Airborne multifunctional radar working mode identification method based on improved K-nearest neighbor

A technology of recognition method and working mode, which is applied in the field of electronic warfare signal processing, can solve the problems that the recognition accuracy rate needs to be improved, and achieve the effect of more adaptability of classification recognition, optimization of accuracy rate, and reduction of complexity

Pending Publication Date: 2021-11-05
XIDIAN UNIV
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Chen You and others proposed a particle swarm optimization algorithm to adaptively optimize the DPNN network, and use the trained

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  • Airborne multifunctional radar working mode identification method based on improved K-nearest neighbor
  • Airborne multifunctional radar working mode identification method based on improved K-nearest neighbor
  • Airborne multifunctional radar working mode identification method based on improved K-nearest neighbor

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Example Embodiment

[0061] The present invention will be further described in connection with the specific embodiments, but the embodiments of the present invention are not limited thereto.

[0062] like figure 1 As shown, one of the present invention provides a multi-function radar operating mode identification method based on improved K-neighboring an adjacent.

[0063] S1, obtain the data set of multi-function radar signals under composite field;

[0064] Wherein, the data set includes signals in a plurality of operating modes, and the signal-to-noise ratio points of each signal include a plurality of samples, and there are multiple samples at different signal-to-noise ratios;

[0065] This step acquisitions the typical working mode data set under empty-empty, empty-ground composite scenarios, which includes ten typical operating modes. For empty-ground scene, the data set includes a dynamic target detection mode signal, a dynamic tracking mode signal, and a synthetic aperture radar mode signal; f...

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Abstract

The invention discloses an airborne multifunctional radar working mode identification method based on improved K-nearest neighbor. The method comprises the steps of acquiring a data set of multifunctional radar signals in a composite scene; preprocessing the data set to obtain multi-dimensional feature representation of each sample in the data set; performing one-bit coding on each sample to obtain a sample label; further obtaining a label sample set, and then randomly selecting to form a training set and a test set; according to the inter-class boundary, effectively rejecting samples with fuzzy boundaries in the training set by using a 1-NN algorithm to obtain a target training set; then carrying out PCA principal component contribution rate analysis, reducing the influence of redundant features on classification recognition, and obtaining a target training set after dimension reduction; repeatedly training a preset k-nearest neighbor algorithm model, and determining an optimal value of k; and using the test set to test the k-nearest neighbor algorithm model determining k as of the optimal value for identification. The method can reduce the classification and identification time, and has good robustness and generalization.

Description

technical field [0001] The invention belongs to the technical field of electronic warfare signal processing, and in particular relates to an airborne multifunctional radar working mode recognition method based on improved K-nearest neighbors. Background technique [0002] With the continuous development and innovation of equipment technology, in today's electromagnetic environment, a single-system radar can no longer meet the operational needs in complex environments. Therefore, multi-functional radars have become more and more important and their applications are becoming more and more extensive. In electronic warfare, what kind of working mode the multi-function radar is currently in and what kind of combat intention it will have at the next moment play an important role in electronic intelligence reconnaissance, electronic support and threat warning systems. Traditional classification and identification methods rely on For the parameter extraction of radar signals, manual...

Claims

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Application Information

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IPC IPC(8): G01S7/02G01S13/88G06K9/62
CPCG01S13/88G01S7/021G06F18/2135G06F18/241
Inventor 李鹏田卫东武斌张葵惠晓龙申慧芳张东燕
Owner XIDIAN UNIV
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