{'fraction_non_alphanumeric': 0.016652059596844872, 'fraction_numerical': 0.0, 'mean_word_length': 5.563218390804598, '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}}
Discrete- and Continuous-State Trajectory Decoders for Positioning in Wireless Networks

The need for accurate positioning and tracking of mobile sensor nodes arises in many applications. To compute position estimates from raw measurements in positioning systems, Bayesian filtering techniques such as Kalman, histogram, or particle filters are frequently employed. A major disadvantage of these techniques is the fact that they compute only a single position estimate in every timestep and, therefore, do not utilize the available information to the full extent. The work presented in this article improves over this current state of the art by instead estimating full node trajectories and, therefore, the most plausible sequence of positions in every timestep. We present two distinct algorithms—for a discrete and for a continuous state-space—and highlight the particular advantages of each variant. Achievable results are shown by simulation and measurement to be more accurate than the traditional Bayesian filtering approach. The most prominent advantage of much more accurate total track length estimation is particularly emphasized.