{'fraction_non_alphanumeric': 0.021592442645074223, 'fraction_numerical': 0.004048582995951417, 'mean_word_length': 5.183333333333334, '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}}
SignalSource Classification Based on Independency Analysis of Doppler Signals

Doppler radar can detect an object movement from a change of ratio reflection. In many cases, a radar with a single receiver could receive multiple reflections as a single mixed signal because of a variety of moving objects. Therefore, the source separation techniques are required to detect each object. In this paper, we extract the features from a mixed radar signal composed of two different period using Fourier analysis and independent component analysis (ICA). Then, we recognize the number of signal sources by using support vector machine (SVM) classifier. Performance evaluation results show that our approach can achieve a detection accuracy of 96.2%.