Migration learning based radar jamming decision-making method

Abstract

The invention provides a migration learning based radar jamming decision-making method. The method can realize jamming decision on an unknown threat data set by constructing a radar parameter prior knowledge base and the low dimensional hidden space of the unknown threat data set, and extracting training samples from the hidden space to perform training through a support vector machine, so that jamming decision accuracy of an intelligent confrontation system can be effectively enhanced.

Description

technical field [0001] The invention belongs to the technical field of electronic interference, and in particular relates to a radar interference decision-making method based on migration learning. Background technique [0002] Electronic jamming refers to the radio wave jamming measures taken to disable or reduce the effectiveness of the enemy's electronic equipment and systems. It is an important link in modern information warfare and runs through the entire process of the war. The ability of electronic equipment and systems to carry out battlefield reconnaissance, combat command, communication and weapon control and guidance creates favorable conditions for concealing one's own intentions and improving the survivability of one's own aircraft and ships. As the core link of electronic jamming, jamming decision-making refers to the process of selecting jamming patterns suitable for different radar working modes through comparison and matching or corresponding algorithms on t...

AI Technical Summary

Problems solved by technology

Aiming at the problem of jamming pattern selection, Tang Wenlong et al. used different criteria to study the jamming pattern selection method based on game theory. This method is based on sufficient prior knowledge and needs to establish a radar jamming-anti-jamming countermeasure matrix. In the actual process, the countermeasure matrix of both parties is Unknown to each other; Li Yunjie et al. introduced the cognitive principle and proposed a cognitive radar countermeasure process design based on the Q-learning algorithm. According to the radar status, the jamming strategy is dynamically adjusted to make the jamming more targeted, flexible and intelligent. However, it is necessary to establish Under the condition of known radar status; Chen Kai proposes a jamming decision-making system for evaluating jamming effects and correcting jamming knowledge base. The system consists of two parts, the parameter base and the knowledge base, and the structure is relatively complex

The above research cannot be applied to newly installed radars and unknown prior knowledge bases

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