摘要
针对基于时频分布的参数估计存在信噪比阈值和低信噪比下方差大的问题,提出了一种基于多峰优化粒子群算法的跳频信号参数估计新算法。该算法首先将跳频信号分解为时频原子的线性组合,然后由匹配原子获取跳频信号的参数估计。仿真结果表明,基于改进的物种形成粒子群算法能够搜索到与跳频信号分量相匹配的原子,与平滑伪魏格纳分布相比,提出的参数估计算法在低信噪比下具有较小的估计方差,更加适宜于电子战的实际应用。
To aim at parameter estimation of signal to noise ratio （SNR） and high variance in low SNR based on time frequency distribution, this paper proposed a novel algorithm based on muhimodal particle swarm optimization （PSO）. First decomposed frequency hopping signal to linear combination of time frequency atoms, and then obtained its parameter by matched atomic parameter. Simulation showed that PSO using specification algorithm could find all the atoms that matched with frequency hopping components. Compared with smoothed pseudo Wigner-Ville distribution, the designed algorithm has lower variance and is more suitable for the actual application of electronic countermeasure.
出处
《计算机应用研究》
CSCD
北大核心
2010年第2期512-514,共3页
Application Research of Computers
基金
河南省科技计划资助项目(D2008090217261501)
信阳师范学院高层次人才科研启动基金资助项目(90217)
关键词
跳频信号
参数估计
粒子群
多峰优化
frequency hopping signal
parameter estimation
particle swarm
multimodal optimization
