基于卡尔曼滤波的时变水声信道估计

By using the cluster-sparse characteristics and temporal correlation of underwater acoustic channel, a time-varying underwater acoustic channel model is constructed and the traditional Kalman filter compressive sensing algorithm is improved under this model. In the proposed method, the channel state response at the previous moment is used to determine the candidate support set at the current moment, and then the state transfer equation for the time-varying underwater acoustic channel is established. The coefficients in the candidate support set are calculated by Kalman filtering, and the error atoms are filtered out by threshold method. The simulation results show that the method can effectively utilize the temporal correlation between underwater acoustic channels to improve the performance of channel estimation, and the cluster characteristics of underwater acoustic channels makes the method robust.