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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2020, Vol. 41 ›› Issue (10): 23912-023912.doi: 10.7527/S1000-6893.2020.23912

• Review • Previous Articles     Next Articles

Phantom track deception against radar networks using UAVs: Review

BAI Peng1,2, WANG Yubing1, LIANG Xiaolong1, ZHANG Jiaqiang1,2, WANG Weijia3   

  1. 1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China;
    2. National Key Laboratory of Air Traffic Collision Prevention, Air Force Engineering University, Xi'an 710051, China;
    3. Institute of Systems Engineering, Military Academy of Sciences, Beijing 100101, China
  • Received:2020-02-29 Revised:2020-07-20 Published:2020-07-17
  • Supported by:
    National Natural Science Foundation of China (61703427)

Abstract: The demand for system operations in future wars is not only to "win with quantity" by scale, but also to have tactical wisdom to "win by flexibility". Only by interfering with the enemy's inference of our intention, or even designing false intentions to confuse the enemy battlefield awareness, can we shape the situation in the game between the two sides, and gain the initiative in the top-level design and strategic tactics. An effective countermeasure to cheat the enemy's network radars is to use UAV swarms which, in addition to detecting the enemy, consume the computing resources of the enemy's network radars, and interfere with the enemy's inference of our intention, thereby improving the perception ability and survival ability of our side. The concept and the current progress of phantom track deception is introduced, followed by analyses of the key problems and technical difficulties according to the characteristics of phantom track deception jamming, involving phantom design and generation, estimation problems caused by uncertain errors, the time delay strategy and same source testing rule, and dynamic constraints. The application prospect and the future research direction of phantom track deception are provided.

Key words: phantom track deception, UAVs, radar networks, intention inference, system operations

CLC Number: