Abstract
Proposed is an L 0 norm constraint set-membership affine projection algorithm with coefficient vector reuse, which is derived by minimising a differentiable cost function that utilises the L 0 norm of the updated weight vector as well as the sum of the squared Euclidean norms of the differences between the updated weight vector and past weight vectors. In addition, a power estimate method is introduced to eliminate the effect of the fluctuation of the output error on the step size. Simulations on sparse system identifications demonstrate that the proposed algorithm achieves a lower steady-state misalignment than the existing algorithms in a high background noise environment.
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