摘要
计算复杂度高是制约时频原子分解算法在信号处理中应用的主要问题,文中提出了一种基于粒子群算法(PSO)的时频原子分解快速算法,该方法在过完备Chirp原子库的基础上,采用时频原子分解算法分解信号,并通过PSO算法降低时频原子分解算法搜索过程的计算复杂度,提高信号处理效率。对雷达辐射源信号的仿真实验结果表明,该方法与传统的时频原子分解算法相比计算速度大幅提高,且用在数量上比Gabor少的Chirp原子刻画出信号的主要时频特征。
The main problem of time frequency atom decomposition （TFAD） is its high computational complexity. This issue restricts the application of TFAD to radar emitter signal processing. A fast TFAD algorithm based on particle swarm optimization algorithm （PSO） is presented in this paper. The time-frequency atom libraries were built by chirp atoms which can match best the orig- inal signal, and PSO was applied to reduce searching time. Experiments conducted on radar emitter signals show that the proposed method not only decreases the computational burden but also obtains time-frequency concentration of reconstructed signal, as compared to the traditional TFAD algorithms.
出处
《现代雷达》
CSCD
北大核心
2009年第10期64-69,共6页
Modern Radar
基金
国家自然科学基金资助项目(60702026)
关键词
雷达辐射源信号
时频原子分解算法
粒子群算法
Chirp原子
radar emitter signal
frequency atom decomposition algorithm
particle swarm optimization algorithm
Chirp atom
