A Multi-Class Average Maximization Method for Extracting True and False Target Features for Radar Target Recognition
A radar target and class averaging technology, applied in radio wave measurement systems, instruments, etc., can solve the problem of reduced recognition performance of feature extraction methods, and achieve the effect of improving classification performance
Active Publication Date: 2022-07-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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[0008] However, feature extraction methods such as subspace are only suitable for the case where the sample data is Gaussian distribution, and the distribution of sample data in practice may be non-Gaussian. For non-Gaussian distribution, the recognition performance of conventional feature extraction methods is significantly reduced.
There is room for further improvement in the recognition performance of existing conventional feature extraction methods
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[0043] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments.
[0044] The multi-class average maximization feature extraction method proposed by the present invention uses multi-component Gaussian distribution to represent the likelihood functions of various target data, and can still accurately describe the distribution of target data under the condition of non-Gaussian distribution. Therefore, the most effective features for classification and recognition of radar target recognition are screened out, the defects of conventional feature extraction methods are overcome, and the classification performance of radar true and false targets is effectively improved.
[0045]The realization process of the multi-class average maximum true and false target feature extraction method for radar target recognition of the present invention is as f...
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The invention discloses a multi-class average maximization true and false target feature extraction method for radar target recognition, which belongs to the technical field of radar target recognition. The present invention uses multi-component Gaussian distribution to represent the likelihood function of the target data. In the case that the target sample data is non-Gaussian distribution, the distribution of the target data can still be accurately described, and the most effective one-dimensional range image feature elements are selected from the target. classification identification features. It overcomes the disadvantage that the conventional method is only suitable for the Gaussian distribution of the sample data, thereby improving the radar target recognition performance.
Description
technical field [0001] The invention belongs to the technical field of radar target recognition, in particular to a multi-class average maximization true and false target feature extraction for radar target recognition. Background technique [0002] Radar target recognition needs to extract the relevant information signs and stable features (target features) of the target from the radar echo of the target and determine its attributes. It identifies targets based on their back electromagnetic scattering. Using the characteristics of the scattered field generated by the target in the far region of the radar, information for target identification (target information) can be obtained. The obtained target information is processed by computer and compared with the characteristics of the existing target, so as to achieve the purpose of automatically identifying the target. Radar target recognition consists of two parts: feature extraction and classification recognition. [0003]...
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IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 周代英张瑛沈晓峰廖阔冯健
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA