Bluetooth Channel Sounding with Adaptive Filtering for Indoor Ranging
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Solution Overview
Problem
Existing phase-based ranging solutions using narrow-band radios struggle to achieve centimeter accuracy in indoor environments due to dynamic changes in signal propagation paths, reflections, diffractions, and interference from other wireless devices, leading to inaccurate distance measurements.
Innovation Solution
A Kalman-filter assisted Bluetooth channel sounding (CS) processing pipeline that includes scene identification, adaptive bandpass filtering, and minimum variance distortion-less response (MVDR) algorithms to process phase measurements, reducing noise and improving accuracy by adapting to environmental changes and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase-based ranging using unmodulated pulses is used, then distance measurement capability is provided, but measurement precision deteriorates in indoor environments due to multipath interference and signal propagation changes
Solution Approach 1:
The patent implements adaptive filtering that dynamically adjusts filter coefficients based on real-time signal conditions. The system continuously monitors signal quality metrics and adapts the filtering parameters to optimize performance in changing indoor environments, thereby maintaining measurement precision despite multipath interference variations.
Solution Approach 2:
The patent introduces an intermediate processing stage that includes adaptive filtering and signal quality assessment between the raw phase measurement and final distance calculation. This intermediary processing extracts reliable distance information by filtering out multipath components and weighting measurements based on their quality, thus improving overall measurement accuracy.
2Measurement precision
If adaptive filtering and scene identification are implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the signal processing into distinct functional modules: scene identification module, adaptive filtering module, and range estimation module. Each module performs a specific function and can be independently optimized or disabled based on application requirements, managing complexity through functional segmentation while maintaining high measurement precision.
Solution Approach 2:
The adaptive filtering system automatically adjusts its parameters based on real-time signal quality assessment without requiring manual calibration or external intervention. The system self-adapts to different indoor scenarios (line-of-sight, non-line-of-sight, reflective environments) by monitoring signal characteristics and adjusting filter coefficients accordingly, reducing operational complexity.
Data Source
AI summary
Techniques described here introduce confidence-based adaptive filtering technique in phase-based ranging (PBR). An initiator of the PBR may adaptively adjust a bandwidth of a bandpass filter used to filter I/Q measurement data from the PBR based on the confidence level feedback from a Kalman Filter covariance matrix representing uncertainty in the range estimates or based on other variance of the range estimates. In one aspect, post-processing by the initiator includes filtering I/Q measurement data using an adaptive bandpass filter to generate filtered data. The filter setting of the adaptive bandpass filter is adaptive to a confidence level in estimating a range between the initiator and a reflector. The post-processing determines a range estimate between the initiator and the reflector based on the filtered data. The post-processing determines a confidence level in the range estimate. The post-processing adjusts the filter setting such as the passband bandwidth based on the confidence level.