CN112838909A - Communication interference detection method based on Gaussian eye pattern texture entropy characteristics - Google Patents
Communication interference detection method based on Gaussian eye pattern texture entropy characteristics Download PDFInfo
- Publication number
- CN112838909A CN112838909A CN202011608168.2A CN202011608168A CN112838909A CN 112838909 A CN112838909 A CN 112838909A CN 202011608168 A CN202011608168 A CN 202011608168A CN 112838909 A CN112838909 A CN 112838909A
- Authority
- CN
- China
- Prior art keywords
- gaussian
- signal
- interference
- eye
- eye pattern
- 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.)
- Granted
Links
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
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0045—Arrangements at the receiver end
- H04L1/0047—Decoding adapted to other signal detection operation
- H04L1/0048—Decoding adapted to other signal detection operation in conjunction with detection of multiuser or interfering signals, e.g. iteration between CDMA or MIMO detector and FEC decoder
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Monitoring And Testing Of Transmission In General (AREA)
Abstract
The invention provides a communication interference detection method based on Gaussian eye pattern texture entropy characteristics, and mainly solves the problems that the existing interference detection method is low in detection rate and long in detection time under low interference-to-signal ratio. The method comprises the following implementation steps: (1) generating a Gaussian eye diagram of the wireless communication signal to be detected; (2) calculating texture entropy characteristics of the Gaussian eye diagram; (3) setting interference detection test statistic; (4) and carrying out inspection judgment to obtain an interference detection result. The method has the advantages of higher interference detection probability and high detection speed under high interference-to-signal ratio and low interference-to-signal ratio, and effectively solves the problems of low detection probability and overlong detection time under low interference-to-signal ratio in the conventional interference detection method.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and further relates to a communication interference detection method based on Gaussian eye pattern texture entropy characteristics in the technical field of signal and information processing. The invention can be used for detecting the noise modulation interference signals of the wireless communication system and the communication countermeasure system.
Background
The noise-modulated interference signal refers to electronic interference that modulates the frequency, amplitude and phase of a signal with low-frequency random noise, and is a type of interference signal commonly found in a communication system. The communication quality is severely affected if the communication system is disturbed. If the interference signal can be effectively detected, then the reliability and the accuracy of the communication system can be improved by adopting corresponding anti-interference measures, so that the interference detection technology is a key technology in an anti-interference system of the communication system. The interference-to-signal ratio refers to the proportional relationship between the interference signal power and the communication signal power in the communication system. Most of the existing interference detection technologies are used for detecting an interference signal when no communication signal exists, and if the communication signal exists, the communication signal is mistaken as the interference signal; the interference detection technology for the simultaneous existence of the communication signal and the interference signal cannot effectively detect the interference signal under the condition of lower interference-to-signal ratio.
An interference detection method based on frequency domain transformation under low interference-to-signal ratio is disclosed in a patent document DSSS frequency domain interference detection method (patent application No. 211102905758, application publication No. CN102307055A) applied by fifty-four research institute of Chinese electronic science and technology group company. The method comprises the following specific steps: carrying out FFT (fast Fourier transform) on a received signal in a direct-spread communication system to obtain frequency domain information; preprocessing the frequency domain information, and averaging the frequency domains of a plurality of periods to enable the frequency domain information to be more accurate; calculating the 3dB bandwidth of the normalized frequency spectrum of the frequency domain information, and judging whether narrow-band or width interference exists in the time domain spread spectrum signal according to the value; and (3) adopting a front-back comparison method for narrow-band interference, and carrying out combined judgment on the similarity and the flatness of signal spectrum envelopes for wide-band interference to obtain a final detection result. The method is capable of detecting the presence of interfering signals at low interference to signal ratios. However, the method still has the disadvantage that, because the method estimates the signal bandwidth in the frequency domain, when the interference signal ratio is low, that is, the interference signal is smaller than the background noise, the frequency spectrum of the interference signal is drowned by the noise frequency spectrum, the bandwidth of the interference signal is not accurately estimated, and part of the noise is detected as the interference signal, so that the problem of false detection exists when the interference signal of the wireless communication system is detected under the low interference signal ratio.
The patent document of the university of sienna traffic in its application, "a GNSS deception jamming detection method and system based on LSTM in signal capturing phase" (patent application No. 201910404639.9, application publication No. CN 110231634 a) discloses a GNSS deception jamming detection method based on LSTM in signal capturing phase. The method comprises the following specific steps: in a signal acquisition stage, a two-dimensional search array with Doppler frequency shift and code phase as axes, namely a matrix A, is generated by a Global Navigation Satellite System (GNSS) receiver; extracting parameters of the matrix A at a plurality of moments to form characteristic parameters, and taking the obtained characteristic parameters as a training data set; training a Long Short-Term Memory (LSTM) neural network model through the obtained training data set, and obtaining a well-trained LSTM neural network model after training; and detecting the signals received by the GNSS receiver through the trained LSTM neural network model to finish the GNSS deception jamming detection based on the LSTM in the signal capturing stage. The method can extract relatively stable characteristic values by training the neural network model, and has higher interference detection accuracy at low interference-to-signal ratio. However, the method has the disadvantages that a large number of samples are needed to train the neural network model, the calculation amount is large, and the problem of long detection time exists when the interference signal detection is carried out on the wireless communication system.
Disclosure of Invention
The invention aims to provide a communication interference detection method based on Gaussian eye pattern texture aiming at the defects in the prior art, and is used for solving the problems of poor interference detection performance and overlong detection time under low interference-to-signal ratio.
The technical idea for realizing the purpose of the invention is that a signal to be analyzed is mapped to generate a Gaussian eye pattern through Gaussian mapping, when an interference signal exists, the waveform of the interference signal is superposed on the Gaussian eye pattern of the communication signal, the shape and element distribution of the Gaussian eye pattern change no matter under low interference-signal ratio or high interference-signal ratio, the texture characteristic of the corresponding Gaussian eye pattern changes, and test statistics are set according to the texture entropy characteristic of the Gaussian eye pattern of the signal to be analyzed and the entropy characteristic of the Gaussian eye pattern when the communication system is normal when no interference exists, and are compared with a set threshold, so that the detection of the interference signal in a communication countermeasure system and a wireless communication system under high interference-signal ratio and low interference-signal ratio is realized.
In order to achieve the above object, the present invention comprises the following steps:
(1) generating a Gaussian eye diagram of the wireless communication signal to be detected:
(1a) normalizing the amplitude of the wireless communication signal to be detected with the length of N after digital-to-analog conversion, and cutting off the normalized signal every K lengths to obtain M sections of signals, wherein K is more than or equal to 100 and less than or equal to 250, and N is more than or equal to M.K;
(1b) mapping the length K and the amplitude of each truncated section of signal to an r multiplied by d two-dimensional Gaussian eye diagram from left to right and from top to bottom, wherein the value of r is equal to K,
λ represents a mapping parameter with a value range of [0.01,0.04 ]]，
Represents a rounding up operation;
(1c) the value of each element in the gaussian eye is calculated as follows:
wherein E isi,jRepresents the element value of ith row and jth column in the Gaussian eye diagram, sigma represents the summation operation, M represents the serial number of the truncation signal, M is 1,2, …, M, e(·)The exponential operation with a natural constant e as the base is shown, a represents the regulating parameter, and the value range is [0.8,2 ]]，yi,jRepresenting element E in a Gaussian eye diagrami,jCorresponding position information, xm(k) Representing the m-th signal after truncation, xm(k) X ((m-1) K + K), K representing the time corresponding to the kth amplitude in each segment of the truncated signal, K being 1,2, …, K;
(2) calculating texture entropy characteristics of the Gaussian eye diagram:
(2a) compressing each element value in the Gaussian eye pattern by using a linear transformation formula to obtain a Gaussian gray eye pattern;
(2b) respectively calculating gray level co-occurrence matrixes of the Gaussian gray level eye pattern in four directions of 0 degree, 45 degrees, 90 degrees and 135 degrees;
(2c) filling the average value of the elements at the same position of the four gray level co-occurrence matrixes into the elements at the corresponding position in the average gray level co-occurrence matrix;
(2d) calculating texture entropy characteristics of a Gaussian eye pattern of the signal to be detected by using the average gray level co-occurrence matrix;
(2e) test statistics were set as follows:
where T represents the test statistic, H1Texture entropy characteristics of the Gaussian eye diagram representing the signal to be detected, H0Texture entropy characteristics of a Gaussian eye diagram of the signal in normal operation of the communication system without the interference signal, which are obtained by calculation by the same formula in the step (2d), are represented, and | represents absolute value operation;
(3) and (3) checking and judging interference:
and comparing the test statistic T with an interference detection threshold, if T is greater than gamma, judging that an interference signal exists in the communication system, and if T is less than or equal to gamma, judging that the interference signal does not exist in the communication system.
The interference detection threshold γ is calculated by the following formula:
wherein c represents an adjusting parameter, and the value range is [0.2,0.4 ]]，erfc-1Denotes the inverse operation on the complementary error function, PfaRepresenting the false alarm probability set by the user,
representing the communication system noise floor power.
The invention has the following advantages:
first, because the gaussian eye pattern of the signal is generated by mapping, more data characteristics of the communication signal can be reserved, when an interference signal exists, the interference signal can be superposed on the eye pattern of the communication signal, corresponding position elements in the gaussian eye pattern corresponding to the superposed part in the eye pattern are increased, the texture of the gaussian eye pattern can also change, even under a low interference-to-signal ratio, the texture change of the gaussian eye pattern is obvious, a test statistic and a threshold are constructed by using texture entropy characteristics to detect the interference signal, and the problem of false detection caused by inaccurate extraction of interference signal characteristics under the low interference-to-signal ratio in the prior art is solved, so that the invention not only can have high interference detection probability to a communication system under the high interference-to-signal ratio, but also has high interference detection probability under the low interference-to-signal ratio.
Secondly, when calculating the texture entropy characteristics of the Gaussian eye pattern, each element value in the Gaussian eye pattern is compressed by using a linear transformation formula, so that the line number and the column number of the gray level co-occurrence matrix are reduced while the clear texture of the Gaussian eye pattern is ensured, the calculation amount for calculating the texture entropy characteristics of the Gaussian eye pattern is reduced, the calculation speed is high, the problems that in the prior art, a large number of training samples are required for supporting when a training model extracts the characteristics, the calculation amount is large, and the interference detection speed is low are solved, and the requirement for performing rapid interference detection on a communication system can be met.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a simulation of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The implementation steps of the present invention are further described with reference to fig. 1.
Step 1, generating a Gaussian eye diagram of the wireless communication signal to be detected.
Normalizing the amplitude of the wireless communication signal to be detected with the length of N after the digital-to-analog conversion, and cutting the normalized signal every K lengths to obtain M sections of signals, wherein K is more than or equal to 100 and less than or equal to 250, and N is more than or equal to M.K.
Mapping the length K and the amplitude of each truncated section of signal to an r multiplied by d two-dimensional Gaussian eye diagram from left to right and from top to bottom, wherein the value of r is equal to K,
λ represents a mapping parameter with a value range of [0.01,0.04 ]]，
Indicating a rounding up operation.
The value of each element in the gaussian eye is calculated as follows:
wherein E isi,jRepresents the element value of ith row and jth column in the Gaussian eye diagram, sigma represents the summation operation, M represents the serial number of the truncation signal, M is 1,2, …, M, e(·)The exponential operation with a natural constant e as the base is shown, a represents the regulating parameter, and the value range is [0.8,2 ]]，yi,jRepresenting element E in a Gaussian eye diagrami,jCorresponding position information, xm(k) Representing the m-th signal after truncation, xm(k) X ((m-1) K + K), K representing the time instant corresponding to the kth amplitude in each segment of the truncated signal, K being 1,2, …, K.
The position information is calculated by the following formula:
where i represents the serial number of the rows in the gaussian eye diagram, j represents the serial number of the columns in the gaussian eye diagram, and d represents the total number of rows in the gaussian eye diagram.
And 2, calculating texture entropy characteristics of the Gaussian eye diagram.
Compressing each element value in the Gaussian eye diagram according to the following linear transformation formula to obtain the Gaussian gray eye diagram:
wherein D isi,jRepresenting the compressed values of the elements, E, in the ith row and jth column of the Gaussian gray eye diagrammaxRepresenting the maximum of the elements in a gaussian eye-diagram,
indicating a rounding down operation and G indicating a gray level, either 32 or 64.
And respectively calculating gray level co-occurrence matrixes of the compressed eye diagram gray level matrixes in four directions of 0 degree, 45 degrees, 90 degrees and 135 degrees.
And filling the average value of the elements at the same position of the four gray level co-occurrence matrixes into the elements at the corresponding position in the average gray level co-occurrence matrix.
Calculating the texture entropy characteristics of the Gaussian eye diagram of the signal to be detected according to the following formula:
wherein z represents the serial number of the row in the average gray level co-occurrence matrix of the signal to be detected, z is 1,2, …, G, l represents the serial number of the column in the average gray level co-occurrence matrix of the signal to be detected, l is 1,2, …, G, p1(z, l) represents the element value of the z row and l column in the average gray level co-occurrence matrix of the signal to be detected, and log (-) represents the base-10 logarithmic operation.
Test statistics are set according to the following formula:
where T represents the test statistic, H0The texture entropy characteristic of the Gaussian eye pattern of the signal in normal working of the communication system when no interference signal exists needs to be calculated in advance, the calculation formula is the same as the formula for calculating the texture entropy characteristic of the Gaussian eye pattern, and | represents absolute value operation.
Step 3, checking and judging the interference:
and comparing the test statistic T with an interference detection threshold gamma, if T is larger than gamma, judging that an interference signal exists in the communication system, and if T is smaller than or equal to gamma, judging that the interference signal does not exist in the communication system.
The interference detection threshold is set according to the following formula:
wherein c represents an adjusting parameter, and the value range is [0.2,0.4 ]]，erfc-1Denotes the inverse operation on the complementary error function, PfaRepresenting the false alarm probability set by the user,
representing the communication system noise floor power.
The effects of the present invention can be further explained by the following simulations.
1. Simulation conditions are as follows:
all the following simulation experiments are realized by MATLAB2018b software under a Win7 operating system; the communication signal Binary Phase Shift Keying (BPSK) signal used in the simulation experiment is a noise amplitude modulation interference signal, a noise frequency modulation interference signal and a noise Phase modulation interference signal. Setting sampling frequency f in simulation experiments500MHz, interference signal frequency fJ370MHz, white gaussian noise with mean 0 and variance 1, false alarm probabilityIs set to be Pfa＝0.01。
2. And analyzing simulation contents and simulation results thereof.
The simulation experiment of the invention is that the method and the common prior art (energy detection method) are respectively adopted to carry out 1000 times of detection on BPSK (binary phase shift keying) existing communication signals, and when interference signals are noise amplitude modulation interference signals, noise frequency modulation interference signals and noise phase modulation interference signals, the detection accuracy of the two methods under different interference-signal ratios is respectively calculated, and the detection accuracy is the ratio of the number of times of correctly detecting the interference signals to the total number of times of detection. The detection accuracy of these two methods for different interference signals under different interference-to-signal ratios is shown in fig. 2.
The abscissa in fig. 2 represents the interference-to-signal ratio in the range of [ -35,10] dB, and the ordinate represents the interference signal detection accuracy. FIG. 2(a) is a graph of the detection probability of different interference signals under different interference-to-signal ratios by using the method of the present invention, and a curve marked by a circle represents the detection accuracy of a noise amplitude modulation interference signal by using the method of the present invention; the curve marked by a square represents a detection accuracy curve of the noise frequency modulation interference signal by adopting the method; the curve marked by diamonds represents the detection accuracy of the noise phase modulation interference signal by the method of the present invention. FIG. 2(b) is a graph of probability of detecting different interference signals under different interference-to-signal ratios by using a prior art energy detection method, and a curve marked by a triangle represents a curve of accuracy of detecting noise amplitude-modulated interference signals by using the energy detection method; a curve marked by a five-pointed star represents a detection accuracy curve of the noise frequency modulation interference signal by adopting an energy detection method; and a curve marked by a hexagram represents the detection accuracy of the noise phase modulation interference signal by adopting an energy detection method.
As can be seen from the simulation result of FIG. 2(a), under the same simulation condition, the method of the present invention has higher detection accuracy for three noise modulation interference signals under low interference-to-signal ratio, the lowest interference-to-signal ratio which can be detected reaches-8 dB, and the detection accuracy reaches 100%. As can be seen from the simulation result of fig. 2(b), under the same simulation conditions as those of the present invention, the energy detection method has a lower detection accuracy for three noise modulation interference signals at a low interference-to-signal ratio, has a higher interference detection accuracy only for interference signals with an interference-to-signal ratio greater than-2 dB, and has a lower detection accuracy for interference signals at a low interference-to-signal ratio.
In summary, the communication interference detection method based on the texture entropy features of the gaussian eye pattern disclosed by the invention solves the problems of low detection speed and low detection probability under low interference-to-signal ratio in the technical field of interference detection by analyzing the texture entropy features of the gaussian eye pattern. The invention provides an interference signal detection method which can carry out rapid detection and is more effective under high interference-signal ratio and low interference-signal ratio.
Claims (5)
1. A communication interference detection method based on Gaussian eye pattern texture entropy features is characterized in that a Gaussian eye pattern of a signal to be detected is generated, and test statistics and a decision threshold are determined according to the texture entropy features of the Gaussian eye pattern, and the method specifically comprises the following steps:
(1) generating a Gaussian eye diagram of the wireless communication signal to be detected:
(1a) normalizing the amplitude of the wireless communication signal to be detected with the length of N after digital-to-analog conversion, and cutting off the normalized signal every K lengths to obtain M sections of signals, wherein K is more than or equal to 100 and less than or equal to 250, and N is more than or equal to M.K;
(1b) mapping the length K and the amplitude of each truncated section of signal to an r multiplied by d two-dimensional Gaussian eye diagram from left to right and from top to bottom, wherein the value of r is equal to K,
λ represents a mapping parameter with a value range of [0.01,0.04 ]]，
Represents a rounding up operation;
(1c) the value of each element in the gaussian eye is calculated as follows:
wherein E isi,jRepresents the element value of ith row and jth column in the Gaussian eye diagram, sigma represents the summation operation, M represents the serial number of the truncation signal, M is 1,2, …, M, e(·)The exponential operation with a natural constant e as the base is shown, a represents the regulating parameter, and the value range is [0.8,2 ]]，yi,jRepresenting element E in a Gaussian eye diagrami,jCorresponding position information, xm(k) Representing the m-th signal after truncation, xm(k) X ((m-1) K + K), K representing the time corresponding to the kth amplitude in each segment of the truncated signal, K being 1,2, …, K;
(2) calculating texture entropy characteristics of the Gaussian eye diagram:
(2a) compressing each element value in the Gaussian eye pattern by using a linear transformation formula to obtain a Gaussian gray eye pattern;
(2b) respectively calculating gray level co-occurrence matrixes of the Gaussian gray level eye pattern in four directions of 0 degree, 45 degrees, 90 degrees and 135 degrees;
(2c) filling the average value of the elements at the same position of the four gray level co-occurrence matrixes into the elements at the corresponding position in the average gray level co-occurrence matrix;
(2d) calculating texture entropy characteristics of a Gaussian eye pattern of the signal to be detected by using the average gray level co-occurrence matrix;
(2e) test statistics were set as follows:
where T represents the test statistic, H1Texture entropy characteristics of the Gaussian eye diagram representing the signal to be detected, H0Texture entropy characteristics of a Gaussian eye diagram of the signal in normal operation of the communication system without the interference signal, which are obtained by calculation by the same formula in the step (2d), are represented, and | represents absolute value operation;
(3) and (3) checking and judging interference:
and comparing the test statistic T with an interference detection threshold, if T is greater than gamma, judging that an interference signal exists in the communication system, and if T is less than or equal to gamma, judging that the interference signal does not exist in the communication system.
2. The method for detecting communication interference based on entropy characteristics of Gaussian eye pattern textures as claimed in claim 1, wherein the method comprises the following steps: the position information in step (1c) is calculated by the following formula:
where i represents the serial number of the rows in the gaussian eye diagram, j represents the serial number of the columns in the gaussian eye diagram, and d represents the total number of rows in the gaussian eye diagram.
3. The method for detecting communication interference based on entropy characteristics of Gaussian eye pattern textures as claimed in claim 1, wherein the method comprises the following steps: the linear transformation formula described in step (2a) is as follows:
wherein D isi,jRepresenting the value of the element in the ith row and jth column of a Gaussian-grey eye-diagram, EmaxRepresenting the maximum of the elements in the gaussian eye,
indicating a rounding down operation and G indicating a gray level, either 32 or 64.
4. The method for detecting communication interference based on entropy characteristics of Gaussian eye pattern textures as claimed in claim 1, wherein the method comprises the following steps: in the step (2d), the texture entropy feature of the gaussian eye pattern of the signal to be detected is calculated by the following formula:
wherein z represents the serial number of the row in the average gray level co-occurrence matrix of the signal to be detected, z is 1,2, …, G, l represents the serial number of the column in the average gray level co-occurrence matrix of the signal to be detected, l is 1,2, …, G, p1(z, l) represents the element value of the z row and l column in the average gray level co-occurrence matrix of the signal to be detected, and log (-) represents the base-10 logarithmic operation.
5. The method for detecting communication interference based on entropy characteristics of Gaussian eye pattern textures as claimed in claim 1, wherein the method comprises the following steps: the interference detection threshold γ in step (3) is calculated by the following formula:
wherein c represents an adjusting parameter, and the value range is [0.2,0.4 ]]，erfc-1Denotes the inverse operation on the complementary error function, PfaRepresenting the false alarm probability set by the user,
representing the communication system noise floor power.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011608168.2A CN112838909B (en) | 2020-12-30 | 2020-12-30 | Communication interference detection method based on Gaussian eye pattern texture entropy characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011608168.2A CN112838909B (en) | 2020-12-30 | 2020-12-30 | Communication interference detection method based on Gaussian eye pattern texture entropy characteristics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112838909A true CN112838909A (en) | 2021-05-25 |
| CN112838909B CN112838909B (en) | 2022-03-04 |
Family
ID=75925427
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011608168.2A Active CN112838909B (en) | 2020-12-30 | 2020-12-30 | Communication interference detection method based on Gaussian eye pattern texture entropy characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112838909B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115102674A (en) * | 2022-06-17 | 2022-09-23 | 西安电子科技大学 | Bi-LSTM network-based high-speed link eye pattern prediction method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180083604A1 (en) * | 2016-09-21 | 2018-03-22 | Mstar Semiconductor, Inc. | Eye pattern measurement apparatus, and clock and data recovery system and method of the same |
| CN108631893A (en) * | 2017-03-17 | 2018-10-09 | 成都信息工程大学 | A kind of GMSK channel disturbances detection method |
| CN109274438A (en) * | 2018-09-20 | 2019-01-25 | 扬州万方电子技术有限责任公司 | A kind of adaptive double threshold co-interfere detection method and device |
| CN109510644A (en) * | 2018-11-23 | 2019-03-22 | 西安电子科技大学 | Direct-sequence spread-spectrum communication signal interference detection method based on eye figure energy |
| CN110932806A (en) * | 2019-10-31 | 2020-03-27 | 西安电子科技大学 | Multi-antenna spectrum sensing method under alpha stable noise fading channel |
-
2020
- 2020-12-30 CN CN202011608168.2A patent/CN112838909B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180083604A1 (en) * | 2016-09-21 | 2018-03-22 | Mstar Semiconductor, Inc. | Eye pattern measurement apparatus, and clock and data recovery system and method of the same |
| CN108631893A (en) * | 2017-03-17 | 2018-10-09 | 成都信息工程大学 | A kind of GMSK channel disturbances detection method |
| CN109274438A (en) * | 2018-09-20 | 2019-01-25 | 扬州万方电子技术有限责任公司 | A kind of adaptive double threshold co-interfere detection method and device |
| CN109510644A (en) * | 2018-11-23 | 2019-03-22 | 西安电子科技大学 | Direct-sequence spread-spectrum communication signal interference detection method based on eye figure energy |
| CN110932806A (en) * | 2019-10-31 | 2020-03-27 | 西安电子科技大学 | Multi-antenna spectrum sensing method under alpha stable noise fading channel |
Non-Patent Citations (2)
| Title |
|---|
| KANG, SM等: "Mitigation of Optical Interference Noise by Optical Carrier Cancelation in Self-Coherent Reflective PON Uplink Transmission", 《IEEE PHOTONICS JOURNAL》 * |
| 王晶: "北斗民用信号干扰检测技术研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115102674A (en) * | 2022-06-17 | 2022-09-23 | 西安电子科技大学 | Bi-LSTM network-based high-speed link eye pattern prediction method |
| CN115102674B (en) * | 2022-06-17 | 2023-08-22 | 西安电子科技大学 | Bi-LSTM network-based high-speed link eye diagram prediction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112838909B (en) | 2022-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110244271B (en) | Radar radiation source sorting and identifying method and device based on multiple synchronous compression transformation | |
| CN108600135B (en) | Method for identifying signal modulation mode | |
| CN106330385B (en) | A kind of interference type recognition methods | |
| BARBER et al. | SAR sea ice discrimination using texture statistics- A multivariate approach | |
| CN102967854B (en) | Multi-fractal detection method of targets in FRFT (Fractional Fourier Transformation) domain sea clutter | |
| CN107577999B (en) | Radar signal intra-pulse modulation mode identification method based on singular value and fractal dimension | |
| CN101799921B (en) | Cloud detection method of optic remote sensing image | |
| CN103236063B (en) | Based on the SAR image oil spilling detection method of multiple dimensioned spectral clustering and decision level fusion | |
| CN110175560A (en) | A kind of radar signal intra-pulse modulation recognition methods | |
| CN111965632B (en) | Radar target detection method based on Riemann manifold dimensionality reduction | |
| CN102063907B (en) | Steganalysis method for audio spread-spectrum steganography | |
| CN109212503B (en) | Offshore target detection method and device based on AR spectrum fractal correlation characteristics | |
| CN112838909B (en) | Communication interference detection method based on Gaussian eye pattern texture entropy characteristics | |
| CN107450054B (en) | A kind of adaptive Coherent Noise in GPR Record denoising method | |
| CN112859012B (en) | Radar spoofing interference identification method based on cascade convolution neural network | |
| CN109543643A (en) | Carrier signal detection method based on one-dimensional full convolutional neural networks | |
| CN114895263A (en) | Radar active interference signal identification method based on deep migration learning | |
| CN115984778A (en) | Multi-feature optimization based method for rapidly and dynamically monitoring Sentinel-1 data in flood | |
| CN109359523A (en) | A kind of satellite navigation interference type recognition methods based on SVM multi-classification algorithm | |
| Yang et al. | Evaluation and mitigation of rain effect on wave direction and period estimation from X-band marine radar images | |
| CN102567726B (en) | Technology for automatically extracting floating ice in polar ice sheet edge regions | |
| CN115932898A (en) | Efficient interference identification method for satellite navigation system | |
| Sahu et al. | Characterization of precipitation in the subdivisions of the Mahanadi River basin, India | |
| CN110850420B (en) | Fisher SVM sonar signal discrimination method based on marble loss | |
| CN104573662B (en) | A kind of cloud sentences method and 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |