CN113156382A - Signal identification method and device for vehicle-mounted range radar - Google Patents
Signal identification method and device for vehicle-mounted range radar Download PDFInfo
- Publication number
- CN113156382A CN113156382A CN202110397704.7A CN202110397704A CN113156382A CN 113156382 A CN113156382 A CN 113156382A CN 202110397704 A CN202110397704 A CN 202110397704A CN 113156382 A CN113156382 A CN 113156382A
- Authority
- CN
- China
- Prior art keywords
- signal
- sampling
- amplitude
- signals
- frequency response
- 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
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000013598 vector Substances 0.000 claims abstract description 71
- 238000005070 sampling Methods 0.000 claims abstract description 63
- 230000004044 response Effects 0.000 claims abstract description 44
- 238000012549 training Methods 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 11
- 230000006870 function Effects 0.000 claims description 20
- 230000003595 spectral effect Effects 0.000 claims description 12
- 230000002452 interceptive effect Effects 0.000 claims description 6
- 239000000523 sample Substances 0.000 description 15
- 238000012360 testing method Methods 0.000 description 11
- 238000012706 support-vector machine Methods 0.000 description 8
- 238000001514 detection method Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000010200 validation analysis Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The invention discloses a signal identification method and a signal identification device for a vehicle-mounted range radar, wherein the method comprises the following steps: acquiring a plurality of sampling signals, wherein each sampling signal is a target echo signal or an interference signal; processing each sampling signal to obtain a plurality of processed signals; performing Fourier transform on each processed signal to obtain amplitude-frequency response information corresponding to each sampling signal; determining a feature vector corresponding to each of the sampled signals according to the amplitude-frequency response information; training a binary classification vector machine by using the feature vector; and identifying the received signal by using the trained binary vector machine to determine that the received signal belongs to the target echo signal or the interference signal.
Description
Technical Field
The invention relates to the technical field of vehicle-mounted radars, in particular to the field of a signal identification method and a signal identification device for a vehicle-mounted range radar.
Background
In practical application, because the road traffic condition is complicated and changeable, and a large number of automobiles are provided with the vehicle-mounted range radar and work simultaneously, and respective working frequencies are very close to each other, mutual interference is possible, and false alarm is possible to occur. In addition, other objects on the road surface can generate certain noise for the vehicle-mounted radar, and the vehicle-mounted radar can judge whether a target exists according to the fact that whether the signal amplitude exceeds a threshold or not because the total noise output is a random variable, so that a false target can be generated, the false target does not exist in reality, but appears as a real target for the radar probe. If the interference signal falls within the radar receiver bandwidth, a false alarm phenomenon occurs, and in practice the interference information should be detected and suppressed during signal processing. The waveforms, timing, bandwidth, antenna patterns and signal processing aspects employed by each manufacturer are typically slightly different, which is an advantage in terms of interference signal rejection, but can result in radar responses to interference that are different. Therefore, testing different kinds of noise may more fully represent these interference problems.
The invention discloses a method for determining the value of a target signal and an interference signal by using a threshold value determination method, which is characterized in that different kinds of noises have a plurality of characteristic parameters, and the value between the target signal and the interference signal cannot be determined by using a traditional threshold value determination method, so that a classifier of a Support Vector Machine (SVM) can well deal with the problem of irregular data through nonlinear mapping.
Disclosure of Invention
Technical problem to be solved
The invention discloses a signal identification method and a signal identification device for a vehicle-mounted range radar, which aim to at least partially solve the technical problems.
(II) technical scheme
In order to achieve the above object, an embodiment of the present invention provides a signal identification method for a vehicle-mounted range radar, including: acquiring a plurality of sampling signals, wherein each sampling signal is a target echo signal or an interference signal; processing each sampling signal to obtain a plurality of processed signals; performing Fourier transform on each processed signal to obtain amplitude-frequency response information corresponding to each sampling signal; determining a feature vector corresponding to each of the sampled signals according to the amplitude-frequency response information; training a binary classification vector machine by using the feature vector; and identifying the received signal by using the trained binary vector machine to determine that the received signal belongs to the target echo signal or the interference signal.
According to an embodiment of the invention, the interfering signal comprises at least one of a noise amplitude modulated interfering signal and a sinusoidal amplitude modulated interfering signal.
According to an embodiment of the present invention, processing each of the sampled signals to obtain a plurality of processed signals includes: calculating the mean value of the time domain amplitude of each sampling signal; and subtracting the mean value of the time domain amplitude of each sampling signal from the time domain amplitude of each sampling signal to obtain the processed signal.
According to an embodiment of the present invention, the feature vector of each of the sampled signals includes at least one of a mean, a standard deviation, a kurtosis, a difference between a maximum and a minimum, and an average power spectral density of amplitude-frequency response information of each of the sampled signals.
According to an embodiment of the present invention, the kurtosis is calculated as follows:
wherein K is kurtosis, s (f)iInformation representing the amplitude-frequency response of each of said sampled signals, σ represents the standard deviation of the amplitude-frequency response of each of said sampled signals,
the average value of the amplitude-frequency response information of each sampling signal is obtained, n is the number of sampling points of each sampling signal, and i is any one positive integer from 1 to n.
According to an embodiment of the present invention, the difference between the maximum value and the minimum value is calculated as follows: and pk is the difference between the maximum value and the minimum value, max is the maximum value of the amplitude-frequency response information of each sampling signal, and min is the minimum value of the amplitude-frequency response information of each sampling signal.
According to an embodiment of the invention, the average power spectral density is calculated by:
wherein E is an average power spectral density, f is a frequency of each of the sampling signals, s (f) is amplitude-frequency response information of each of the sampling signals, and N is the number of sampling points of each of the sampling signals.
According to the embodiment of the invention, a Gaussian radial basis function is preset in the two-classification vector machine, and the training of the two-classification vector machine by using the feature vector comprises the following steps: obtaining a feature vector sample; normalizing the feature vector sample to obtain a target feature vector sample, wherein the maximum value and the minimum value of each type of feature vector in the target feature vector sample are respectively kept consistent; and inputting the target feature vector sample into a two-classification vector machine, and outputting a classification decision function containing target parameters, wherein the target parameters comprise a penalty parameter C and a kernel function parameter gamma.
According to an embodiment of the present invention, identifying the received signal by using the trained binary vector machine to determine that the received signal belongs to the target echo signal or the interference signal includes: identifying the received signals by using the classification decision function to obtain interface information for distinguishing the target echo signals or the interference signals; and determining the target echo signal or the interference signal according to the interface information.
The invention further provides a signal identification device for the vehicle-mounted range radar, which is used for realizing the method.
(III) advantageous effects
According to the method and the device, the signal identification can be carried out according to different amplitude-frequency response characteristics of the target signal and the interference signal, so that the ranging capability of the vehicle-mounted ranging radar in a complex road environment is improved.
Drawings
Fig. 1 schematically shows a flow chart of a signal identification method for a vehicle-mounted ranging radar according to an embodiment of the present invention;
FIG. 2 is a graph that schematically shows the result of amplitude-frequency response information of a target echo signal, in accordance with an embodiment of the present invention;
FIG. 3 schematically illustrates a resulting graph of amplitude-frequency response information of a noise amplitude modulated jamming signal according to an embodiment of the present invention;
FIG. 4 schematically illustrates a resulting graph of amplitude-frequency response information of a sinusoidal amplitude modulated jamming signal in accordance with an embodiment of the present invention;
fig. 5 schematically shows a flow chart for obtaining feature vectors according to an embodiment of the invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
An embodiment of the present invention provides a signal identification method for a vehicle-mounted range radar, including: acquiring a plurality of sampling signals, wherein each sampling signal is a target echo signal or an interference signal; processing each sampling signal to obtain a plurality of processed signals; fourier transform is carried out on each processed signal to obtain amplitude-frequency response information corresponding to each sampling signal; determining a feature vector corresponding to each sampling signal according to the amplitude-frequency response information; training a two-classification vector machine by using the feature vectors; and identifying the received signal by using a trained two-classification vector machine to determine that the received signal belongs to a target echo signal or an interference signal.
Fig. 1 schematically shows a flowchart of a signal identification method for a vehicle-mounted ranging radar according to an embodiment of the present invention.
As shown in fig. 1, the flow includes operations S101 to S105.
In operation S101, a target echo signal and an interference signal are acquired.
According to an embodiment of the present invention, the target echo signal and the interference signal constitute, for example, the plurality of sampling signals, for example, sampling signals in the form of doppler frequencies. Taking a specific embodiment as an example, the sampling process of each target echo signal or interference signal may include: the method includes the steps of transmitting a sinusoidal signal by a modulation signal generator, forming a signal with a center frequency of 1GHz (for example only, which can be randomly adjusted according to actual application scenarios, the same applies hereinafter) after passing through an oscillator, wherein frequency changes are triangular waves, namely local oscillators, the offset of the local oscillators is 25MHz/V (for example only), generating the local oscillators by the modulation signal generator and the oscillator, generating a return signal by a circulator, mixing the local oscillators and the return signal to generate a difference frequency signal and a sum frequency signal, passing the difference frequency signal through a band-pass filter to obtain a required harmonic frequency, performing distance separation, performing secondary mixing on the harmonic signal, and filtering to obtain a Doppler frequency, namely the target echo signal.
According to an embodiment of the present invention, the interference signal may comprise at least one of a noise amplitude modulated interference signal and a sinusoidal amplitude modulated interference signal, for example. After the target echo signal is acquired in the foregoing manner, for example, the original circulator in the process for acquiring the target echo signal may be replaced by a sinusoidal signal transmitter and a noise transmitter, respectively, where the center frequency is aligned with the center frequency of the target echo signal, the frequency of the interference signal is, for example, 50Hz (by way of example only), and the step length (the time taken for each sweep of the sweep frequency, and after several sweeps, the coverage on all the frequencies to be swept is completed) is 0.001s, for 500 steps. By changing the interference frequency of the interference signal and increasing the distance, the two-way distance of the echo signal is changed into the one-way distance, and the acquisition of the noise amplitude modulation interference signal and the sine amplitude modulation interference signal can be realized.
According to the embodiment of the present invention, for example, 100 target echo signals under the undisturbed action, 100 noise amplitude modulation interference signals and 100 sinusoidal amplitude modulation interference signals may be collected as sampling signals, the sampling frequency is, for example, 100kHz, and the sampling time is, for example, 0.019 s.
In operation S102, a fast fourier transform is performed.
According to an embodiment of the present invention, the operation may further include, for example, processing each of the sampled signals to obtain a plurality of processed signals, and performing fourier transform on each of the processed signals to remove zero-frequency components, i.e., direct-current components, and obtain amplitude-frequency response information corresponding to each of the sampled signals.
Fig. 2 schematically shows a resultant graph of amplitude-frequency response information of a target echo signal according to an embodiment of the present invention.
Fig. 3 schematically shows a resulting graph of amplitude-frequency response information of a noise amplitude modulated jamming signal according to an embodiment of the invention.
Fig. 4 schematically shows a resulting graph of amplitude-frequency response information of a sinusoidal amplitude modulated jamming signal according to an embodiment of the invention.
As shown in fig. 2 to 4, it can be seen that the amplitude-frequency response peak of the target echo signal is relatively prominent, and the amplitude-frequency response peak of the noise signal is relatively small.
According to an embodiment of the present invention, the above-mentioned manner of processing each sampling signal may include, for example: calculating the mean value of the time domain amplitude of each sampling signal; and subtracting the mean value of the time domain amplitude of each sampling signal from the time domain amplitude of each sampling signal to obtain the processed signal.
In operation S103, a feature vector is extracted.
According to an embodiment of the present invention, the operation may include, for example, determining a feature vector corresponding to each of the sampled signals according to the amplitude-frequency response information, where the feature vector of each of the sampled signals may include, for example, at least one of a mean, a standard deviation, a kurtosis, a difference between maximum and minimum values, and an average power spectral density of the amplitude-frequency response information of each of the sampled signals.
Fig. 5 schematically shows a flow chart for obtaining feature vectors according to an embodiment of the invention.
As shown in fig. 5, the process includes calculating a mean value, a standard deviation, a kurtosis, a difference between a maximum value and a minimum value, an average power spectral density of a frequency spectrum of the magnitude-frequency response information obtained after fourier transform, and determining a feature vector.
According to the embodiment of the invention, the mean value, the standard deviation, the kurtosis, the difference between the maximum and the minimum values and the average power spectral density of the amplitude-frequency response of the processed signal can be obtained through a known function.
According to an embodiment of the present disclosure, the kurtosis may also be calculated, for example, by the following formula:
wherein s (f)iInformation representing the amplitude-frequency response of each of said sampled signals, σ represents the standard deviation of the amplitude-frequency response of each of said sampled signals,
and taking the average value of the amplitude-frequency response information of each sampling signal, wherein n is the number of sampling points of each sampling signal, and i is any one positive integer from 1 to n.
According to an embodiment of the present disclosure, the difference between the maximum value and the minimum value may also be calculated, for example, as follows:
pk＝max-min
and the method comprises the following steps of sampling signals, wherein pk is the difference between the maximum value and the minimum value, max is the maximum value of amplitude-frequency response information of each sampling signal, and min is the minimum value of amplitude-frequency response information of each sampling signal.
According to an embodiment of the present disclosure, the average power spectral density may also be calculated, for example, by:
wherein E is an average power spectral density, f is a frequency of each of the sampling signals, s (f) is amplitude-frequency response information of each of the sampling signals, and N is the number of sampling points of each of the sampling signals.
According to an embodiment of the present disclosure, referring to fig. 5, in the process of determining the feature vector, for example, the kurtosis, the difference between the maximum and minimum values, and the average power spectral density obtained through the above calculation may be used as a feature vector, and the feature vector may be expressed in a form of d ═ K, pk, E, for example, and used as an input for supporting a binary classification vector machine.
According to the embodiment of the invention, in order to better know whether the feature vector is effective, Kruskal-Wallis is used to test whether the distribution of a plurality of populations has significant difference analysis, and the original hypothesis H is0The p-value of the returned test result after the non-parametric analysis of the data is carried out on a plurality of independent samples from the same population or a plurality of populations generating the independent samples according to the same distribution Kruskal-Wallis, the p-value is an important parameter of the hypothesis test, the smaller the p-value, the more remarkable the result is, namely H is rejected0The more sufficient the reason for or the more confident we are to refuse to accept H0And accept alternative hypothesis H1The distribution of the various populations varies significantly and the results of the test are considered reliable for p-values less than 0.01.
In operation S104, a binary class vector machine is trained.
According to the embodiment of the invention, since the weight vector of the support vector machine must be mapped from a low dimension to a high dimension, the adoption of the kernel function becomes an indispensable condition, which makes the sample linearly separable, and based on this, the two-classification vector machine is preset with a gaussian radial basis kernel function, for example. Because some sample points may fall between the hyperplane and the boundary, a slack variable needs to be introduced, for each slack variable, a cost needs to be paid, a penalty parameter C is a penalty for the slack variable, the larger the C value is, the smaller the C value is, the misclassification point is as small as possible, so as to make the interval as large as possible, for example, C may be introduced into the two-classification vector machine to make a harmonic coefficient.
According to an embodiment of the present invention, the operation S401 may be represented by, for example, training a binary vector machine using the feature vector, and specifically, in the case of training samples with an interference signal, the operation may include: obtaining a feature vector sample; normalizing the feature vector sample to obtain a target feature vector sample, wherein the maximum value and the minimum value of each type of feature vector in the target feature vector sample are respectively kept consistent; and inputting the target feature vector sample into a two-classification vector machine, and outputting a classification decision function containing target parameters, wherein the target parameters comprise a penalty parameter C and a kernel function parameter gamma.
According to an embodiment of the present invention, before the obtaining the feature vector samples, for example, the method may further include: and establishing an interface between the interference signal and the target echo signal characteristic parameter value by adopting a two-classification SVM (vector machine), wherein the interference signal and the target echo signal are respectively positioned at two sides of the interface. By using a standard C-SVM and taking three parameters (such as the above-mentioned feature vector d) as input of the SVM, the kernel function selects a gaussian radial basis kernel function, and the planning problem can be solved under the condition that the parameters C i0 and γ 0.01, so that four support vectors and corresponding classification decision functions can be obtained, and the interface between the interference signal and the target signal can be obtained.
According to the embodiment of the invention, the target parameters can be determined by using a 10-fold cross-validation method, for example, by randomly dividing the original training set into 10 parts without repetition, selecting one part as a validation set, using the remaining 9 parts as the training set for model training, obtaining a model after training on the training set, testing on the validation set by using the model, and storing the evaluation indexes of the model for 10 times (ensuring that each subset has one opportunity to serve as the validation set).
According to an embodiment of the present invention, in order to further optimize the target parameter, for example, the feature vector may be divided into a training set and a test set by using a 10-fold cross-checking method, specifically, for example, the feature vector sample data after the normalization processing may be divided into 10 samples, where one sample is used as the test set, and the rest samples are used as the training set, and in order to further determine the parameter, for example, the obtained parameter value may be divided into a grid pattern, and each grid uses the 10-fold cross-checking method to calculate the verification classification accuracy so as to obtain the optimal target parameter value. After obtaining the optimal target parameter value, 3/4 samples (i.e., 75 sets of target echo signals and 150 sets of interference signals) are randomly taken out from the target signal samples and the interference signal samples respectively as training samples, and a learning training of the SVM classifier is performed, for example, the above-mentioned classification decision function containing the target parameter is obtained.
According to the embodiment of the present invention, after obtaining the classification decision function, for example, the remaining 1/4 sampling samples (i.e., 25 sets of target echo signals and 50 sets of interference signals) may be used as test samples, and input to train to obtain the SVM classifier, so as to obtain the target detection rate and the interference detection rate of the current experiment. By performing the random grouping test for 200 times, and finally averaging the target detection rate and the interference detection rate obtained each time, the final target detection rate and the final interference detection rate can be obtained, and the average value of the classification accuracy can be used as the performance index of the machine algorithm.
According to the embodiment of the invention, by continuously training the SVM model and performing simulation test, for example, a visual implementation manner of a classification decision function containing target parameters can be finally obtained.
In operation S105, a classification recognition capability is obtained.
According to an embodiment of the present invention, the operation may be represented as: and identifying the received signal by using a trained two-classification vector machine to determine that the received signal belongs to a target echo signal or an interference signal. Specifically, the implementation process may include, for example: identifying the received signals by using a classification decision function to obtain interface information for distinguishing target echo signals or the interference signals; and determining a target echo signal or an interference signal according to the interface information.
Another embodiment of the present invention provides a signal recognition apparatus for a vehicle-mounted ranging radar, which may implement the method as described above.
By the method and the device, signal identification can be performed according to different amplitude-frequency response characteristics of the target signal and the interference signal, so that the ranging capability of the vehicle-mounted ranging radar in a complex road environment is improved.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A signal identification method for a vehicle-mounted range radar comprises the following steps:
acquiring a plurality of sampling signals, wherein each sampling signal is a target echo signal or an interference signal;
processing each sampling signal to obtain a plurality of processed signals;
performing Fourier transform on each processed signal to obtain amplitude-frequency response information corresponding to each sampling signal;
determining a feature vector corresponding to each of the sampled signals according to the amplitude-frequency response information;
training a binary classification vector machine by using the feature vector;
and identifying the received signal by using the trained binary vector machine to determine that the received signal belongs to the target echo signal or the interference signal.
2. The method of claim 1, wherein the interfering signal comprises at least one of a noise amplitude modulated interfering signal and a sinusoidal amplitude modulated interfering signal.
3. The method of claim 1, wherein processing each of the sampled signals to obtain a plurality of processed signals comprises:
calculating the mean value of the time domain amplitude of each sampling signal;
and subtracting the mean value of the time domain amplitude of each sampling signal from the time domain amplitude of each sampling signal to obtain the processed signal.
4. The method of claim 1, wherein the feature vector of each of the sampled signals includes at least one of a mean, a standard deviation, a kurtosis, a difference between maxima and minima, and an average power spectral density of amplitude-frequency response information of each of the sampled signals.
5. The method of claim 4, wherein the kurtosis is calculated by:
wherein K is kurtosis, s (f)iInformation representing the amplitude-frequency response of each of said sampled signals, σ represents the standard deviation of the amplitude-frequency response of each of said sampled signals,
the average value of the amplitude-frequency response information of each sampling signal is obtained, n is the number of sampling points of each sampling signal, and i is any one positive integer from 1 to n.
6. The method of claim 4, wherein the difference between the maximum and minimum values is calculated by:
pk＝max-min，
and the method comprises the following steps of sampling signals, wherein pk is the difference between the maximum value and the minimum value, max is the maximum value of amplitude-frequency response information of each sampling signal, and min is the minimum value of amplitude-frequency response information of each sampling signal.
7. The method of claim 4, wherein the average power spectral density is calculated by:
wherein E is an average power spectral density, f is a frequency of each of the sampling signals, s (f) is amplitude-frequency response information of each of the sampling signals, and N is the number of sampling points of each of the sampling signals.
8. The method of claim 1, wherein the two-class vector machine is pre-configured with gaussian radial basis functions, and training the two-class vector machine with the feature vectors comprises:
obtaining a feature vector sample;
normalizing the feature vector sample to obtain a target feature vector sample, wherein the maximum value and the minimum value of each type of feature vector in the target feature vector sample are respectively kept consistent;
and inputting the target feature vector sample into a two-classification vector machine, and outputting a classification decision function containing target parameters, wherein the target parameters comprise a penalty parameter C and a kernel function parameter gamma.
9. The method of claim 8, wherein identifying the received signal with the trained classification vector machine to determine whether the received signal belongs to the target echo signal or the interference signal comprises:
identifying the received signals by using the classification decision function to obtain interface information for distinguishing the target echo signals or the interference signals;
and determining the target echo signal or the interference signal according to the interface information.
10. A signal recognition apparatus for a vehicle range radar, wherein the apparatus is arranged to implement the method of any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110397704.7A CN113156382B (en) | 2021-04-13 | 2021-04-13 | Signal identification method and device for vehicle-mounted range radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110397704.7A CN113156382B (en) | 2021-04-13 | 2021-04-13 | Signal identification method and device for vehicle-mounted range radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113156382A true CN113156382A (en) | 2021-07-23 |
| CN113156382B CN113156382B (en) | 2023-12-01 |
Family
ID=76890344
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110397704.7A Active CN113156382B (en) | 2021-04-13 | 2021-04-13 | Signal identification method and device for vehicle-mounted range radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113156382B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107817532A (en) * | 2017-10-10 | 2018-03-20 | 成都菲斯洛克电子技术有限公司 | A kind of millimeter wave sensor and Smart Detector |
| CN108256488A (en) * | 2018-01-19 | 2018-07-06 | 中国人民解放军陆军装甲兵学院 | A kind of radar target identification method based on micro-Doppler feature extraction and deep learning |
| CN108267724A (en) * | 2018-01-19 | 2018-07-10 | 中国人民解放军陆军装甲兵学院 | A kind of unknown object recognition methods of radar target recognition |
| CN109164429A (en) * | 2018-10-17 | 2019-01-08 | 赣南师范大学 | Multi-fractal features Aircraft Targets classification method based on principal component analysis |
| CN110109080A (en) * | 2019-05-29 | 2019-08-09 | 南京信息工程大学 | Method for detecting weak signals based on IA-SVM model |
| CN110443138A (en) * | 2019-07-05 | 2019-11-12 | 东南大学 | Combine SVM and CNN Multi-Target Classification Method based on vehicle-mounted millimeter wave radar |
| CN111160176A (en) * | 2019-12-19 | 2020-05-15 | 南京理工大学 | Fusion feature-based ground radar target classification method for one-dimensional convolutional neural network |
| CN111175718A (en) * | 2019-12-18 | 2020-05-19 | 南京理工大学 | Time-frequency domain combined ground radar automatic target identification method and system |
-
2021
- 2021-04-13 CN CN202110397704.7A patent/CN113156382B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107817532A (en) * | 2017-10-10 | 2018-03-20 | 成都菲斯洛克电子技术有限公司 | A kind of millimeter wave sensor and Smart Detector |
| CN108256488A (en) * | 2018-01-19 | 2018-07-06 | 中国人民解放军陆军装甲兵学院 | A kind of radar target identification method based on micro-Doppler feature extraction and deep learning |
| CN108267724A (en) * | 2018-01-19 | 2018-07-10 | 中国人民解放军陆军装甲兵学院 | A kind of unknown object recognition methods of radar target recognition |
| CN109164429A (en) * | 2018-10-17 | 2019-01-08 | 赣南师范大学 | Multi-fractal features Aircraft Targets classification method based on principal component analysis |
| CN110109080A (en) * | 2019-05-29 | 2019-08-09 | 南京信息工程大学 | Method for detecting weak signals based on IA-SVM model |
| CN110443138A (en) * | 2019-07-05 | 2019-11-12 | 东南大学 | Combine SVM and CNN Multi-Target Classification Method based on vehicle-mounted millimeter wave radar |
| CN111175718A (en) * | 2019-12-18 | 2020-05-19 | 南京理工大学 | Time-frequency domain combined ground radar automatic target identification method and system |
| CN111160176A (en) * | 2019-12-19 | 2020-05-15 | 南京理工大学 | Fusion feature-based ground radar target classification method for one-dimensional convolutional neural network |
Non-Patent Citations (1)
| Title |
|---|
| 罗彬珅 等: "噪声调制类有源干扰信号识别技术研究", 雷达科学与技术, vol. 17, no. 6, pages 597 - 602 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113156382B (en) | 2023-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107301432B (en) | Self-adaptive radiation source modulation identification method based on time-frequency analysis | |
| CN105137498B (en) | A kind of the underground objects detection identifying system and method for feature based fusion | |
| CN106778610B (en) | Intra-pulse modulation identification method based on time-frequency image characteristics | |
| CN102510363A (en) | LFM (linear frequency modulation) signal detecting method under strong interference source environment | |
| CN106330385A (en) | Interference type identification method | |
| CN112560803A (en) | Radar signal modulation identification method based on time-frequency analysis and machine learning | |
| CN108469560A (en) | A kind of objective complexity evaluation methodology of electromagnetic interference based on quick S-transformation time frequency space model | |
| Chen et al. | Interrupted-sampling repeater jamming suppression based on stacked bidirectional gated recurrent unit network and infinite training | |
| CN108919203B (en) | Radar active interference identification method and system | |
| CN112859014A (en) | Radar interference suppression method, device and medium based on radar signal sorting | |
| Hayvaci et al. | Classification of electronic devices with power-swept signals using harmonic radar | |
| Deng et al. | A novel specific emitter identification method based on radio frequency fingerprints | |
| Akyon et al. | Classification of intra-pulse modulation of radar signals by feature fusion based convolutional neural networks | |
| KR101426863B1 (en) | A method for recognizing radar intra-pulse modulation type using features | |
| CN112083393A (en) | Intermittent sampling forwarding interference identification method based on spectrogram average time characteristic | |
| Perrin et al. | Multisensor fusion in the frame of evidence theory for landmines detection | |
| KR101362451B1 (en) | Method and device for determining a kind of land mind based on various characteristics | |
| CN111401185B (en) | Electromagnetic signal and equipment association analysis method and system based on radio frequency fingerprint SEV | |
| CN113156382A (en) | Signal identification method and device for vehicle-mounted range radar | |
| CN116359851A (en) | Radar active interference detection and identification method and device based on converged network | |
| CN115755020A (en) | Tracking method for scanning position of mechanical scanning radar antenna | |
| Bhattacharyya et al. | Automatic target recognition (atr) system using recurrent neural network (rnn) for pulse radar | |
| CN115345216A (en) | FMCW radar interference elimination method fusing prior information | |
| CN115061094A (en) | Radar target identification method based on neural network and SVM | |
| Shahi et al. | Distinguishing electronic devices using harmonic radar based on a linear model |
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 |