{'fraction_non_alphanumeric': 0.02029520295202952, 'fraction_numerical': 0.0, 'mean_word_length': 5.458333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fast Adaptive BSS Algorithm for Independent/Dependent Sources

Blind separation for dependent sources and multi-Gaussian sources is a challenging problem. This letter proposes a new adaptive blind source separation algorithm, which is referred to as vector auto-regressive diagonalization (VAR-JD), to deal with this problem. This algorithm estimates demixing matrix by matrix joint JD of a set of matrices containing the estimated VAR coefficients. Compared with traditional adaptive BSS method, which assumes that source signals are mutual independent and no more than one Gaussian source signal exists, the proposed VAR-JD algorithm is not only able to separate independent source signals but also dependent source signals, and there is no demand for the number of Gaussian source signals. Simulation results on the separation of synthetic and communication signals demonstrate the effectiveness of the proposed approach. This latter is important to anti-jamming communications. Moreover, VAR-JD is suitable for determined mixtures and can extend easily to overdetermined mixtures.