RF Range Class Estimation via Windowed Signal Segmentation
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Solution Overview
Problem
Existing RF technologies like Wi-Fi, Bluetooth Low Energy, and NFC face limitations in accurately estimating range due to parasitic effects such as multipath interference, making it difficult to determine the proximity of devices in indoor environments.
Innovation Solution
A system that classifies windowed signal measurements into range classes using a RF signal propagation model, with a state estimator and state machine to provide a range state for initiating actions, such as communication with RF signal sources, by processing RSSI values and filtering out interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RF technologies (Wi-Fi, Bluetooth Low Energy, NFC) are used for range estimation, then communication between devices can be established, but measurement precision deteriorates due to parasitic effects such as multipath interference
Solution Approach 1:
The patent segments the range estimation process into multiple discrete range classes (e.g., near, far, very far) rather than attempting continuous measurement. This segmentation allows the system to work around the imprecision caused by multipath interference by categorizing signals into distinct proximity zones based on RSSI thresholds, thereby maintaining measurement reliability despite the parasitic effects of RF technologies.
Solution Approach 2:
The system performs preliminary classification of signal measurements into range classes before making final range determination. By pre-defining threshold values for different range classes and classifying measurements against these thresholds, the system prepares the data in advance to filter out the effects of multipath interference, improving the reliability of subsequent range-based actions.
2Measurement precision
If signal measurements are taken continuously to improve range estimation accuracy, then measurement precision improves, but device complexity increases due to the need for windowing and state estimation processing
Solution Approach 1:
The patent applies segmentation by dividing the continuous stream of signal measurements into discrete windows and classifying each window into specific range classes. This segmentation simplifies the processing complexity by breaking down the continuous measurement problem into manageable discrete categories, making the system more tractable while maintaining precision.
Solution Approach 2:
The system uses partial action by selecting a subset of signal measurements within a time window for classification, rather than processing every single measurement. This approach achieves sufficient measurement precision without the excessive complexity of processing all available data points, balancing accuracy and computational load.
3Productivity
If threshold values are used to classify range classes, then productivity improves by enabling quick range-based decisions, but measurement precision may be lost due to coarse classification
Solution Approach 1:
The patent segments the range estimation into multiple discrete classes (near, far, very far) with specific threshold values for each. This segmentation enables quick productivity by allowing the system to make range-based decisions through simple threshold comparisons rather than complex continuous calculations, while maintaining acceptable precision through the multi-level classification structure.
Solution Approach 2:
The system changes the parameter of range representation from continuous distance values to discrete range class categories. This parameter transformation enables faster processing and decision-making (improving productivity) while the multi-class structure preserves sufficient precision for practical applications by providing granular categorization rather than binary classification.
Data Source
AI summary
Implementations are disclosed for obtaining a range state of a device operating in an indoor environment with radio frequency (RF) signal sources. In some implementations, windowed signal measurements obtained from RF signals transmitted by an RF signal source are classified into range classes that are defined by threshold values obtained from a RF signal propagation model. A range class observation is obtained by selecting a range class among a plurality of range classes based on a percentage of a total number of windowed signal measurements that are associated with the range class. The range class observation is provided as input to a state estimator that estimates a range class that accounts for process and/or measurement noise. The output of the state estimator is provided as input to a state machine.