Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach
Current electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of radar intelligent anti-jamming systems are analyzed,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/3/236 |
| _version_ | 1797614127702605824 |
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| author | Huaixi Xing Qinghua Xing Kun Wang |
| author_facet | Huaixi Xing Qinghua Xing Kun Wang |
| author_sort | Huaixi Xing |
| collection | DOAJ |
| description | Current electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of radar intelligent anti-jamming systems are analyzed, and a scheduling method for radar anti-jamming action based on the Partially Observable Markov Process (POMDP) is proposed. The sample-based belief distribution is used to reflect the radar’s cognition of the environment and describes the uncertainty of the recognition of jamming patterns in the belief state space. The belief state of jamming patterns is updated with Bayesian rules. The reward function is used as the evaluation criterion to select the best anti-jamming strategy, so that the radar is in a low threat state as often as possible. Numerical simulation combines the behavioral prior knowledge base of radars and jammers and obtains the behavioral confrontation benefit matrix from the past experience of experts. The radar controls the output according to the POMDP policy, and dynamically performs the best anti-jamming action according to the change of jamming state. The results show that the POMDP anti-jamming policy is better than the conventional policy. The POMDP approach improves the adaptive anti-jamming capability of the radar and can quickly realize the anti-jamming decision to jammers. This work provides some design ideas for the subsequent development of an intelligent radar. |
| first_indexed | 2024-03-11T07:05:26Z |
| format | Article |
| id | doaj.art-4b824e2138824c7abd21e4ffeddb3f18 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T07:05:26Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-4b824e2138824c7abd21e4ffeddb3f182023-11-17T08:58:15ZengMDPI AGAerospace2226-43102023-02-0110323610.3390/aerospace10030236Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process ApproachHuaixi Xing0Qinghua Xing1Kun Wang2Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaCurrent electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of radar intelligent anti-jamming systems are analyzed, and a scheduling method for radar anti-jamming action based on the Partially Observable Markov Process (POMDP) is proposed. The sample-based belief distribution is used to reflect the radar’s cognition of the environment and describes the uncertainty of the recognition of jamming patterns in the belief state space. The belief state of jamming patterns is updated with Bayesian rules. The reward function is used as the evaluation criterion to select the best anti-jamming strategy, so that the radar is in a low threat state as often as possible. Numerical simulation combines the behavioral prior knowledge base of radars and jammers and obtains the behavioral confrontation benefit matrix from the past experience of experts. The radar controls the output according to the POMDP policy, and dynamically performs the best anti-jamming action according to the change of jamming state. The results show that the POMDP anti-jamming policy is better than the conventional policy. The POMDP approach improves the adaptive anti-jamming capability of the radar and can quickly realize the anti-jamming decision to jammers. This work provides some design ideas for the subsequent development of an intelligent radar.https://www.mdpi.com/2226-4310/10/3/236anti-jamming countermeasuresPOMDPintelligent radarelectronic warfare |
| spellingShingle | Huaixi Xing Qinghua Xing Kun Wang Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach Aerospace anti-jamming countermeasures POMDP intelligent radar electronic warfare |
| title | Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach |
| title_full | Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach |
| title_fullStr | Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach |
| title_full_unstemmed | Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach |
| title_short | Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach |
| title_sort | radar anti jamming countermeasures intelligent decision making a partially observable markov decision process approach |
| topic | anti-jamming countermeasures POMDP intelligent radar electronic warfare |
| url | https://www.mdpi.com/2226-4310/10/3/236 |
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