2022
- 2022-09-29 CN CN202211204842.XA patent/CN115567163A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060140251A1 (en) * | 2004-05-04 | 2006-06-29 | Colin Brown | Frequency hopping communication system |
| WO2007064286A2 (en) * | 2005-12-02 | 2007-06-07 | Telefonaktiebolaget Lm Ericsson (Publ) | Hopping pilot pattern for telecommunications |
| CN107612587A (en) * | 2017-06-20 | 2018-01-19 | 西安电子科技大学 | A kind of method for parameter estimation for being used for Frequency Hopping Signal in frequency hopping non-cooperative communication |
| CN108462509A (en) * | 2018-03-26 | 2018-08-28 | 西安电子科技大学 | Asynchronous frequency hopping net platform method for separating based on time-frequency figure information |
| CN114785379A (en) * | 2022-06-02 | 2022-07-22 | 厦门大学马来西亚分校 | Underwater sound JANUS signal parameter estimation method and system |
Non-Patent Citations (2)
| Title |
|---|
| 宋铁，周林，曹婷: "基于 IVI-CFAR 的模糊恒虚警", 计算机技术与应用, vol. 42, no. 1, 31 January 2016 (2016-01-31), pages 1 - 5 * |
| 郭海召: "跳频信号的检测、参数估计与分选算法研究", 中国优秀硕士学位论文全文数据库信息科技辑, 15 February 2017 (2017-02-15), pages 1 - 89 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786398A (en) * | 2024-02-28 | 2024-03-29 | 杰创智能科技股份有限公司 | Frequency hopping signal characteristic identification method, system, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hwang et al. | Sinusoidal modeling and prediction of fast fading processes | |
| Gardner | A new method of channel identification | |
| CN109167746B (en) | Continuous wave and pulse signal quick identification device | |
| CN101662305B (en) | Pseudo-random code estimation method of direct sequence spread spectrum system | |
| CN107835036B (en) | Non-cooperative frequency hopping signal cracking method | |
| CN109450488B (en) | Blind estimation of pseudo code period of band pulse shaping spread spectrum signal under narrow band interference | |
| CN113972929B (en) | Method for capturing spread spectrum signal under high dynamic Doppler | |
| CN109975771B (en) | Broadband digital channelization method based on signal third-order phase difference | |
| CN115567163A (en) | Frequency hopping signal blind detection method, system and equipment | |
| CN114785379A (en) | Underwater sound JANUS signal parameter estimation method and system | |
| Lobov et al. | Optimum estimation and filtering of the ionospheric channel dispersion characteristics slope algorithms | |
| CN113315540A (en) | Modulation and demodulation method based on pseudorandom phase sequence spread spectrum signal | |
| Noguet et al. | Cyclostationarity detectors for cognitive radio: architectural tradeoffs | |
| Rao et al. | Wavelet based spectrum sensing techniques in cognitive radio | |
| CN114650108B (en) | Method and system for detecting signal of transform domain communication system | |
| Mohapatra et al. | Performance evaluation of cyclostationary based spectrum sensing in cognitive radio network | |
| CN113489552B (en) | Frequency hopping signal detection method based on time-frequency spectrum matrix local variance | |
| CN108900211A (en) | A method of ultra-wideband impulse radio interference is inhibited using correlation receiver stencil design | |
| JPH09116461A (en) | Method and device for separating and estimating noise included in spread spectrum signal | |
| KR20180124501A (en) | Apparatus and method for estimating hopping frequency | |
| Bektas et al. | Energy based spectrum sensing using wavelet transform for fading channels | |
| CN112929053B (en) | Frequency hopping signal feature extraction and parameter estimation method | |
| Arjun et al. | Performance Analysis of Wavelet based Spectrum Sensing and Conventional Spectrum Sensing in Fading Environment for Cognitive Radios | |
| CN113472392B (en) | Frequency band detection method for broadband power line carrier communication | |
| Wang et al. | An Interference Sensing Algorithm Based on Duration Units of Frequency Points for Adaptive Frequency-Hopping System |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |