Radar radiation source identification method based on bispectral self-coding features

Abstract

The invention discloses a radar radiation source identification method based on bispectral self-coding features. The method comprises the following steps that step 1, radar radiation source signals are acquired, and preprocessing and noise filtering are performed on the radar radiation source signals; step 2, the direct estimation method is applied to the discrete radiation source signals obtainedby sampling to calculate the bispectral image of the discrete signals; and step 3, feature learning is performed on the extracted bispectral image by using an over-limit learning machine sparse self-coding algorithm, and finally a radar radiation source identification model is constructed by using an over-limit learning machine classification algorithm. The integrated radar radiation source classification framework based on bispectral feature learning and over-limit learning machine identification is constructed so that the rapid and efficient radar radiation source signal identification method can be established.

Description

technical field [0001] The invention belongs to the field of radar radiation source identification, and relates to a radar radiation source identification method based on bispectral self-encoding features, in particular to a radar radiation source bispectral image and an extreme learning machine (ELM) self-encoding feature extraction and classification recognition algorithm. Background technique [0002] The traditional radar radiation source classification process is actually a signal recognition process, that is, after the feature extraction and selection steps are completed, the recognition algorithm is used to make classification decisions. But the traditional method has the following problems: [0003] 1. Bispectrum can effectively deal with interference noise in the analysis of radiation source signals, and can well reflect the essential characteristics of radar radiation sources. However, due to the complex process of calculating bispectral features and the high dim...

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Problems solved by technology

However, due to the complex process of calculating bispectral features and the high dimension of the formed feature vectors, the engineering implementation is faced with problems such as high computational complexity;

[0004] 2. Most of the neural network classification algorithms currently used for radiation source identification are based on shallow network structures. However, shallow network models have limited ability to express complex functions and high-dimensional big data samples, which limits their generalization ability;

[0005] 3. Most of the traditional intelligent recognition algorithms based on the deep network model structure rely on the learning idea of ​​iterative update of parameters. When dealing with bispectral image features of high-dimensional radar emitter signals, they often face problems such as large amount of calculation and high complexity.

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