{'fraction_non_alphanumeric': 0.016018306636155607, 'fraction_numerical': 0.0, 'mean_word_length': 6.016042780748663, '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}}
Optimization of Indoor Positioning Algorithm Based on Asynchronous TDOA Model

Industrial Internet has become an important driving force for intelligent manufacturing, and indoor positioning technology has provided important location information for the monitoring of industrial production. However, the industrial environment is relatively complicated. There are a large number of industrial noises and industrial equipments, which easily cause interference to wireless positioning signals and affect positioning accuracy. TDOA (Time Difference-of-Arrival) is the current mainstream industrial indoor positioning technology, but it still has some problems of packet loss and floating positional coordinates. To resolve these problems, we propose an optimization scheme based on the asynchronous TDOA positioning algorithm model, which improves the positioning accuracy to some extent. Firstly, the positioning data of the unknown node are classified to determine whether the movement occurs, and further provides more accurate characteristic data for the TDOA prediction. Secondly, the positioning data are judged whether or not prediction processing is required. The experiments show that the optimization scheme can effectively improve the positioning accuracy and solve the problem of positioning data loss.