CN115508793A - Radar interference effect online evaluation intelligent method based on countermeasure analysis feature screening - Google Patents
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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
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Abstract
The invention discloses an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening, which comprises the following steps: determining an interference effect evaluation index according to the transmission interference type, the working mode conversion of the target radar after interference and the pre-estimated anti-interference measure; acquiring index data of the target radar before and after interference through real-time reconnaissance; inputting the index data into an evaluation model to obtain an evaluation result of the transmitted interference type; wherein, the evaluation model is as follows: and (3) screening interference effect evaluation indexes based on a radar sample data set and a grey correlation analysis method, and then training to obtain a GA-BP neural network model. The method integrates the angles of an interference party and a radar party to resist the two parties, considers and analyzes the correlation between data by combining with an actual interference scene, screens redundancy evaluation indexes by a grey correlation analysis method, introduces a GA-BP neural network model to realize online evaluation and intelligent evaluation of the interference effect, avoids manual intervention and improves the evaluation accuracy.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening.
Background
The radar interference effect evaluation is to qualitatively or quantitatively analyze the result of radar interference action and the influence caused by the radar interference action under specified conditions. In the process of the radar countermeasure game, the interference effect becomes an important index, and the battlefield situation can be judged more effectively and the countermeasure can be decided through the analysis of the interference effect result. With the development of radar countermeasure towards intellectualization and actual combat, the traditional off-line evaluation thought that working parameters and performance indexes can be obtained by an interfering party and used for evaluation before and after the target radar is interfered based on the cooperative relationship between the two aspects of the countermeasure is not applicable, and the evaluation of the interference effect tends to be on-line, namely, the interference effect is evaluated only by using information received by a radar reconnaissance system of an interference implementing party.
In the related art, when the interference effect evaluation is performed by a radar interference effect evaluation method such as an analytic hierarchy process, a fuzzy comprehensive evaluation method and the like, evaluation index weight needs to be determined by comprehensive expert evaluation opinions, influence of human factors is introduced, and the evaluation result lacks objectivity. Moreover, the existing evaluation index system does not consider increasingly complex interference pattern action mechanisms and differences brought by different interference patterns to evaluation results, and only establishes an index system from a single interference party or a radar party; in addition, the existing evaluation method does not carry out feature screening, so that when the interference environments are different, only part of excessive indexes can be used for evaluation, and the evaluation result is inaccurate.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening, which comprises the following steps:
determining an interference effect evaluation index according to the type of the transmission interference, the working mode conversion of the target radar after the interference and the pre-estimated anti-interference measure of the target radar;
acquiring index data of the target radar before and after interference through real-time reconnaissance;
inputting the index data into an evaluation model to obtain an evaluation result of the transmitted interference type; wherein the evaluation model is: and screening the interference effect evaluation indexes based on a radar sample data set and a grey correlation analysis method, and training to obtain the GA-BP neural network model.
In one embodiment of the invention, the types of the transmission interference include: at least one of noise amplitude modulation interference, noise frequency modulation interference, distance spoofing interference, speed spoofing interference, dense replication decoy interference, and intermittent sampling interference.
In one embodiment of the present invention, the interference effect evaluation index includes: at least one of pulse width, pulse repetition frequency, beam offset, pulse amplitude, carrier frequency, bandwidth, and beam dwell time.
In one embodiment of the invention, the radar sample data set comprises a plurality of sample data respectively corresponding to a plurality of interference types;
based on a radar sample data set and a grey correlation analysis method, screening the interference effect evaluation index according to the following steps:
taking sample data corresponding to each interference effect evaluation index of the same interference type as a sample data sequence, and dividing the sample data in the sample data sequence by first sample data in the sequence to obtain a first sequence;
after a reference index of each interference type is determined from a plurality of interference effect evaluation indexes of each interference type, respectively obtaining absolute values of differences between the first sequences corresponding to other interference effect evaluation indexes of the interference type and the ith sample data in the first sequences corresponding to the reference indexes, and determining the maximum value and the minimum value of all the absolute values of the differences between the first sequences corresponding to other interference effect evaluation indexes and the first sequences corresponding to the reference indexes according to the absolute values of the differences;
calculating the correlation coefficient and the grey correlation degree between the other interference effect evaluation indexes of the interference type and the reference index according to the maximum value and the minimum value;
and screening the interference effect evaluation indexes according to the grey correlation degree.
In an embodiment of the present invention, the step of calculating a correlation coefficient between the interference type other interference effect evaluation indicator and the reference indicator according to the maximum value and the minimum value includes:
calculating a correlation coefficient gamma between the ith sample data in the first sequence corresponding to the other interference effect evaluation indexes of the interference type and the first sequence corresponding to the reference index according to the following formula 1j (i)：
Wherein Max and Min respectively represent the maximum value and the minimum value, and Δ j-1 (i) Absolute value j of difference between ith sample data in first sequence corresponding to other interference effect evaluation indexes representing interference types and first sequence corresponding to reference index>1, zeta is a preset resolution coefficient, and zeta belongs to (0,1);
and respectively calculating the average value of the correlation coefficients between the first sequence corresponding to other interference effect evaluation indexes of the interference type and all sample data in the first sequence corresponding to the reference index, and taking the average value as the correlation coefficient between the other interference effect evaluation indexes of the interference type and the reference index.
In one embodiment of the invention, the grey correlation degree between the interference type and other interference effect evaluation indexes and the reference index is calculated according to the following formula:
wherein N represents the number of sample data in the sample data sequence, gamma 1j Indicating the grey correlation degree between the interference effect evaluation index j of the interference type and the reference index, j ≠ 1.
In an embodiment of the present invention, the step of screening the interference effect evaluation index according to the gray correlation includes:
and when the grey correlation degree between the other interference effect evaluation indexes of the interference type and the reference index is larger than or equal to a preset threshold value, deleting the other evaluation indexes and keeping the reference index.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening, which is characterized in that an interference effect evaluation index is selected according to the interference type transmitted by one party, so that the problem that analysis and evaluation can be carried out only by knowing parameter information of two parties of countermeasure is solved; the method integrates the angles of an interference party and a radar party to resist the two parties, considers and analyzes the correlation between data by combining with an actual interference scene, screens redundancy evaluation indexes by a grey correlation analysis method, introduces a GA-BP neural network model to realize online evaluation and intelligent evaluation of the interference effect, avoids manual intervention and improves the evaluation accuracy.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a flowchart of an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating simulation results according to an embodiment of the present invention;
fig. 3 is a schematic diagram of another simulation result provided in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Fig. 1 is a flowchart of an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening according to an embodiment of the present invention. As shown in fig. 1, the present invention provides an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening, which includes:
s1, determining an interference effect evaluation index according to the type of transmission interference, the working mode conversion of a target radar after interference and the pre-estimated anti-interference measure of the target radar;
s2, acquiring index data of the target radar before and after interference through real-time reconnaissance;
s3, inputting the index data into an evaluation model to obtain an evaluation result of the transmitted interference type; wherein, the evaluation model is as follows: and (3) screening the interference effect evaluation indexes based on a radar sample data set and a grey correlation analysis method, and then training to obtain the GA-BP neural network model.
It should be understood that after some type of interference is transmitted to the target radar, the target radar may take anti-interference measures according to the change of the interfered working state, and when the target radar is changed from the tracking mode to the searching mode, it indicates that the interference effect is better.
Specifically, the change of the working state of the target radar is generally shown in the parameters of the transmitting end of the target radar, for example, when the target radar is in a searching state, the pulse width is large, the acting distance is long, the pulse repetition frequency is low, a plurality of interference types are deceptive interference, and the wave beam of the target radar can be deviated; when the target radar is in a tracking state, the target can be stably tracked, the pulse width is small, target parameters need to be updated in real time, the pulse repetition frequency is high, and the beam points to a real target in the state.
Further, after the target radar is interfered, the influence caused by the interference can be reduced through certain measures, and the parameters of the target radar transmitting end change at this time include: (1) When the target radar is suppressed by the power of the pressing type interference, the target radar controls the radiation power through a radio frequency radiation management technology, and increases the power of a transmitting signal to carry out interference suppression; (2) Carrier frequency, the target radar adopts anti-interference measures such as frequency agility, frequency diversity and the like to ensure that the working frequency of the target radar is not covered by interference signals; (3) The target radar realizes interference suppression by changing signal bandwidth through a waveform design anti-interference means, and increases the bandwidth to improve the radar distance resolution aiming at distance deception interference; (4) And the beam residence time is increased aiming at speed deception interference, so that the speed resolution of the target radar can be improved.
In this embodiment, the types of transmission interference include: at least one of noise amplitude modulation interference, noise frequency modulation interference, distance spoofing interference, speed spoofing interference, dense replication decoy interference, and intermittent sampling interference.
The interference effect evaluation indexes include: at least one of pulse width, pulse repetition frequency, beam offset, pulse amplitude, carrier frequency, bandwidth, and beam dwell time.
It should be understood that the generation mechanism of different types of interference is different, and therefore different evaluation indexes need to be selected for different functions of the target radar. Illustratively, for noise amplitude modulation interference, a party can transmit strong power noise interference aiming at a target radar, the interference frequency band is narrow, the target radar realizes interference suppression by changing a frequency working interval through frequency agility and increases the transmission power to improve the target detection probability, and the evaluation indexes selected in the embodiment are as follows: pulse width, pulse repetition frequency, pulse amplitude, carrier frequency; for noise frequency modulation interference, our party transmits broadband noise interference aiming at a target radar, the target radar reduces interference influence by improving transmission power, and the evaluation indexes selected in the embodiment are as follows: pulse width, pulse repetition frequency, pulse amplitude, carrier frequency; for the distance deception jamming, the party modulates and forwards the jamming signals with high similarity except the time delay information, the target radar can resist the jamming in the modes of frequency agility, waveform design and the like, and the evaluation indexes selected in the embodiment are as follows: pulse width, pulse repetition frequency, beam offset, pulse amplitude, carrier frequency, bandwidth; for speed deception jamming, the party modulates and forwards the jamming signal with the false doppler information, and the target radar adopts frequency agility, diversity and other modes for jamming resistance, wherein the method for increasing the transmitting signal power has a poor suppression effect, and the evaluation indexes selected in the embodiment are as follows: pulse width, pulse repetition frequency, beam offset, pulse amplitude, carrier frequency, beam dwell time; for dense replication false target interference, one party modulates and forwards a plurality of false targets with different distances and speeds, a target radar mainly adopts modes such as waveform design and frequency agility to carry out interference suppression, and the distance and speed resolution of the radar are increased, and the selected evaluation indexes are as follows: pulse width, pulse repetition frequency, beam offset, pulse amplitude, carrier frequency, bandwidth, beam dwell time; further, for intermittent sampling interference, one party intermittently samples and sequentially forwards signals of the target radar in one sampling period, the target radar can perform interference suppression in the modes of waveform design, intra-pulse agility, interference cancellation and the like, and the pulse width, the pulse repetition frequency, the beam offset, the pulse amplitude, the carrier frequency, the bandwidth and the beam residence time are selected as evaluation indexes in the embodiment.
Optionally, the radar sample data set includes a plurality of sample data respectively corresponding to the plurality of interference types; in this embodiment, based on a radar sample data set and a gray correlation analysis method, interference effect evaluation indexes are screened according to the following steps:
s201, taking sample data corresponding to each interference effect evaluation index of the same interference type as a sample data sequence, and dividing the sample data in the sample data sequence by first sample data in the sequence to obtain a first sequence;
s202, after a reference index of each interference type is determined from a plurality of interference effect evaluation indexes of each interference type, absolute values of differences between first sequences corresponding to other interference effect evaluation indexes of the interference type and ith sample data in the first sequences corresponding to the reference indexes are respectively obtained, and the maximum value and the minimum value of all the absolute values of the differences between the first sequences corresponding to the other interference effect evaluation indexes and the first sequences corresponding to the reference indexes are determined according to the absolute values of the differences;
s203, calculating correlation coefficients and grey correlation degrees between other interference effect evaluation indexes of the interference type and a reference index according to the maximum value and the minimum value;
and S204, screening the interference effect evaluation indexes according to the grey correlation degree.
Specifically, taking noise amplitude modulation interference, noise frequency modulation interference, distance spoofing interference, speed spoofing interference, dense replication false target interference and intermittent sampling interference as examples, in the training process of the GA-BP neural network model, at least 1000 sample data are respectively generated correspondingly for each interference type, and in order to avoid random parameter values, parameter indexes such as pulse pressure ratio and duty ratio can be introduced to test the rationality of the sample data.
Specifically, in step S201, the first sequence is obtained by dividing all sample data in the sample data sequence by the first sample data in the sequence according to the following formula:
y ij '＝y ij /y i＝1,j
wherein, y ij The ith sample data, y in the first sequence corresponding to the interference effect evaluation index j is represented i＝1,j And representing the 1 st sample data in the first sequence corresponding to the interference effect evaluation index j.
Further, the absolute value of the difference between the ith sample data in the first sequence corresponding to the other interference effect evaluation index of the interference type and the first sequence corresponding to the reference index may be expressed as:
Δ j-1 (i)＝|y i,j＝1 '-y i,j>1 '|
the maximum and minimum of the absolute values of all the differences between the first sequence corresponding to the other interference effect evaluation indicators and the first sequence corresponding to the reference indicator can be respectively expressed as:
in step S203, the step of calculating the correlation coefficient between the interference type other interference effect evaluation index and the reference index according to the maximum value and the minimum value includes:
calculating a correlation coefficient gamma between the ith sample data in the first sequence corresponding to the first sequence and the first interference effect evaluation index according to the following formula 1j (i)：
Wherein Max and Min respectively represent the maximum value and the minimum value, and Δ j-1 (i) Absolute value j of difference between ith sample data in first sequence corresponding to other interference effect evaluation indexes representing interference types and first sequence corresponding to reference index>1, zeta is a preset resolution coefficient, and zeta belongs to 0,1;
and respectively calculating the average value of the correlation coefficients between the first sequence corresponding to other interference effect evaluation indexes of the interference type and all sample data in the first sequence corresponding to the reference index, and taking the average value as the correlation coefficient between the other interference effect evaluation indexes of the interference type and the reference index.
Optionally, the gray correlation degree between the interference type other interference effect evaluation index and the reference index is calculated according to the following formula:
wherein, N represents the number of sample data in the sample data sequence, and gamma 1j Indicating the grey correlation degree between the interference effect evaluation index j of the interference type and the reference index, j ≠ 1.
In step S204, the step of screening the interference effect evaluation index according to the gray correlation includes:
and when the grey correlation degree between the other interference effect evaluation indexes of the interference type and the reference index is larger than or equal to a preset threshold value, deleting the other evaluation indexes and keeping the reference index.
Illustratively, the preset threshold may be 0.75.
It should be noted that before the GA-BP neural network model is trained by using sample data, the sample data set may be labeled and normalized, that is, according to the number and size of sample data changes corresponding to the evaluation indexes of the target radar before and after being interfered, the sample data is classified into four evaluation levels, namely, poor evaluation levels, normal evaluation levels, good evaluation levels and excellent evaluation levels, which respectively correspond to four labels, namely, "0", "1", "2" and "3", and the normalization process is to normalize the sample data to the (0,1) interval.
In this embodiment, the GA-BP neural network includes a BP neural network portion and a genetic algorithm portion. The BP neural network part comprises an input layer, a hidden layer and an output layer, wherein nodes of the input layer are determined by the dimension of input data, and 9 interference effect evaluation indexes such as pulse width, pulse repetition frequency, beam deviation, pulse amplitude, carrier frequency, bandwidth, beam residence time, pulse pressure ratio, duty ratio and the like are selected, so that the number of the nodes of the input layer is 9; the output of output layer is interference effect evaluation value, and the node number is 1, and the hidden layer adopts 3 layer structure in order to promote the recognition effect, and node number formula is:
wherein M represents the number of hidden layer nodes, M represents the number of output layer nodes, n represents the number of input layer nodes, and a is a constant from 1 to 10. Alternatively, the structure of the BP neural network may be set to 9-9-12-6-1.
The genetic algorithm part is used for optimizing an initial weight threshold of the BP neural network, and performing population selection, crossing and variation until an optimal weight and threshold with the minimum error of the BP neural network are found, corresponding parameters are set to be 100, the population scale is 40, the crossing probability is selected to be 0.6, and the variation probability is selected to be 0.1.
In the process of training the GA-BP neural network, interference effect evaluation indexes corresponding to various types of screened interference can be input into the network, the indexes removed after feature screening are input to default to 0, the neural network adjusts weight values and threshold values of all layers in the network through a back propagation algorithm until the network converges when the sum of squares of the output differences is lower than a set error, and the training is finished.
The above radar interference effect on-line evaluation intelligent method based on the countermeasure analysis feature screening is further explained by a simulation experiment.
Specifically, a training set and a test set are generated according to the simulation of multifunctional radar sea surface searching and tracking related parameter indexes in a radar manual, and the parameter setting range is as follows: pulse width is 1-200us, pulse repetition frequency is 0.5-20kHZ, wave beam deviation is 0-1.5 degrees, pulse amplitude is reflected as peak power 10-60kW, carrier frequency is 2.9-3.1GHz, bandwidth is 0.2-500MHz, wave beam residence time is 1-5s, pulse pressure ratio is 1-20000, and duty ratio is 0.1-10%. The intermittent sampling interference generates 1000 groups of samples, training set and test set samples 9:1. The GA-BP neural network structure is set to be 9-9-12-6-1, the corresponding parameters in the GA genetic algorithm are set to be 100 times of iteration, 40 population sizes, 0.6 of cross probability selection and 0.1 of variation probability selection.
As shown in fig. 2-3, the evaluation model obtained by training the interference effect evaluation index after the gray-related feature screening is compared with the evaluation model obtained without the gray-related feature screening, and the error results are shown in table 1 below:
TABLE 1
| Model (model) | Mean absolute error | Mean square error | Determining coefficients |
| Non-characterized selection of GA-BP | 0.1393 | 0.13634 | 0.79148 |
| Gray correlation screening GA-BP | 0.0923 | 0.08932 | 0.86059 |
In table 1, the smaller the mean absolute error value is, the higher the prediction accuracy is, the degree of the mean square error reflecting the data change is, the fitting degree of the coefficient representation model is determined, and the closer the mean square error is to 1, the better the fitting effect is. Obviously, the accuracy of the GA-BP (genetic algorithm optimized BP neural network) evaluation result after gray correlation characteristic screening is about 90%, and is 84% higher than the accuracy of the GA-BP (genetic algorithm optimized BP neural network) evaluation result without characteristic screening.
The beneficial effects of the invention are that:
the invention provides an intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening, which is characterized in that an interference effect evaluation index is selected according to the interference type emitted by one party, so that the problem that analysis and evaluation can be carried out only when parameter information of two countermeasures is known is avoided; the method integrates the angles of an interference party and a radar party against the two parties, considers and analyzes the correlation between data by combining with an actual interference scene, screens a redundancy evaluation index by a grey correlation analysis method, introduces a GA-BP neural network model to realize online evaluation and intelligent evaluation of the interference effect, avoids manual intervention and improves the evaluation accuracy.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by those skilled in the art.
While the present application has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the drawings, the disclosure, and the appended claims.
The foregoing is a further detailed description of the invention in connection with specific preferred embodiments and it is not intended to limit the invention to the specific embodiments described. For those skilled in the art to which the invention pertains, numerous simple deductions or substitutions may be made without departing from the spirit of the invention, which shall be deemed to belong to the scope of the invention.
Claims (7)
1. An intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening is characterized by comprising the following steps:
determining an interference effect evaluation index according to the type of the transmission interference, the working mode conversion of the target radar after the interference and the pre-estimated anti-interference measure of the target radar;
acquiring index data of the target radar before and after interference through real-time reconnaissance;
inputting the index data into an evaluation model to obtain an evaluation result of the transmitted interference type; wherein the evaluation model is: and screening the interference effect evaluation indexes based on a radar sample data set and a grey correlation analysis method, and training to obtain the GA-BP neural network model.
2. The intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening according to claim 1, wherein the type of the transmission interference comprises: at least one of noise amplitude modulation interference, noise frequency modulation interference, distance spoofing interference, speed spoofing interference, dense replication decoy interference, and intermittent sampling interference.
3. The intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening of claim 2, wherein the interference effect evaluation index comprises: at least one of pulse width, pulse repetition frequency, beam offset, pulse amplitude, carrier frequency, bandwidth, and beam dwell time.
4. The intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening according to claim 1, wherein the radar sample data set includes a plurality of sample data respectively corresponding to a plurality of interference types;
based on a radar sample data set and a grey correlation analysis method, screening the interference effect evaluation indexes according to the following steps:
taking sample data corresponding to each interference effect evaluation index of the same interference type as a sample data sequence, and dividing the sample data in the sample data sequence by first sample data in the sequence to obtain a first sequence;
after a reference index of each interference type is determined from a plurality of interference effect evaluation indexes of each interference type, respectively obtaining absolute values of differences between the first sequences corresponding to other interference effect evaluation indexes of the interference type and ith sample data in the first sequences corresponding to the reference indexes, and determining the maximum value and the minimum value of all the absolute values of the differences between the first sequences corresponding to other interference effect evaluation indexes and the first sequences corresponding to the reference indexes according to the absolute values of the differences;
calculating the correlation coefficient and the grey correlation degree between the other interference effect evaluation indexes of the interference type and the reference index according to the maximum value and the minimum value;
and screening the interference effect evaluation indexes according to the grey correlation degree.
5. The intelligent method for radar interference effect online evaluation based on countermeasure analysis feature screening of claim 4, wherein the step of calculating the correlation coefficient between the interference type other interference effect evaluation index and the reference index according to the maximum value and the minimum value comprises:
calculating a correlation coefficient gamma between the ith sample data in the first sequence corresponding to the other interference effect evaluation indexes of the interference type and the first sequence corresponding to the reference index according to the following formula 1j (i)：
Wherein Max and Min respectively represent the maximum value and the minimum value, and Δ j-1 (i) Absolute value j of difference between ith sample data in first sequence corresponding to other interference effect evaluation indexes representing interference types and first sequence corresponding to reference index>1, zeta is a preset resolution coefficient, and zeta belongs to (0,1);
and respectively calculating the average value of the correlation coefficients between the first sequence corresponding to other interference effect evaluation indexes of the interference type and all sample data in the first sequence corresponding to the reference index, and taking the average value as the correlation coefficient between the other interference effect evaluation indexes of the interference type and the reference index.
6. The intelligent method for online evaluation of radar interference effect based on countermeasure analysis feature screening of claim 5, wherein the gray correlation between the interference type other interference effect evaluation indexes and the reference index is calculated according to the following formula:
wherein, N represents the number of sample data in the sample data sequence, and gamma is 1j Indicating the grey correlation degree between the interference effect evaluation index j of the interference type and the reference index, j ≠ 1.
7. The intelligent method for radar interference effect online evaluation based on countermeasure analysis feature screening of claim 6, wherein the step of screening the interference effect evaluation index according to the grey correlation degree comprises:
and when the grey correlation degree between the other interference effect evaluation indexes of the interference type and the reference index is larger than or equal to a preset threshold value, deleting the other evaluation indexes and keeping the reference index.
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