基于Kalman滤波的水声混合双向迭代信道均衡算法

水声信道均衡中基于信道估计的均衡方法理论上具有更优的均衡性能，但较高的计算复杂度限制了算法的实际应用。针对这一问题，该文首先基于Kalman滤波和Turbo均衡提出一种迭代Kalman均衡器，实现了基于软符号的迭代信道估计与迭代Kalman均衡，且复杂度较常规方法降低约1个数量级。其次，针对单一均衡算法和单一方向Turbo均衡器存在的误差传递现象，设计了基于迭代Kalman均衡器与改进成比例归一化LMS (IPNLMS)自适应均衡器相结合的混合双向Turbo均衡器，提高了自适应均衡器的收敛速度和均衡性能，并通过双向均衡结构带来的增益改善了符号估计误差传递的现象。理论分析与仿真实验验证了该文算法的有效性。

Hybrid Bi-directional Turbo Equalization for Underwater Acoustic Communications Based on Kalman Filter

In underwater acoustic channel equalization, the channel estimation-based equalization has better performance theoretically, but the high computational complexity limits its practical applications. To solve this problem, an iterative Kalman equalizer based on Kalman filter and Turbo equalization is proposed firstly, which realizes iterative channel estimation and iterative Kalman equalization based on soft symbols generated by the channel decoder, and the complexity is about one order of magnitude lower than that of conventional methods. Secondly, aiming at the error transmission of a single equalization algorithm and single direction Turbo equalizer structure, a hybrid bi-directional Turbo equalizer based on iterative Kalman equalizer and Improved Proportional Normalized LMS (IPNLMS) adaptive equalizer is designed, which improves the convergence speed and equalization performance of the adaptive equalizer, and reduces the error transmission through bi-directional equalization gain. The proposed hybrid bi-directional Turbo equalization for underwater acoustic communications based on the Kalman filter is verified by theoretical analysis and simulation.