【摘要】：This paper addresses the problem of linear timeinvariant multi-input-multi-output (MIMO) system identification.Specifically,we focus on identifying the finite impulse responses (FIRs) of a MIMO system.Observing that the FIRs are often approximately sparse,namely containing many nearzero elements,this paper proposes to use the e1 regularized least squares (e1-LS) method as the estimator.Comparing to the traditional identification methods,such as least squares,the e1-LS method exploits the sparse nature of the FIRs,hence brings three advantages:(1) better estimation of the timedelays,(2) better estimation of the effective lengths of the FIRs,and (3) lower requirement of input-output data.Simulation results validate the efficacy of the proposed sparsity-enhanced identification approach.