{'fraction_non_alphanumeric': 0.012021857923497269, 'fraction_numerical': 0.0, 'mean_word_length': 4.9869281045751634, '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}}
Radio Source Localization Using Received Signal Strength in a Multipath Environment

We consider the problem of localizing a radio emitter in a wireless network using RSS measured by a set of known network nodes in a multipath environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is nontrivial in the presence of multipath. We propose a HMM model within a Bayesian learning framework for processing RSS data in the localization process to deal with RSS fluctuations induced by multipath interference. To address the uncertainty of emitter dynamics, a semi-Markov model is also adopted to model the duration time of the emitter sojourn in a state. We compare the performance of the HMM methods, HsMM methods and RSS fingerprinting methods via a real experiment of a two-region emitter localization problem and Monte Carlo simulations using ray-tracing software.