Adaptive location technology handoffs with asset trackers
OwnerAlberto Vidal, Manu Rao +1inventors
InventorsALBERTO VIDAL, MANU RAO, PRASAD PANCHALAN
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OwnerAlberto Vidal, Manu Rao +1inventors
InventorsALBERTO VIDAL, MANU RAO, PRASAD PANCHALAN
Estimating the term…
Systems and methods are disclosed for determining asset tracker location using multimodal fingerprints and adaptive machine learning techniques. A process obtains data comprising sequences of events, location sources, time values, and sensor measurements to generate multimodal fingerprints. These fingerprints are fused to define “liquid” boundaries with improved certainty. Machine learning models are trained and updated using deep learning architectures that classify and predict asset tracker location based on multimodal fingerprints, motion, and environmental data. Adaptive handoff processes dynamically switch among GPS, Wi-Fi, UWB, dead-reckoning, or other location technologies depending on detected motion changes and operational context, thereby conserving power and optimizing accuracy. An integrated system includes processors, force sensors, radios, inertial sensors, and edge-based AI/ML modules that continuously update location models locally and in conjunction with server-based systems for improved geospatial tracking performance across diverse environments.
A computer system for implementing an adaptive location technology handoff with a plurality of asset trackers comprising:
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