EM-ACO算法及其在多重超声回波参数估计中的应用

EM-ACO algorithm and its application to parameters estimate of multiple ultrasonic echoes

针对EM算法在估计多重超声回波参数时存在收敛速度慢和迭代结果强烈依赖于初始值的缺点,将蚁群算法应用到多重超声回波参数估计的EM算法中,提出一种新的多重超声回波参数估计算法——EM-ACO算法.该算法结合EM算法和蚁群算法的优点,不仅可以改善EM算法估计多重超声回波参数时估计结果强烈依赖于初始值的缺点,有效提高EM算法的收敛速度,而且可以获得更高的参数估计精度.根据超声回波的高斯回波模型,应用EM-ACO算法,在不同的信噪比条件下,对多重超声回波的参数向量组进行估计.仿真结果表明:EM-ACO算法能在各种不同的初始值条件下,以较少的迭代次数估计出多重超声回波的参数向量组,并且具有较高的估计精度.

Aiming at the defects that the convergence speed is so slow and the iterative results depend on the initial values seriously in the application of EM algorithm estimated the parameters of multiple ultrasonic echoes,a new method for parameters estimation of multiple ultrasonic echoes：EM-ACO algorithm is proposed,which combines the advantages of ant colony algorithm and EM algorithm.The new algorithm can not only obtain the good results at different initial guesses and improves the convergence speed of EM algorithm significantly,but also achieve a higher precision.According to Gaussian Echoes model,this newalgorithm is applied to the parameters estimation of multiple ultrasonic echoes for different signal to noise ratio（SNRs）.The simulation results show that EM-ACO algorithm can successfully estimate the parameters of multiple ultrasonic echoes with fewer iterations and has a higher precision in conditions of all sorts of different initial values.