Anti-interference decision-making method applied to cognitive radar

Abstract

The invention discloses an anti-interference decision-making method applied to a cognitive radar, and the method comprises the steps: obtaining environment state information, which comprises external interference information at a current moment and anti-interference actions at all historical moments; taking the environment state information as an initial value of a state S = {Jt, A1, A2,..., At-1} in a Markov decision process < S, A, P, R, gamma >, and executing Markov decision based on a reinforcement learning algorithm until a behavior value matrix converges; and selecting the next anti-interference action of the cognitive radar by using a standard greedy algorithm according to the converged behavior value matrix, wherein P represents the state transition probability, A represents the anti-interference action, R represents the anti-interference income, and gamma represents the time discount factor. According to the method, the cognitive thunder can select a proper anti-interference measure from complex anti-interference measures without being limited by the type of an attacker.

Description

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AI Technical Summary

Benefits of technology

This technology uses advanced techniques like machine learning or artificial intelligence (Al) to learn about how things behave when there are other objects that may also affect them during their executions. It then selects an best way to prevent attacks based solely upon these rules without relying entirely on humans' judgment.

Problems solved by technology

In this patented method described by Crane et al., we discuss different ways to prevent signals transmitted over wireless networks caused by external sources like radios (radio equipment). One way involves adding extra hardware components called radomes on top of existing antennas. These radoms help detect any unwanted electromagnetic waves emanating from other devices nearby without affecting their performance. Another approach suggests utilizing advanced techniques such as signal processing algorithms to identify specific types of noise and improve detection capabilities.

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