{'fraction_non_alphanumeric': 0.02980392156862745, 'fraction_numerical': 0.007058823529411765, 'mean_word_length': 5.348258706467662, '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}}
Experimental evaluation of distributed identity fusion

The fusion of identity estimates continues to be an active area of research in the multi-sensor data fusion community. A good review of the issues in identity fusion can be found in [1], which also includes a comparison of centralised Bayesian and Dempster-Shafer techniques. This paper presents some experimental results obtained from the comparison of distributed Bayesian and non-Bayesian identity fusion, using a real-time data fusion demonstrator/test-bed. The Bayesian approach uses an implementation of a fully decentralised probabilistic combination rule [2], while the non-Bayesian method is based on the use of Fuzzy Relational Inference Logic (an implementation of Dempster-Schafer evidential reasoning) with different combination rules for the cases of consistent or conflicting evidence [3, 4, 5, 6, 7]. In both cases we combine identity estimates from a network of distributed sensing nodes (TV cameras). The estimates of identity, derived from simple image processing, are associated over time to provide increased reliability in target recognition. In both methods the temporal association is mediated through the use of a multi-target tracking system built around a decentralised Kairnan filter [8, 9].