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AssigneeTrakpoint Solutions, Inc.
InventorsJon Siann, Christopher Williams
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Aspects of the present invention provide systems and methods for distributed signal processing of indoor localization signals wherein statistical algorithms and machine learning are used in place of a fingerprint map. The disclosure relates to calculation of angle and distance based on measurements of an indoor localization signal, followed by energy-efficient distribution of signal processing. Local signal processing is performed using any of multiple eigen structure algorithms or a linear probabilistic inference, before cloud-based signal processing is performed using a nonlinear probabilistic inference and machine learning that's been trained with historical data transmitted by the base stations and time-of-day location patterns. Without having to generate and constantly update an energy-exorbitant fingerprint map, the disclosed system reduces localization error to merely 50 cm with 95% probability without compromising energy-efficiency to rival the accuracy of indoor localization systems that utilize fingerprinting.
An energy-efficient method (100) of distributed signal processing for radiofrequency (RF) localization, wherein statistical algorithms and machine learning are used in place of a fingerprint map, the method comprising:
An energy-efficient system of distributed signal processing for radiofrequency (RF) localization, wherein statistical algorithms and machine learning are used in place of a fingerprint map, the system comprising:
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