A sparsity driven approach to cumulant based identification and order determination

The area of blind system identification using Higher-Order-Statistics has gained considerable attention over the last two decades. This paper, motivated by the recent developments in sparse approximations, proposes new algorithms for the blind identification and order determination of sparse systems. The methodology used relies on greedy schemes. In particular, the first algorithm exploits a single step greedy structure, while the second improves performance using a threshold-based selection procedure. Finally, the proposed algorithms are tested on a wide range of randomly generated channels and different output signal lengths.

► We consider the problem of sparse blind system identification via Higher Order Statistics. ► The problem is approached via cumulant based greedy pursuits. ► The suggested algorithms do not pose any conditions on the measurement matrix. ► We address the problem of order determination when the unknown parameter vector is sparse. ► Simulations illustrate that the proposed algorithm(s) perform close to the Genie Aided Estimator.