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In this brief, we consider the problem of blind identification in underdetermined instantaneous mixture cases, where there are more sources than sensors. A new blind identification algorithm, which estimates the mixing matrix in a sequential fashion, is proposed. By using the rank-1 detecting device, blind identification is reformulated as a constrained optimization problem. The identification of one column of the mixing matrix hence reduces to an optimization task for which an efficient iterative algorithm is proposed. The identification of the other columns of the mixing matrix is then carried out by a generalized eigenvalue decomposition-based deflation method. The key merit of the proposed deflation method is that it does not suffer from error accumulation. The proposed sequential blind identification algorithm provides more flexibility and better robustness than its simultaneous counterpart. Comparative simulation results demonstrate the superior performance of the proposed algorithm over the simultaneous blind identification algorithm.