Research on Multiaircrafts Cooperative Arraying to Jam Based on Multiobjective Moth-Flame Optimization Algorithm

doi:10.1109/ACCESS.2022.3193094

2022-01-01

DOI：10.1109/ACCESS.2022.3193094

原文链接

原文求助

摘要

En 中文

The problem of cooperative arraying to jam is an important part of EW mission planning. Aiming at the problem that multi-objective optimization algorithm is easy to fall into local optimum and converge in three-objective optimization, a multi-aircraft jamming and cooperative arraying method based on improved multi-objective Moth-flame optimization algorithm is proposed. Firstly, the simulation environment is established by using digital elevation map and radar detection model. Then, based on the multi-objective Moth-flame optimization algorithm, the population initialization is completed by using Logistic-Tent chaotic map, which increases the diversity and uniformity of the solution and improves the search ability of the algorithm; Then, the decision factor and Gaussian difference mutation are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current solution and search again according to the disturbance, thus enhancing the search ability of the algorithm; Finally, by comparing with NSGA-II, MOEA/D, MOPSO and NSMFO algorithms on test functions of ZDT and DTLZ series, the performance of the algorithm is verified, and it is proved that multi-objective Moth-flame optimization algorithm is better than other algorithms in both convergence and diversity. In addition, compared with the NSMFO and MOEA/D algorithms in the arraying simulation experiment. The values of the interference power, the width of the route safety zone and the detection area of the radar obtained by the algorithm in this paper, are 117.9 kw, 46 km, and 1727 km(2). Compared with the results of the other two algorithms, the effectiveness of interference is improved by 39.8%, 22.8% and 41.9% respectively.

Keyword:

Jamming

Radar

Radar detection

Radar antennas

Optimization

Convergence

Statistics

Cooperative arraying to jam

multi-objective optimization

multi-objective Moth-flame optimization algorithm

chaotic mapping