Model evaluation method, radar signal recognition method and corresponding device

A radar signal and model evaluation technology, which is applied in character and pattern recognition, instruments, radio wave measurement systems, etc., can solve the problems of accuracy impact, model confidence problem, agnostic black box, etc., to improve quality and increase trust degree, the effect of improving transparency

Inactive Publication Date: 2018-12-14
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] At this stage, radar signal recognition basically uses machine learning methods. However, most machine learning methods are applicable within a specific range, requiring artificial selection of algorithms, adjustment of hyperparameters, etc. If the data set changes, the prediction will be accurate. sex will be affected
At the same time, although the model trained by machine learning can get good prediction results, in most cases, the obtained model is an internally agnostic black box, which will bring about the problem of model confidence

Method used

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  • Model evaluation method, radar signal recognition method and corresponding device
  • Model evaluation method, radar signal recognition method and corresponding device
  • Model evaluation method, radar signal recognition method and corresponding device

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no. 1 example

[0070] figure 2 A flow chart of the model evaluation method provided by the first embodiment of the present invention is shown. refer to figure 2 , the method includes:

[0071] S10: The processor 106 of the electronic device 100 obtains a training set and a test set of radar signals.

[0072] The data of the radar signal can be collected by professional equipment, and the collected data is divided into a training set and a test set. Both the training set and the test set include at least one radar signal sample. The radar signal sample in the training set is called the training sample, and the radar signal sample in the test set The radar signal samples are called test samples.

[0073] S11: The processor 106 of the electronic device 100 performs feature extraction on the training samples in the training set by intrapulse feature analysis to obtain a feature data set.

[0074] Intrapulse feature analysis is a method of extracting radar signal features. Each training sam...

no. 2 example

[0120] The second embodiment of the present invention provides a radar signal identification method, which can identify parameters including the modulation mode of the radar signal. This method first obtains actual radar signal samples, which can be collected by professional equipment. Then, feature extraction is performed on the actual radar signal sample by using intrapulse feature analysis to obtain signal features. For specific methods, refer to the relevant description in the first embodiment. Then based on the extracted signal features, use the trained classification model to predict the type of the actual radar signal sample, wherein the trained classification model is evaluated as an available classification model by the model evaluation method provided in the first embodiment, of course In practice, it can also be a classification model that is evaluated as unusable but usable after improvement. Finally, the interpretability of the prediction results of the actual ra...

no. 3 example

[0124] Figure 7 A functional block diagram of the model evaluation device 200 provided by the third embodiment of the present invention is shown. refer to Figure 7 , the device includes a sample acquisition module 210 , a feature extraction module 220 , a model optimization module 230 , an interpretability calculation module 240 and a usability judgment module 250 .

[0125] Wherein, the sample acquisition module 210 is used to obtain a training set and a test set of radar signals;

[0126] The feature extraction module 220 is used to perform feature extraction on the training samples in the training set by intrapulse feature analysis to obtain a feature data set;

[0127] The model optimization module 230 is used to utilize the automatic process optimization TPOT method based on the tree structure to optimize the training process of the feature data set and obtain a classification model for classifying radar signals;

[0128] The interpretability calculation module 240 i...

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Abstract

The invention relates to the technical field of radar signal recognition, and provides a model evaluation method, a radar signal recognition method and a corresponding device. The model evaluation methods include: acquiring the training set and test set of radar signal; in-pulse feature analysis is used to extract features from the training samples in the training set, and the feature dataset is obtained. The TPOT method is used to optimize the training process of the feature data set, and the classification model for radar signal classification is obtained. The LIME method is used to calculate the interpretability of test samples in the test set. Classification model are evaluated for availability base on interpretability. TPOT method can automatically optimize the training process, and can obtain high-quality classification model without manual intervention in the training process, saving time and labor. The LIME method is used to calculate the interpretability of test samples and evaluate the usability of the classification model, which is helpful to improve the transparency of the model, increase the degree of user trust in the model, and improve the quality of the obtained model.

Description

technical field [0001] The invention relates to the technical field of radar signal recognition, in particular to a model evaluation method, a radar signal recognition method and a corresponding device. Background technique [0002] With the development of modern technology, the complexity of new radars and anti-reconnaissance and anti-jamming technologies are becoming more and more mature. It is becoming more and more important to identify different radar radiation source pulses from dense radar pulse streams. It is an important symbol to measure the advanced level of radar countermeasure equipment technology. [0003] At this stage, radar signal recognition basically uses machine learning methods. However, most machine learning methods are applicable within a specific range, requiring artificial selection of algorithms, adjustment of hyperparameters, etc. If the data set changes, the prediction will be accurate. Sex will be affected. At the same time, although the model ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01S7/02
CPCG01S7/021G06F2218/08G06F2218/12G06F18/2411G06F18/214
Inventor 葛鹏金炜东郭建
Owner SOUTHWEST JIAOTONG UNIV
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