Implementation of a combinatorial‐optimisation‐based threat evaluation and jamming allocation system

Electromagnetic warfare is the most extensive and most hidden theatre of battle in modern warfare. To enhance the jamming effectiveness of a cooperative jammer platform against the threat of a radar net, a combinatorial-optimisation-based threat evaluation and jamming allocation (COTEJA) system is proposed. This COTEJA system fully considers the confrontation analysis in the jammer-radar process, including the interactions between radars, jammers, and jammer-radar pairs, and emphasises the realisation of cooperative jamming strategies. The cooperative jamming strategies include the combination of jamming techniques and optimisation algorithms for the objective function. The performance of the COTEJA system is evaluated through a combat mission that considers a platform with four jammers attacking five threats. In addition, the extended permutation-based differential evolution algorithm is used for the first time to optimise the jamming coding matrix, which effectively reduces the danger value of netted radar under multiple constraints. The numerical results reveal that the COTEJA system can make the optimal jamming decision within 1 s, which improves the survival ability of the platform in a complicated electromagnetic environment.

[1]  Jh van Vuuren,et al.  Threat evaluation and weapon assignment decision support: A review of the state of the art , 2007 .

[2]  Jin Soo Kim,et al.  Threat evaluation of enemy air fighters via neural network-based Markov chain modeling , 2017, Knowl. Based Syst..

[3]  V. Krishnamurthy,et al.  Decentralized algorithms for netcentric force protection against antiship missiles , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[4]  W. P. du Plessis,et al.  Threat evaluation and jamming allocation , 2017 .

[5]  Fredrik Johansson,et al.  Performance Evaluation of TEWA Systems for Improved Decision Support , 2009, MDAI.

[6]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[7]  Orhan Karasakal,et al.  Air defense missile-target allocation models for a naval task group , 2008, Comput. Oper. Res..

[8]  Yi Zhuang,et al.  Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem , 2014 .

[9]  G. A. Watson,et al.  IMMPDAF for radar management and tracking benchmark with ECM , 1998 .

[10]  Ning Wang,et al.  A novel hybrid differential evolution approach to scheduling of large-scale zero-wait batch processes with setup times , 2012, Comput. Chem. Eng..

[11]  Haiqing Jiang,et al.  Optimal allocation of cooperative jamming resource based on hybrid quantum-behaved particle swarm optimisation and genetic algorithm , 2017 .

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Darren John Bachmann,et al.  Game Theoretic Analysis of Adaptive Radar Jamming , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Wang Yanxia,et al.  Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm , 2008 .