中文摘要：

针对盲均衡算法收敛速度较慢的问题，提出一种结合改进支持向量机和常数模算法的水声信道盲均衡算法。该算法首先利用具有优异小样本学习能力的支持向量机进行盲均衡器权系数初始化，在完成初始化后切换至运算量较小的常数模算法。考虑到支持向量机本身非自适应运算的限制，在时变水声信道条件下利用经典支持向量机获得的均衡器初始权向量与切换后的信道仍然存在失配。因此，本文导出时变条件下的改进支持向量机用于盲均衡器初始化，改善算法切换时的权系数失配，并结合分数间隔结构和内嵌数字锁相环进一步提高盲均衡算法性能。仿真和湖试实验结果表明：在时变水声信道条件下，本文算法的收敛性能优于经典支持向量机盲均衡算法。

英文摘要：

To improve the converging performance of blind equalizer, a novel blind equalization algorithm incorporating the modified support vector machine （SVM） and constant modulus algorithm （CMA） is proposed. The proposed algorithm firstly adopts the support vector machine （SVM） which possesses excellent generalization ability under small training samples to initialize the coefficients of blind equalizer with a short training sequence. After the SVM ini- tialization, it switches to constant modulus algorithm （CMA） to alleviate the computational burden. However, under time-varying underwater acoustic （UWA） channels, the initial coefficients obtained by SVM may still contain mismatch with the channel after algorithm switching, as the classic SVM algorithm is inherently nonadaptive. To deal with this problem, a modified SVM is formulated to initialize the coefficients of blind equalizer at the presence of non-stationary channels to facilitate smooth algorithm switch. In addition, fractional spaced structure （FSE） as well as embedded digital phase lock loop （PLL） is also adopted to further improve the performance of blind equalization. Experimental results performed with numerical simulation and lake-trial data are provided, demonstrating the improvement of the proposed algorithm in convergence rate under time-varying channels.