Bayesian Inference Model for Object Motion Dynamics from Radio Signals
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Solution Overview
Problem
Inferring an object model and its behavior in real-time with high resolution from wireless signals is challenging due to the need for efficient integration of data from multiple sources, particularly in Low-Power and Lossy Networks (LLNs) where resources are constrained, and translating radio frequency signals into usable object models for recognition and service delivery is not straightforward.
Innovation Solution
A Bayesian inference model is used to predict physical states of an object from signal characteristic data received by antennas, incorporating Newtonian motion dynamics to update the model and enforce consistency with physical laws, allowing for super-resolution inference of motion dynamics and inverse kinematics from single or multi-spectral radio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple radio signals is integrated to infer object model with high resolution, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces Bayesian inference as an intermediary computational framework that mediates between raw radio signal data and object model inference. This intermediary layer processes multiple signal characteristics (phase shift, attenuation, time of arrival) from multiple radio signals and synthesizes them into coherent object state estimates, thereby improving measurement precision while managing the complexity of multi-source data integration through a unified probabilistic framework
2Productivity
If real-time object model inference is performed with high resolution, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the Bayesian inference model structure and motion dynamics constraints before real-time processing. The system pre-establishes the probabilistic framework, likelihood functions, and physical constraints (Newtonian motion dynamics) that will guide real-time inference. This preliminary setup enables efficient real-time updates by avoiding complex model reconstruction during operation, thus improving productivity while controlling energy consumption through optimized computational pathways
Solution Approach 2:
The patent implements dynamics by continuously updating the Bayesian inference model with new radio signal measurements while maintaining consistency with physical motion dynamics. The system dynamically adjusts object state estimates (position, velocity, acceleration) based on incoming signal characteristics, allowing real-time adaptation to changing object motion while enforcing physical constraints to maintain inference accuracy without requiring excessive computational resources
3Reliability
If Newtonian motion dynamics are enforced on predicted physical states, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent employs feedback by continuously comparing predicted object states with actual radio signal measurements and using the discrepancies to update the Bayesian inference model. The system enforces Newtonian motion dynamics as feedback constraints, where predicted accelerations, velocities, and positions are continuously validated against physical laws. This feedback mechanism improves reliability by ensuring physical consistency while managing complexity through iterative model updates that leverage previous inference results
Data Source
AI summary
In one embodiment, a service receives signal characteristic data indicative of characteristics of wireless signals received by one or more antennas located in a particular area. The service uses the received signal characteristic data as input to a Bayesian inference model to predict physical states of an object located in the particular area. A physical state of the object is indicative of at least one of: a mass, a velocity, an acceleration, a surface area, or a location of the object. The service updates the Bayesian inference model based in part on the predicted state of the object and a change in the received signal characteristic data and based in part by enforcing Newtonian motion dynamics on the predicted physical states.