Bias Estimation for TOA Wireless Geolocation
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Solution Overview
Problem
Current methods for bias estimation and correction in time-of-arrival (TOA) based wireless geolocation systems, particularly in urban and indoor environments, face challenges due to multipath propagation and non-line-of-sight (NLOS) issues, leading to inaccurate range and angle estimation, which degrades localization accuracy.
Innovation Solution
A method that recursively estimates bias in distance measurements between a mobile device and base stations by dynamically calculating differential bias changes, allowing for accurate and robust localization without assuming zero bias in line-of-sight (LOS) conditions or requiring LOS/NLOS identification, and corrects distance estimations using a sliding window and simple algebraic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLOS bias mitigation techniques are used, then localization accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent employs simple algebraic operations and a sliding window approach that require minimal computational resources, effectively using low-complexity processing steps that can be executed rapidly on mobile devices without powerful processors
Solution Approach 2:
The system performs self-calibration by automatically estimating and correcting biases using only the mobile device's own measurements and a sliding window of historical data, without requiring external calibration infrastructure or complex preprocessing
2Measurement precision
If statistical bias correction techniques are used, then measurement precision is improved, but reliability decreases due to assumptions that do not hold in dynamic environments
Solution Approach 1:
The patent uses a sliding window that dynamically adapts to changing environments by continuously updating the bias estimate based on recent measurements, allowing the system to track time-varying biases without requiring a priori statistical characterizations
Solution Approach 2:
The system incorporates feedback through the sliding window mechanism, where past bias estimates inform current corrections, creating a recursive estimation process that adapts to environmental changes while maintaining consistency
3Measurement precision
If NLOS identification and mitigation is used, then localization accuracy is improved, but device complexity increases due to requirement for LOS/NLOS identification
Solution Approach 1:
The patent extracts only the essential bias correction functionality from complex NLOS identification systems, using a simplified sliding window approach that directly estimates biases without requiring separate LOS/NLOS classification stages
Solution Approach 2:
The sliding window bias estimation mechanism serves multiple functions simultaneously: it filters measurements, estimates biases, and adapts to environmental changes, eliminating the need for separate specialized modules for each function
Data Source
AI summary
This invention relates to methods and devices for bias estimation and correction, particularly for time-of-arrival (TOA) based wireless geolocation systems. Multipath and non-line-of-sight (NLOS) biases can cause distance estimation errors in the range of tens-hundreds of meters and is particularly problematic in urban and indoor environments. The behavior of the biases dynamically changes depending on the clutter and/or obstructions between the base station and the mobile device. Aspects of the present invention provide practical real-time bias estimation and correction techniques for TOA-based systems and are based on inferring and estimating the biases from dynamic time differential measurements. The techniques can operate in real-time and involve simple calculations.