基于优化匹配追踪稀疏分解的微弱超声回波提取

为提高低信噪比环境中微弱超声回波信号的提取性能，提出优化的匹配追踪(Matching Pursuit,MP)稀疏分解的超声回波提取算法。该算法将具有连续空间搜索能力的粒子群优化(Particle Swarm Optimization,PSO)算法引入到MP稀疏分解中，以缓解原子集的遍历有限性需求与超完备性之间的矛盾，通过改进粒子群算法的参数自适应设置及MP算法的目标函数和重构函数，实现自适应的PSO-MP稀疏分解算法，并建立了连续伽柏(Gabor)原子集，提高了最优原子与不同参数超声回波信号的匹配程度，最后由最优原子集通过重构函数对回波信号进行重构，实现对回波的降噪和准确提取。实验结果表明，该算法显著降低了计算量，效果优于已有小波阈值等算法且具有较好鲁棒性。

Weak ultrasonic echo extraction based on optimized matching pursuit sparse decomposition

To improve the extraction performance of weak ultrasound echo signals in low SNR environment, an optimized matching pursuit (MP) sparse decomposition algorithm is proposed. An adaptive particle swarm optimization (PSO) algorithm with continuous space search ability is introduced into the MP sparse decomposition to alleviate the contradiction between the ergodic limitation requirement and over-completeness of MP atomic set. By improving the parameter-adaptive setting of the PSO algorithm and the objective function and reconstruction function of the MP algorithm, the adaptive MP sparse decomposition algorithm improved by PSO optimization is realized. And then, a continuous over complete Gabor atom set is established, which improves the matching degree between the optimal atom and the sound signal in the evolution process. Finally, the echo signal is reconstructed by the optimal atom through the reconstruction function to realize noise reduction and echo accurate extraction. The experimental results show that the proposed algorithm significantly reduces the amount of computation, which is better than that of the existing wavelet thresholds method and others, and achieves better robustness.