Reduced-rank blind adaptive frequency-shift filtering for signal extraction

In this paper, we first illustrate that a blind adaptive frequency-shift (BA-FRESH) filter can be represented as a generalized sidelobe canceler (GSC). Since the computational power of the BA-FRESH filter is quite high, a reduced-rank implementation is thus proposed and achieved by using the eigen-subspace method. To avoid under-representation, a rule for choosing the rank/dimension of the signal subspace is introduced by looking at the eigenvalue spread of the signal covariance matrix. The proposed PCA-based reduced-rank BA-FRESH filter not only has a lower computational complexity, but is also more efficient in signal extraction when compared with the conventional, CSP-based and Krylov subspace-based BA-FRESH filters. The performance of this new method in reducing the spectrally overlapped interference of BPSK signals has been examined rigorously.