摘要
本文以Costas信号为例提出了基于高斯基集的跳频信号自适应分解法,将跳频信号分解为一组高斯基函数的线性叠加。采用遗传算法估计与信号最匹配的高斯基函数。和其它方法相比,该方法具有很高的时频分辨率且无交叉项干扰,参数估计精度高。仿真结果证明了该方法的有效性,适合于低截获概率波形的低信噪比情况。
The adaptive decomposition of hopping signal based on Gaussian elementary is presented in this paper, with Costas signal represented by a class of Gaussian elementary function. Gaussian elementary function matched well with signal is estimated by Genetic algorithm. Comparison with other methods, this novel method has the characteristic of cross-term interference free and better time and frequency resolution, with high parameter estimation accuracy. Simulation results indicate that it＇s effective even at low SNR of low probability of intercept waveforms.
出处
《电路与系统学报》
CSCD
北大核心
2007年第4期60-63,共4页
Journal of Circuits and Systems
基金
重点实验室基金资助项目(51431050103ZS0106)
关键词
Costas跳频信号
高斯函数
自适应分解
遗传算法
低截获概率
Costas frequency hopping signals
Gauss
adaptive decomposition
genetic algorithm
low probability of intercept
