Optimization of False Target Jamming against UAV Detection

Abstract

Unmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but currently there are few research efforts devoted to this adversarial problem. This paper formulates an optimization problem of false target jamming based on a counterpart problem of UAV detection, where each false target jamming solution is evaluated according to its adversarial effects on a set of possible UAV detection solutions. To efficiently solve the problem, we propose an evolutionary framework, which is implemented with four popular evolutionary algorithms by designing/adapting their evolutionary operators for false target jamming solutions. Experimental results on 12 test instances with different search regions and numbers of UAVs and false targets demonstrate that the proposed approach can significantly reduce the UAV detection probability, and the water wave optimization (WWO) metaheuristic exhibits the best overall performance among the four evolutionary algorithms. To our knowledge, this is the first study on the optimization of false target jamming against UAV detection, and the proposed framework can be extended to more countermeasures against UAV operations.

Crop cultivation planning with fuzzy estimation using water wave optimization

“…Since its proposal, WWO has attracted considerable attention in both academic and industrial communities. There have been a lot of work on modified WWO algorithms ( Zheng and Zhang, 2015 ; Wu et al., 2017 ; Zhang et al., 2018 ; Zhang et al., 2019 ) and their applications to a variety of engineering optimization problems ( Zheng et al., 2017c ; Fard and Hajaghaei-Keshteli, 2018 ; Shao et al., 2018 ; Shao et al., 2019 ; Zhao et al., 2019 ; Zhou et al., 2019 ; Yan et al., 2021 ; Su et al., 2022 ; Zhang et al., 2022 ).…”

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

“…For example, a constant jammer will keep transmitting a signal at high power until its physically removed from the networks. For a random jammer, the enemy emits a jamming signal at a random time [21,22]. It switches between active and transmitting modes at various times.…”