Blind MIMO System Identification
This work has been supported by the National Science Foundation ( NSF) and also by the Office of Naval Research (ONR)
The goal of blind system blind identification is to identify an unknown system driven by unobservable inputs, based on the system outputs, and subsequently use the system estimate to recover the input signals (sources). Blind identification of a Multiple-Input Multiple-Output (MIMO) system is of great importance in many applications, since many problems can be formulated as MIMO identification problems. For example, in speech enhancement in the presence of competing speakers, an array of microphones is used to obtain multiple recordings, based on which the signal of interest can be estimated. The microphone outputs can be viewed as the outputs of a MIMO system representing the acoustic environment. MIMO models arise frequently in digital multiuser/multi-access communications systems, digital radio with diversity, multisensor sonar/radar systems. They also arise in biomedical measurements, when recordings of a distributed array of sensors, placed on the skin, are used to pick up signals originating from inside the body.
Our work considers the convolutive MIMO problem.
Most of the existing approaches for MIMO system blind identification operate in the time domain. They require a priori knowledge of the order of the mixing system while are sensitive to order mismatch, and their complexity increases rapidly with channel length. We have developed a frequency domain framework that does not require system length information. Common problems with frequency domain approaches are frequency dependent scaling and permutation ambiguities. By exploiting redundancy in the higher-order spectra domain, the proposed framework can effectively deal with the aforementioned ambiguities.
Sample of our work on this problem can be found in the adjacent Feature section under various Publications
K.I. Diamantaras, A.P. Petropulu and B. Chen, "Blind Two-Input-Two-Output FIR Channel Identification Based On Frequency Domain Second-Order Statistics," IEEE Transactions on Signal Processing, vol. 48(2), pp. 534-542, February 2000. MATLAB Code for this approach can be found HERE
Binning Chen and Athina P. Petropulu, "Frequency Domain Blind MIMO System Identification Based On Second- And Higher-Order Statistics," IEEE Transactions on Signal Processing, vol. 49(8), pp. 1677-1688, August 2001. Abstract.MATLAB Code for HOS based MIMO Identification:
I. Bradaric, A.P. Petropulu and K.I. Diamantaras, "On Blind Identifiability of FIR-MIMO Systems with Cyclostationary Inputs Using Second Order Statistics," IEEE Transactions on Signal Processing, vol. 51, no. 2, pp. 434-441, February 2003.
I. Bradaric, A.P. Petropulu and K.I. Diamantaras, "Blind MIMO FIR Channel Identification Based on Second-Order Spectra Correlations," IEEE Transactions on Signal Processing, vol. 51, no. 6, pp. 1668-1674, June 2003.
B. Chen, A.P. Petropulu and L. De Lathauwer, "Blind Identification of Convolutive MIMO System with 3 Sources and 2 Sensors," EURASIP Journal on Applied Signal Processing, Special Issue on Space-Time Coding and Its Applications, 2002:5 (2002) 487-496, May 2002. MATLAB Code for the Tensor based 3-Input 2-Output MIMO method
M. Castella, J.C. Pesquet and A.P. Petropulu, “Family of Frequency and Time-Domain Contrasts for Blind Separation of Convolutive Mixtures of Temporally Dependent Signals,” IEEE Trans. on Signal Processing, vol. 53, issue 1, pp. 107-120, January 2005.
S. Yatawatta, A.P. Petropulu and C. J. Graff, “Energy Efficient Channel Estimation in MIMO Systems," EURASIP Journal on Wireless Communications and Networking, March 2006.
T. Acar, Y. Yu and A.P. Petropulu, “Blind MIMO system estimation based on PARAFAC decomposition of tensors formed based HOS of the system output,” IEEE Trans. on Signal Processing, Vol. 54, Issue 11, pp. 4156 - 4168, November 2006.
Y. Yu and A.P. Petropulu, “PARAFAC
Based Blind Estimation Of Possibly Under-determined Convolutive
MIMO Systems,” IEEE Trans. on Signal Processing, accepted in 2007.
MATLAB Code for this approach can be found here