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An auxiliary model based multi-innovation recursive least squares estimation algorithms is proposed in this paper. The unknown variables in the information vector can be estimated by using the auxiliary model. The proposed recursive least squares algorithm uses not only the current innovation but also the past innovations at each recursion and thus the parameter estimation accuracy can be improved. Finally, the simulation results indicate that the proposed algorithm has good performances.