基于粒子群优化算法的LFM信号参数估计

LFM Signal Parameter Estimation Based on Particle Swarm Optimization Algorithm

在相同时宽范围内,恒定能量的线性调频信号频谱幅度平方与调频斜率呈反比。基于该特性,将信号解调后幅度平方最大值对应的参考信号调频斜率作为其估计值,进而估计其他参数。但该参数估计算法存在高估计精度与大运算量之间的矛盾,为此,将粒子群优化算法引入信号参数的搜索估计过程中。对搜索区间进行高精度划分以提高参数估计的精度,逐步改变参数值,并利用粒子群优化寻找解调后信号幅度平方的最大值,从而估计出相应参数。仿真实验结果证明,该算法运算量较少,且具有更高的估计精度。

With the same time duration, if Linear Frequency Modulation（LFM） signal possesses constant energy, its spectrum amplitude square is inversely proportional to the frequency modulation slope, Based on the theory, the frequency modulation slope and other parameters can be estimated by searching the parameter that is in accord with the largest amplitude square of demodulated signal. But the contradiction between high estimation precision and large computation amount exists in the algorithm. To solve this problem, the Particle Swarm Optimization（PSO） is introduced into the searching process. Searching area is divided more precisely to improve the parameter estimation precision. PSO is used to search the largest amplitude square to get the estimation results. Simulation experimental results validate that this algorithm consumes fewer computation amount but with better estimation precision.