Blind paraunitary equalization
Signal Processing
Blind paraunitary equalization
Signal Processing
Non-cancellation multistage kurtosis maximization with prewhitening for blind source separation
EURASIP Journal on Advances in Signal Processing
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
A quadratic programming approach to blind equalization and signal separation
IEEE Transactions on Signal Processing
Noncircularity-rate maximization: a new approach to adaptive blind beamforming
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
IEEE Transactions on Neural Networks
Sequential extraction algorithm for BSS without error accumulation
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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Blind source separation has been the subject of extensive research. In particular, blind antenna beamforming is an effective signal separation technique for communication systems to combat co-channel interference. Among many potential candidate approaches, the simple constant modulus algorithm (CMA) has been widely studied and used in practice. The CMA is designed to capture and separate signals with negative kurtosis. However, when some signals have positive kurtoses, the CMA is unable to capture and separate these sources. We show that the kurtosis maximum algorithm (KMA) can capture signals with both the positive and negative kurtoses. Its global convergence proof is presented for noiseless systems with multiple signals sources and for systems with a single source and zero-kurtosis (such as Gaussian) additive noise