{'fraction_non_alphanumeric': 0.02119700748129676, 'fraction_numerical': 0.0012468827930174563, 'mean_word_length': 4.948148148148148, '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}}
An Improved Distributed Consensus Kalman Filter Design Approach

This paper proposes an improved design approach for distributed consensus Kalman filtering (DCKF). We provide an improved consensus gain factor compared to the sub-optimal design proposed in [1]. This factor is derived from an LMI appearing in the stability analysis of the DCKF and can be computed using semi-definite programming. We also propose a decentralized consensus gain that can be computed by each agent in the sensor network, and depends only on local properties of the network, i.e., the number of neighbors of each sensor. We show in simulation that this approach holds even for networks with time varying communication regime. Our results are compared to other existing solutions in the literature with a numerical example.