Trimmed affine projection algorithms

The least trimmed squares (LTS) estimator is a robust estimator as it can avoid undue influence from outliers. The exact solution of the LTS estimation is however hard to And and if the number of data is large then the method is unfeasible. In this work, we apply the LTS criterion to adaptive filtering and develop the trimmed affine projection algorithm (TAPA) and kernel trimmed affine projection algorithm (KTAPA). The proposed adaptive algorithms are very robust to outliers and have low computational complexity. Simulation results conflrm their excellent and robust performance.