A Novel Underdetermined Blind Source Separation Algorithm of
Frequency-hopping Signals via Time-frequency Analysis
Abstract
To address the significant performance degradation of conventional
underdetermined blind source separation algorithms for frequency-hopping
(FH) signals under time-frequency (TF) overlapping conditions, this
paper presents a novel three-stage scheme based on the TF distribution
of FH signals. In the first stage, key parameters of the FH signal are
estimated using a TF binary graph. In the second step, the initial
mixing matrix is estimated for non-overlapping and overlapping carrier
frequencies employing density peaks clustering and tensor decomposition
methods, respectively. In the third step, the final mixing matrix,
directions of arrival (DOA), and source signals are estimated using the
expectation-maximization algorithm within the nonnegative matrix
factorization model. Finally, different segments of the FH signals are
spliced together based on the DOAs of different source signals.
Comprehensive experimental results demonstrate the superior performance
of the proposed algorithm compared to state-of-the-art algorithms. Even
at a signal-to-noise ratio of 5 dB, the correlation coefficient of the
estimated source signals can still reach 0.91.