Low complexity sparse underwater acoustic channel estimation method with fast convergence speed
In view of the sparsity of underwater acoustic channel(UAC),this paper proposes a sparse UAC estimation method with lower complexity based on set membership L1-norm constrained improved proportion-ate affine projection algorithm(SM-L1-IPAPA).Firstly,we borrow a robust SM(RSM)filtering idea to set a dynamic error threshold,which accelerates the convergence speed without increasing steady-state error.Then the channel update equation is optimized by using the historical proportionate matrix,so that some process matrices can be updated by recursive method,which reduces computational complexity from the perspective of matrix operation.The results of lake trial and sea trial data processing show that this method has faster convergence speed and slight lower steady-state error than other sparse UAC estimation methods when facing both weak and strong time-varying channels,and it can reduce the computational complexity from two aspects of matrix operation and iteration times.
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