RF Sensing Node Calibration via Metadata Analysis
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Solution Overview
Problem
Existing radio frequency (RF) sensing systems for smart light modules lack an efficient calibration process, leading to reduced accuracy and increased complexity due to the use of unsuitable nodes for motion sensing, which affects the overall performance and reliability of the system.
Innovation Solution
A RF sensing system with a calibration mode controller that analyzes metadata from RF nodes, selects optimal nodes for motion sensing, and provides user instructions through a user device for activities to improve calibration, allowing for the deactivation of non-contributory nodes and refinement of the sensing function, thereby enhancing accuracy and reducing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the density of RF sensing nodes is increased to improve sensing accuracy, then the reliability and accuracy of the sensing network will increase, but the complexity of the entire system will increase
Solution Approach 1:
The system segments the RF sensing nodes into different functional groups: anchor nodes with known positions and unknown nodes to be localized. This segmentation allows the system to process nodes in a structured manner, reducing the complexity of managing dense node distributions while maintaining high sensing accuracy through organized signal processing and position determination protocols.
Solution Approach 2:
Anchor nodes serve as intermediaries between the unknown nodes and the central coordination system. These anchor nodes with known positions facilitate the localization process by providing reference points for signal transmission and reception, enabling accurate position determination of unknown nodes without requiring direct communication between all node pairs, thus reducing system complexity.
2Reliability
If all RF sensing nodes are used for motion sensing to improve reliability, then the sensing coverage and reliability will increase, but the calibration time and processing overhead will increase
Solution Approach 1:
The system performs preliminary position determination and node classification before the actual motion sensing calibration process. By pre-establishing which nodes are anchor nodes with known positions and identifying unknown nodes, the system prepares the sensing network in advance, allowing the calibration phase to focus only on determining positions of unknown nodes rather than calibrating all nodes from scratch, thus reducing calibration time while maintaining reliability.
3Measurement precision
If metadata analysis is performed on all sensing nodes to select optimal nodes, then the sensing accuracy will improve, but the processing time and computational load will increase
Solution Approach 1:
The system extracts and utilizes metadata from RF sensing nodes to identify and separate anchor nodes with known positions from unknown nodes. By extracting this positional information metadata, the system can focus calibration and processing efforts only on unknown nodes, improving sensing accuracy through selective processing while enhancing calibration efficiency by avoiding redundant analysis of already-known anchor node positions.
Data Source
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AI summary
A radio frequency (RF) sensing system (10) is provided and comprises RF sensing nodes (200) having a RF node transceiver (210), and a node controller (220) with a RF sensing controller (223) sensing RF signals from other RF nodes (200). A controller (100) comprises a mode controller (120) having a calibration mode controller (122). In a calibration mode the calibration mode controller (122) analyzes metadata from the RF sensing nodes (200) to select a number of RF sensing nodes (200) from among the RF sensing nodes for performing RF sensing based on the analyzed metadata. The calibration mode controller (122) is outputs instructions to a user device (300) to instruct a user to perform activities or movements to enable a calibration of the RF sensing nodes (200) by analyzing the RF signals detected by the RF sensing nodes (200) during the activities or the movements of the user.