Super-resolution Object Mass Estimation via Multi-spectral Signal Analysis
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Solution Overview
Problem
Inferring an object's model and behavior in real-time with high resolution and efficiency is challenging, especially in wireless networks where data integration from multiple sources is complex, and slight variations in signal phase shifts can indicate object motion, necessitating advanced signal processing techniques.
Innovation Solution
The implementation of a Bayesian inference framework that utilizes multi-spectral signal data to estimate object motion dynamics and inverse kinematics, combining physics-based models with signal processing to infer object characteristics, such as direction and mass, by analyzing changes in signal phases and gains across multiple antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced signal processing techniques are used to detect slight phase shift variations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex signal processing task into distinct functional modules: phase extraction module, gain calculation module, motion detection module, and mass estimation module. Each module handles a specific aspect of the signal analysis, making the overall system more manageable and implementable while maintaining high detection precision for phase shift variations
Solution Approach 2:
The patent introduces intermediate calculation steps as mediators: extracting phase information as an intermediate parameter from the complex signal, calculating gain as another intermediate parameter, and using these intermediates to derive motion and mass. This intermediary approach simplifies the direct complexity of detecting slight phase variations by breaking it into manageable computational steps
2Productivity
If real-time inference is achieved, then productivity is improved, but measurement precision may deteriorate due to processing speed requirements
Solution Approach 1:
The patent performs preliminary actions by pre-defining the relationship between phase/gain changes and motion parameters, and pre-establishing the mathematical models connecting signal variations to object characteristics. This allows real-time inference to proceed through straightforward calculations rather than complex real-time optimization, maintaining both speed and precision
Solution Approach 2:
The patent implements a feedback mechanism where the inferred motion and mass information is continuously updated as new signal data arrives. The system uses the ratio of gain changes to phase changes as a feedback parameter that stabilizes the mass estimation, allowing real-time processing while maintaining precision through continuous refinement of the estimates
Data Source
AI summary
In one embodiment, a service receives signal data indicative of phases and gains associated with wireless signals received by one or more antennas located in a particular area. The service determines, from the received signal data, changes in the phases and gains associated with the wireless signals. The service estimates a direction of motion of one or more objects located in the particular area, based on the determined changes in the gains associated with the wireless signals. The service estimates a total mass of the one or more objects located in the particular area based on a ratio of the determined changes in the gains associated with the wireless signals over the determined changes in the phases associated with the wireless signals.