Hybrid Turbo Equalization Based on Kalman Filter for Underwater Acoustic Communications

The reliability of underwater acoustic (UW A) communications system is degraded by the strong time-varying and multipath effect of UW A channel, making turbo equalization more important and necessary. But the present methods have high computational complexity and poor realtime performance, channel estimation and symbol detection are also independent. In this work, we introduce a Kalman filter into turbo equalization and propose a hybrid turbo equalization based on the Kalman equalizer. The proposed method uses an estimated sparse channel to build the Kalman equalization model, which combines channel estimation process with symbol detection jointly, and the computational complexity is one order of magnitude lower than the minimum mean squared error (MMSE). The Kalman equalization is used for the first turbo iteration, an improved proportionate normalized least mean squares (IPNLMS) adaptive algorithm is used to complete the subsequent iteration. With low complexity and strong channel tracking ability, the Kalman equalization can accelerate the convergence speed of the subsequent adaptive algorithm based turbo equalization and improve the equalization performance. The proposed Kalman based hybrid turbo equalization method was verified by an undersea experiment and showed excellent performance.