Model-free reinforcement learning-based multistage smart noise jamming method

A smart noise jamming and reinforcement learning technology, applied in the field of radar, can solve the problems of jamming performance loss, inaccuracy, and high jamming power

Active Publication Date: 2019-07-19
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, expert experience is often inaccurate, and inaccurate jamming power allocation can lead to loss of jamming performance
1) Too small interference power cannot effectively reduce the performance of FCR
2) If the interference power is too large, the probability of finding interference increases

Method used

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Embodiment Construction

[0031] In order to describe content of the present invention conveniently, at first the following terms are explained:

[0032] Term 1: Fire Control Radar

[0033] Fire control radar refers to the radar used to accurately track targets and provide target coordinate data for weapon command and control systems. Abbreviated as FCR.

[0034] Term 2: Radar warning receiver

[0035] Radar warning receiver refers to the electronic countermeasure equipment used to intercept, analyze and identify enemy radar signals, judge the threat level in real time and give timely warning, referred to as RWR.

[0036] Term 3: Track while scanning

[0037] Tracking while scanning refers to the working method in which the radar scans the search space while tracking single or multiple targets, referred to as TWS.

[0038] Term 4: Track plus search

[0039] Tracking plus search refers to the way that the radar can complete the search and precise tracking of single or multiple targets at the same t...

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Abstract

The invention discloses a model-free reinforcement learning-based multistage smart noise jamming method which is applied to the technical field of radar, and aims at solving the optimum jamming powerdistribution problems of jammers under the condition that the environment models such as enemy fire control radar jamming methods, anti-jamming measures and working mode conversion laws are unknown. According to the method, a multistage jamming power distribution problem is modeled to a Markov decision making process of an unknown environment model; in order to assess multistage noise jamming performance, an average search-locking time of fire control radar is selected as an evaluation index; a principle of noise jamming power distribution is analyzed, and aiming at the challenge of the unknown environment model, a reinforcement learning framework for the multistage jamming power distribution problem is established; and finally, a Q-learning algorithm0based multistage jamming power distribution method is put forward. The method is capable of effectively solving the optimum distribution problem of jamming power in practical application and then improving the jamming success rate.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a radar smart noise jamming technology. Background technique [0002] Smart noise jamming means that the jammer emits a coherent noise-like signal to overlap and cover the target echo signal in the time domain, thereby confusing the radar target detection and tracking. Noise jamming technology plays a key role in electronic countermeasures. Whether effective jamming can be carried out is related to the safety of our combat resources and combat personnel. Therefore, smart noise jamming has become a key research topic for experts at home and abroad. [0003] Because modern fire control radar has strong anti-jamming capability and multiple working modes. In the face of this modern FCR, the traditional noise interference performance is getting worse and worse. In this case, it is necessary to study better smart noise interference measures. The generation of smart noise jamming wave...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/38G06N20/00
CPCG01S7/38G06N20/00
Inventor 张天贤王远航贾瑞韩毅孔令讲杨晓波
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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