The sample myriad and the weighted myriad filter are normally computed using the batch processing fixed-point algorithm. Since a block of input samples has to be gathered first before the algorithm can perform estimation, significant delay may arise if the block size is large. In this correspondence, we derive the sequential sample myriad and sequential weighted myriad that compute the estimate in real-time by updating the current estimate whenever a new input sample becomes available. Simulation results show that the proposed sequential techniques which have a lower computational complexity, achieve almost the same convergence speed and accuracy as the fixed-point algorithm.
IEEE Transactions on Signal Processing
computational complexity,convergence,filtering theory,batch processing fixed-point algorithm,computational complexity,convergence speed,sequential algorithms,sequential sample myriad filter,sequential weighted myriad filter,$alpha$-stable distribution,fixed-point search,impulsive noise,sample myriad,weighted myriad filter
Convergence (routing),Block size,Mathematical optimization,Algorithm design,Algorithm,Batch processing,Filtering theory,Mathematics,Computational complexity theory