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A sequential M-estimation algorithm is proposed as an alternative to sequential least squares (LS). Being an approximation to exact M-estimation, the proposed technique is robust to non-Gaussian noise and outperforms sequential LS. A low-cost technique is introduced for initialization. We also show that sequential LS is a special case of the proposed algorithm.