A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more sci-entific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).     

群时延傅里叶分解模型及其估计方法

建立了基于三角函数级数的群时延模型,并理论证明了仅采用有限阶数的三角函数群时延即可表征任意群时延对信号相关峰的影响。在此基础上,通过测量信号相关峰,并与不同三角函数群时延组合下的相关峰进行匹配搜索即可估计群时延的三角函数分解级数,从而估计得到相关峰影响等效的群时延特性。仿真中,采用该方法估计得到的群时延特性对滤波器进行修正后,信号相关峰与经过滤波器之前的信号相关峰高度吻合,时延估计偏差在0.1 ns之内。