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Radar radiation source signal depth pulse internal feature automatic extraction method

A radar signal, automatic extraction technology, applied in the direction of instruments, biological neural network models, calculations, etc., can solve the problems of insufficient objectivity, achieve the effect of clear classification boundaries, high accuracy rate, and enhanced linear separability

Active Publication Date: 2019-04-12
AIR FORCE UNIV PLA
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Problems solved by technology

[0004] The present invention aims at the problem of insufficient objectivity due to dependence on prior knowledge when extracting intrapulse features of radar signals, and provides an automatic extraction method for deep intrapulse features of radar radiation source signals, and the correct identification effect of radiation source signals is better

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  • Radar radiation source signal depth pulse internal feature automatic extraction method
  • Radar radiation source signal depth pulse internal feature automatic extraction method
  • Radar radiation source signal depth pulse internal feature automatic extraction method

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[0036] The technical solutions of the present invention will be further described below in conjunction with the embodiments.

[0037] Firstly, the framework of the autoencoder is analyzed, and then the SAE is obtained by imposing specific sparsity constraints. Finally, the sparse autoencoder is optimized and its training scheme is determined, and the depth intrapulse features of the radar signal are automatically extracted by using the parameters of the encoding layer.

[0038] When optimizing the deep autoencoder for extracting intravein features, it is first necessary to add sparse constraints to the deep autoencoder, and then increase the number of hidden layers and neurons, adjust the distribution of hidden layer nodes and change the weights Optimize the basic framework of DAE; Finally, according to the needs of different tasks, select the appropriate cost function and its optimization strategy hidden layer quality factor and performance index when optimizing system paramet...

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Abstract

The invention provides a radar radiation source signal depth pulse internal feature automatic extraction method. The method comprises the following steps: firstly, applying a specific sparse constraint to an auto-encoder to obtain a sparse auto-encoder; then, the sparse auto-encoder is optimized, a training scheme of the sparse auto-encoder is determined, the coding layer parameters are used for automatically extracting the radar signal depth intra-pulse characteristics, and the extracted characteristics can better achieve the classification and recognition of the radar radiation source signals within a large signal-to-noise ratio range.

Description

technical field [0001] The invention relates to the field of radar information processing, in particular to a method for automatically extracting deep intrapulse features of radar radiation source signals. Background technique [0002] The key to effectively sorting and identifying radar signals is to extract features that can reflect the essence of the signal, and the autoencoder (autoencoder, AE) under the deep learning theory aims at reconstructing the original input at the output layer, because it does not It needs additional supervision information to be able to extract the distributed features of the data, and can avoid the implicit subjectivity when designing features, and has become a hot direction that has attracted people's attention in recent years. In 2006, Hinton improved the prototype autoencoder structure and obtained a deep autoencoder (deep autoencoder, DAE). Bengio deepened the deep autoencoder and proposed a sparse autoencoder (sparse autoencoder, SAE), wh...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06N3/045G06F2218/12
Inventor 王世强李兴成白娟徐彤郑桂妹孙青
Owner AIR FORCE UNIV PLA
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