{'fraction_non_alphanumeric': 0.030208333333333334, 'fraction_numerical': 0.0, 'mean_word_length': 5.240259740259741, '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}}
Robust adaptive signal-level fusion for enhanced target detection

In a series of prior papers Kadar et al have designed, developed and experimentally evaluated a family of centralized detection algorithms for multi-frequency radars operating in various degrees of clutter. Previously, two centralized fusion algorithms, called non-coherent integration (NCI) and T-squared (TSQ), followed by adaptive constant false alarm (ACFAR) post-processing were evaluated. In this paper, an optimum adaptive CFAR version of the T- squared algorithm, called ATSQ, is developed. The performance of ATSQ is compared with the NCI and TSQ algorithms using measured data in clutter. The fusion performance comparisons are presented in terms of receiver operating characteristics (ROC). It is shown that the new ATSQ algorithm is robust to changes of the background clutter. Its ROC performance is near the optimum achievable, and is significantly better than either NCI or TSQ.