摘要
针对基于时频分布的跳频信号参数估计存在信噪比阈值的问题,提出了一种参数估计的算法。该算法首先基于粒子群优化,利用匹配追踪算法对信号进行自适应分解,获取匹配原子;然后基于原子参数对跳频信号进行参数估计。仿真结果表明,该方法不仅解决了匹配追踪算法运算量巨大的问题,而且克服了跳频信号各参数估计误差的相互影响,同时在低信噪比下参数估计的方差也比较小,更加适应于实际的电子战环境。
A parameter estimation algorithm for frequency-hopping（FH） signal is proposed which the threshold of Signal to Noise Ratio（SNR） existed in parameter estimation based on time-frequency distribution is removed.First of all,the new algorithm based on particle swarm optimization uses matching pursuit algorithm for adaptive signal decomposition to obtain matched atoms.Hereafter,parameter estimation algorithm for FH signal is designed based on atomic parameters.Simulation resuits indicate that this method not only solve enormous computing burden in matching pursuit,but also overcome the mutual influence among all the parameter estimation errors.At the same time,the estimation variance is relative small in low SNR which make it more suitable for the actual electric warfare environment than using time-frequency distribution.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第27期142-144,共3页
Computer Engineering and Applications
关键词
跳频信号
匹配追踪
粒子群优化
参数估计
frequency-hopping signal
matching pursuit
particle swarm optimization
parameter estimation
