CN116299196A - Electromagnetic interference identification method, device, equipment and readable storage medium - Google Patents
Electromagnetic interference identification method, device, equipment and readable storage medium Download PDFInfo
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/021—Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G—PHYSICS
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- 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
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- 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
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Abstract
The application discloses an electromagnetic interference identification method, an electromagnetic interference identification device, electromagnetic interference identification equipment and a readable storage medium, and relates to the technical field of signal processing, wherein the electromagnetic interference identification method comprises the following steps: acquiring basic data of a radar signal to be identified; calculating characteristic parameters of the radar signals to be identified according to the basic data; and determining the recognition result of the radar signal to be recognized according to the characteristic parameters and the electromagnetic interference recognition model. Therefore, the method and the device can calculate the characteristic parameters of the radar signal to be identified based on the basic data of the radar signal to be identified, input the characteristic parameters into the pre-trained electromagnetic interference identification model, accurately identify various electromagnetic interferences, avoid missed judgment of the electromagnetic interferences and misjudgment of useful signals, and greatly improve the identification accuracy of whether the radar signal is subjected to the electromagnetic interferences.
Description
Technical Field
The present invention relates to the field of signal processing technologies, and in particular, to an electromagnetic interference identification method, an apparatus, a device, and a readable storage medium.
Background
The weather radar is an important facility for realizing weather monitoring and early warning of disaster weather. However, the weather radar is often subjected to electromagnetic interference in operation, and the normal operation of the weather radar is seriously affected. Electromagnetic interference refers to interference caused by interference signals entering a radar receiver through a radar antenna, wherein the carrier of the interference signals is the same as or similar to the carrier of radar transmitting frequency. In order to improve the quality of radar signals received by weather radar, it is necessary to detect whether the radar signals are subject to electromagnetic interference.
The existing solution is to detect whether the radar signal is subject to electromagnetic interference by means of a radial noise algorithm and a SQI (Speech Quality Index) algorithm. However, the radial noise algorithm can only identify the spoke-shaped electromagnetic interference, cannot identify the electromagnetic interference in the face of spiral electromagnetic interference, and has low identification accuracy; although the SQI algorithm has a certain recognition effect on both spoke-shaped electromagnetic interference and spiral electromagnetic interference, the SQI algorithm is easy to cause misjudgment of electromagnetic interference and misjudgment of useful signals, and the recognition accuracy is low.
Disclosure of Invention
The embodiment of the application provides an electromagnetic interference identification method, an electromagnetic interference identification device and a readable storage medium, which can realize accurate identification of various electromagnetic interference, avoid missed judgment of the electromagnetic interference and misjudgment of useful signals, and improve the identification accuracy of whether radar signals suffer from the electromagnetic interference.
In view of this, an embodiment of the present application provides an electromagnetic interference identification method, including:
acquiring basic data of a radar signal to be identified;
calculating characteristic parameters of the radar signals to be identified according to the basic data;
and determining the recognition result of the radar signal to be recognized according to the characteristic parameters and the electromagnetic interference recognition model.
Optionally, the base data of the radar signal to be identified comprises in-phase quadrature data of the radar signal to be identified in a plurality of range bins; calculating the characteristic parameters of the radar signal to be identified according to the basic data, wherein the characteristic parameters comprise:
and calculating the maximum power change coefficient, the skewness coefficient, the kurtosis coefficient and the maximum sharpness of the radar signal to be identified according to the in-phase and quadrature data.
Optionally, determining the recognition result of the radar signal to be recognized according to the characteristic parameter and the electromagnetic interference recognition model includes:
and inputting the characteristic parameters into the electromagnetic interference recognition model to respectively obtain recognition results of the radar signals to be recognized in a plurality of distance libraries.
Optionally, the method further comprises:
determining a pollution area of the radar signal to be identified according to identification results of the radar signal to be identified in a plurality of distance libraries;
recording the polluted area and sending an alarm prompt to technicians.
Optionally, the training process of the electromagnetic interference recognition model includes:
acquiring a training data set and a verification data set, wherein the training data set comprises to-be-trained characteristic parameters and to-be-trained recognition results of the to-be-recognized radar signals, and the verification data set comprises to-be-verified characteristic vectors and to-be-verified recognition results of the to-be-recognized radar signals;
creating a neural network model;
inputting the training data set into the neural network model for training to obtain an initial electromagnetic interference recognition model;
and adjusting the performance parameters of the initial electromagnetic interference recognition model according to the verification data set to obtain the electromagnetic interference recognition model.
The embodiment of the application also provides an electromagnetic interference identification device, which comprises:
the acquisition unit is used for acquiring basic data of the radar signal to be identified;
the computing unit is used for computing the characteristic parameters of the radar signals to be identified according to the basic data;
and the determining unit is used for determining the recognition result of the radar signal to be recognized according to the characteristic parameter and the electromagnetic interference recognition model.
Optionally, the base data of the radar signal to be identified comprises in-phase quadrature data of the radar signal to be identified in a plurality of range bins;
the computing unit is specifically configured to compute a maximum power variation coefficient, a bias coefficient, a kurtosis coefficient and a maximum sharpness of the radar signal to be identified according to the in-phase and quadrature data.
Optionally, the determining unit is specifically configured to input the characteristic parameter into the electromagnetic interference recognition model, and obtain recognition results of the radar signal to be recognized in a plurality of range bins respectively.
The embodiment of the application also provides a computer device, which comprises: a memory, a processor, and a bus system;
wherein the memory is used for storing programs;
the processor is used for executing the program in the memory to realize any one of the electromagnetic interference identification methods;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
Embodiments of the present application also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any one of the electromagnetic interference identification methods described above.
The embodiment of the application provides an electromagnetic interference identification method, which comprises the following steps: acquiring basic data of a radar signal to be identified; calculating characteristic parameters of the radar signals to be identified according to the basic data; and determining the recognition result of the radar signal to be recognized according to the characteristic parameters and the electromagnetic interference recognition model. Therefore, the method and the device can calculate the characteristic parameters of the radar signal to be identified based on the basic data of the radar signal to be identified, input the characteristic parameters into the pre-trained electromagnetic interference identification model, accurately identify various electromagnetic interferences, avoid missed judgment of the electromagnetic interferences and misjudgment of useful signals, and greatly improve the identification accuracy of whether the radar signal is subjected to the electromagnetic interferences.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic flow chart of an electromagnetic interference identification method provided in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a neural network model according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electromagnetic interference recognition device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The weather radar is an important facility for realizing weather monitoring and early warning of disaster weather. However, the weather radar is often subjected to electromagnetic interference in operation, and the normal operation of the weather radar is seriously affected. Electromagnetic interference refers to interference caused by interference signals entering a radar receiver through a radar antenna, wherein the carrier of the interference signals is the same as or similar to the carrier of radar transmitting frequency. Electromagnetic interference is classified into synchronous interference of the same frequency, which is generally concentric circles on a radar image, and asynchronous interference of the same frequency, which is generally spiral on a radar image, wherein the asynchronous interference of the same frequency, which is generated by an interference source in which a transmitting antenna does not rotate, appears as a spoke on the radar image. Electromagnetic interference existing in weather radars in China is mainly synchronous and asynchronous interference, namely, the electromagnetic interference is expressed as spoke-shaped, spiral line and pitting, and adverse effects are brought to data quality and application. For this reason, in order to improve the quality of the radar signal received by the weather radar, it is necessary to detect whether the radar signal is subjected to electromagnetic interference.
The current electromagnetic interference recognition algorithm is based on In-phase/Quadrature (IQ) data. The I/Q data is a complex signal with radar echoes phase shifted 90 degrees apart. The base data is the reflectance, speed, spectral width, etc. parameters calculated based on the I/Q data.
The main SQI algorithm and radial noise algorithm based on I/Q data. The SQI algorithm uses a characteristic that the SQI is less than 0.5 in the presence of electromagnetic interference to identify the electromagnetic interference by a threshold value, and determines that the electromagnetic interference is present when the SQI of the target unit is less than the threshold value. However, because the SQI values of electromagnetic interference of different stations and different ranges vary between 0 and 0.5, a threshold is selected in the whole detection range to perform discrimination, and this method easily causes missed discrimination of electromagnetic interference and misdiscrimination of useful signals. In addition, the SQI of strong convection weather is less than 0.5, and if the SQI is taken as a criterion, weather signals can be damaged.
The radial noise algorithm utilizes the characteristic that noise is obviously increased when radial interference exists, and judges whether electromagnetic interference exists or not by estimating the noise of each radial direction in real time. However, this method is more effective in identifying radial disturbances, but ineffective in spiral or pitting disturbances. The electromagnetic interference identification method in the prior art has lower identification accuracy.
Therefore, in view of the above problems, the embodiments of the present application provide an electromagnetic interference identification method, an apparatus, a device, and a readable storage medium, which can implement accurate identification of various electromagnetic interference, and can avoid missed judgment of electromagnetic interference and misjudgment of useful signals, and improve identification accuracy of whether a radar signal suffers from electromagnetic interference.
Referring to fig. 1, an electromagnetic interference method provided in an embodiment of the present application includes the following steps.
S101, acquiring basic data of a radar signal to be identified.
In this embodiment, the basic data of the radar signal to be identified may be first obtained, where the basic data may be characteristic data that may represent characteristics or attributes of the radar signal to be identified, and may include in-phase and quadrature data of the radar signal to be identified in a plurality of range bins. The distance libraries are small distance units which are divided into distances along the radial direction in the radar echo signal processing, namely the radar signals to be identified can be divided into a plurality of distance libraries according to the distances, and the homodromous orthogonal data, namely I/Q data, of the radar signals to be identified in each distance library are obtained. It will be appreciated that the radar signal to be identified may be a plurality of radar signals, i.e. a plurality of radar signals may be identified simultaneously.
S102, calculating characteristic parameters of the radar signals to be identified according to the basic data.
In this embodiment, after the basic data of the radar signal to be identified is obtained, the characteristic parameter of the radar signal to be identified may be calculated according to the basic data, so as to determine the identification result of the radar signal to be identified through the electromagnetic interference identification model.
In one possible implementation, the maximum power change coefficient, the skewness coefficient, the kurtosis coefficient and the maximum sharpness of the radar signal to be identified may be calculated according to the I/Q data of the radar signal to be identified in a plurality of range bins.
Specifically, if the number of radar signals to be identified is M and the total number of range bins of the radar signals to be identified is N, the I/Q data of the ith radar signal in the kth range bin may be expressed as:
x(k) i ，i＝1,2…M，k＝1,2…N；
wherein x (k) i I/Q data representing an ith radar signal in a kth range bin.
Firstly, calculating power data of a radar signal to be identified, wherein a calculation formula is as follows:
X(k) i ＝x(k) i *conj(x(k) i )；
wherein X (k) i Power data representing the ith radar signal in the kth range bin, conj () is a conjugate operation.
The calculation formula of the maximum power change coefficient Maxv is as follows:
MaxV(k)＝Max(X)-Min(X)；
wherein MaxV (k) is the maximum power change coefficient of the kth distance library, and X is X (k) i Is a shorthand for the ith radar signal in the kth range bin.
The calculation formula of the bias coefficient Sk is as follows:
skk) is the bias coefficient of the kth distance library, E is the expected value of the I/Q data in all the distance libraries, sigma is the standard deviation of the sample, and mu is the average value.
The calculation formula of Ku is:
where Ku (k) is the kurtosis coefficient of the kth range bin.
An operator Lap (n) can be defined, typically n=3 as follows:
the calculation formula of the maximum sharpness based on the operator Lap (n) is:
Macu(k)＝max(conv(X k ,Lap))；
wherein Macu (k) is the maximum sharpness of the kth distance library, X k ＝10*log 10 (abs(x(i) k ) I=0, 1..m-1, conv represents convolution and abs represents absolute value.
S103, determining the recognition result of the radar signal to be recognized according to the characteristic parameters and the electromagnetic interference recognition model.
In this embodiment, after the characteristic parameter of the radar signal to be identified is calculated, the characteristic parameter may be input into the electromagnetic interference identification model, and the electromagnetic interference identification model outputs the identification result of the radar signal to be identified. It can be understood that, since the training data set for training the electromagnetic interference recognition model includes the characteristic parameters of the radar signal and the corresponding recognition results, inputting the characteristic parameters of the radar signal to be recognized into the trained electromagnetic interference recognition model can obtain accurate recognition results.
Specifically, the training process of the electromagnetic interference recognition model comprises the steps of obtaining a training data set and a verification data set, wherein the training data set comprises characteristic parameters to be trained and recognition results to be trained of radar signals to be recognized, and the verification data set comprises characteristic vectors to be verified and recognition results to be verified of the radar signals to be recognized; creating a neural network model; inputting the training data set into a neural network model to obtain an initial electromagnetic interference recognition model; and adjusting the performance parameters of the initial electromagnetic interference recognition model according to the verification data set to obtain the electromagnetic interference recognition model.
It can be understood that the data of a plurality of radar signals subjected to electromagnetic interference and the data of a plurality of radar signals not subjected to electromagnetic interference can be selected from the historical database, the characteristic parameters are calculated respectively, the characteristic parameters and the corresponding recognition results are used as training sample data, and the training samples are randomly divided into a training set and a verification set according to the proportion of M to 1.
Then, a neural network model is created, the number of preset hidden layers, the number of neurons of each hidden layer, the activation function of each layer and the number of output parameters in the neural network model are set, a loss function and an optimizer are selected, and the created neural network model can be shown as a figure 2.
The hidden layer of the neural network model can be N layers, the front N-1 layer is built by adopting a full-connection layer mode, the adopted excitation function is tanh, and the calculation formula is as follows:
in order to prevent the training process from being fitted, a Dropout algorithm is adopted, so that part of neurons are not activated in the training process; the output layer does not comprise an excitation function, and the electromagnetic interference and the non-electromagnetic interference are needed to be distinguished, so that the number of the output parameters is 2, and if more targets need to be identified, the number of the output parameters is changed according to the needs. The output result is transformed into probabilities using softmax for the calculation of the loss function.
The loss function may employ a cross entropy, the formula of which is:
where y is the true probability distribution,
for predicting probability distributions, the cross entropy describes the distance between two probability distributions, the smaller the cross entropy value, the better the predicted result.
The optimizer chooses Adam (Adaptive Moment Estimation), adam dynamically adjusts the learning rate α of each parameter (W, b) using a first moment estimate (gradient mean) and a second moment (gradient non-centered variance) estimate of the loss function gradient.
The parameters W and b are updated by adopting a back propagation algorithm, the back propagation algorithm is mainly characterized in that signals are transmitted forward, errors are propagated backward, the final output of the network is as close as possible to the expected output by continuously adjusting weight values, so that the aim of training is achieved, and the parameter updating formula is as follows:
where alpha is the learning rate, its range of values is (0, 1), J (W, b) is the loss function,
represents the connection weight from the jth neuron of the L-1 layer to the ith neuron of the L layer,/L>
Is the bias of the ith neuron of the L-th layer.
The initial learning efficiency is set as a constant, the initial weight is set as an all-zero matrix, the initial bias is set as an all-zero vector, and the preset iteration number is N.
After the neural network model is created, a training data set can be input into the neural network model, and a training method of batch gradient descent is adopted to train the neural network to obtain a trained weight W and a bias b, so as to obtain an initial electromagnetic interference model; and then inputting the training set and the verification set data into the adjusted optimal model for training, finding out the optimal function which minimizes the loss function, obtaining the final model parameters, and finally obtaining the trained electromagnetic interference recognition model.
In one possible implementation, the characteristic parameters may be input into an electromagnetic interference recognition model, so as to obtain recognition results of the radar signal to be recognized in a plurality of range bins, respectively. It can be understood that after the characteristic parameters of the radar signal to be identified are input into the electromagnetic interference identification model, the electromagnetic interference identification model can respectively output the identification result of the radar signal to be identified in each range bin, i.e. whether the radar signal to be identified is subjected to electromagnetic interference in each range bin can be respectively determined.
In one possible implementation manner, the pollution area of the radar signal to be identified can be determined according to the identification results of the radar signal to be identified in a plurality of range bins; recording the polluted area and giving an alarm prompt to a technician. It can be understood that after the identification results of the radar signal to be identified in the multiple distance libraries are determined, the pollution area where the radar signal to be identified receives electromagnetic interference can be further determined, namely, the electromagnetic interference exists in the multiple distance libraries respectively, the pollution area is marked and recorded, and an alarm prompt is sent to a technician, so that the technician can further process the part of the radar signal to be identified, which is subjected to electromagnetic interference.
In summary, the embodiment of the application provides an electromagnetic interference identification method, which can accurately identify various electromagnetic interferences by calculating characteristic parameters of a radar signal to be identified based on basic data of the radar signal to be identified and inputting the characteristic parameters into a pre-trained electromagnetic interference identification model, and can avoid missed judgment of the electromagnetic interferences and misjudgment of useful signals, thereby greatly improving the identification accuracy of whether the radar signal suffers from the electromagnetic interferences.
Referring to fig. 3, an electromagnetic interference recognition device is further provided in the embodiment of the present application, including:
an acquiring unit 301, configured to acquire basic data of a radar signal to be identified;
a calculating unit 302, configured to calculate a characteristic parameter of the radar signal to be identified according to the basic data;
and the determining unit 303 is configured to determine a recognition result of the radar signal to be recognized according to the characteristic parameter and the electromagnetic interference recognition model.
Optionally, the base data of the radar signal to be identified comprises in-phase quadrature data of the radar signal to be identified in a plurality of range bins;
the calculating unit 302 is specifically configured to calculate a maximum power variation coefficient, a bias coefficient, a kurtosis coefficient and a maximum sharpness of the radar signal to be identified according to the in-phase and quadrature data.
Optionally, the determining unit 303 is specifically configured to input the characteristic parameter into the electromagnetic interference recognition model, so as to obtain recognition results of the radar signal to be recognized in a plurality of range bins respectively.
Optionally, the determining unit 303 is further configured to determine a contaminated area of the radar signal to be identified according to recognition results of the radar signal to be identified in a plurality of range bins;
and the recording unit is used for recording the polluted area and sending an alarm prompt to technicians.
Optionally, the training process of the electromagnetic interference recognition model includes:
acquiring a training data set and a verification data set, wherein the training data set comprises to-be-trained characteristic parameters and to-be-trained recognition results of the to-be-recognized radar signals, and the verification data set comprises to-be-verified characteristic vectors and to-be-verified recognition results of the to-be-recognized radar signals;
creating a neural network model;
inputting the training data set into the neural network model for training to obtain an initial electromagnetic interference recognition model;
and adjusting the performance parameters of the initial electromagnetic interference recognition model according to the verification data set to obtain the electromagnetic interference recognition model.
In summary, the embodiment of the application provides an electromagnetic interference recognition device, which can calculate the characteristic parameters of a radar signal to be recognized based on the basic data of the radar signal to be recognized, input the characteristic parameters into a pre-trained electromagnetic interference recognition model, can realize accurate recognition of various electromagnetic interferences, can avoid missed judgment of the electromagnetic interferences and misjudgment of useful signals, and greatly improves the recognition accuracy of whether the radar signal suffers from the electromagnetic interference.
The embodiment of the application also provides a computer device, which comprises: a memory, a processor, and a bus system;
wherein the memory is used for storing programs;
the processor is configured to execute the program in the memory to implement an electromagnetic interference identification method as described above;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
Embodiments of the present application also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any one of the electromagnetic interference identification methods described above.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of electromagnetic interference identification, the method comprising:
acquiring basic data of a radar signal to be identified;
calculating characteristic parameters of the radar signals to be identified according to the basic data;
and determining the recognition result of the radar signal to be recognized according to the characteristic parameters and the electromagnetic interference recognition model.
2. The method according to claim 1, wherein the base data of the radar signal to be identified comprises in-phase quadrature data of the radar signal to be identified in a plurality of range bins; calculating the characteristic parameters of the radar signal to be identified according to the basic data, wherein the characteristic parameters comprise:
and calculating the maximum power change coefficient, the skewness coefficient, the kurtosis coefficient and the maximum sharpness of the radar signal to be identified according to the in-phase and quadrature data.
3. The method according to claim 1, wherein determining the recognition result of the radar signal to be recognized according to the characteristic parameter and an electromagnetic interference recognition model comprises:
and inputting the characteristic parameters into the electromagnetic interference recognition model to respectively obtain recognition results of the radar signals to be recognized in a plurality of distance libraries.
4. A method according to claim 3, characterized in that the method further comprises:
determining a pollution area of the radar signal to be identified according to identification results of the radar signal to be identified in a plurality of distance libraries;
recording the polluted area and sending an alarm prompt to technicians.
5. The method of claim 1, wherein the training process of the electromagnetic interference identification model comprises:
acquiring a training data set and a verification data set, wherein the training data set comprises to-be-trained characteristic parameters and to-be-trained recognition results of the to-be-recognized radar signals, and the verification data set comprises to-be-verified characteristic vectors and to-be-verified recognition results of the to-be-recognized radar signals;
creating a neural network model;
inputting the training data set into the neural network model for training to obtain an initial electromagnetic interference recognition model;
and adjusting the performance parameters of the initial electromagnetic interference recognition model according to the verification data set to obtain the electromagnetic interference recognition model.
6. An electromagnetic interference identification apparatus, the apparatus comprising:
the acquisition unit is used for acquiring basic data of the radar signal to be identified;
the computing unit is used for computing the characteristic parameters of the radar signals to be identified according to the basic data;
and the determining unit is used for determining the recognition result of the radar signal to be recognized according to the characteristic parameter and the electromagnetic interference recognition model.
7. The apparatus of claim 6, wherein the base data of the radar signal to be identified comprises in-phase-quadrature data of the radar signal to be identified in a plurality of range bins;
the computing unit is specifically configured to compute a maximum power variation coefficient, a bias coefficient, a kurtosis coefficient and a maximum sharpness of the radar signal to be identified according to the in-phase and quadrature data.
8. The device according to claim 6, wherein the determining unit is specifically configured to input the characteristic parameter into the electromagnetic interference recognition model to obtain recognition results of the radar signal to be recognized in a plurality of range bins, respectively.
9. A computer device, comprising: a memory, a processor, and a bus system;
wherein the memory is used for storing programs;
the processor being adapted to execute a program in the memory to implement the method of any one of claims 1 to 5;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
10. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 5.
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| CN119199745A (en) * | 2024-11-20 | 2024-12-27 | 中国气象局武汉暴雨研究所 | A method for identifying electromagnetic interference echoes of weather radar based on target detection |
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2023
- 2023-04-24 CN CN202310446527.6A patent/CN116299196A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119199745A (en) * | 2024-11-20 | 2024-12-27 | 中国气象局武汉暴雨研究所 | A method for identifying electromagnetic interference echoes of weather radar based on target detection |
| CN119199745B (en) * | 2024-11-20 | 2025-03-07 | 中国气象局武汉暴雨研究所 | Weather radar electromagnetic interference echo identification method based on target detection |
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