Hybrid Time-of-Arrival Ranging for Multipath-Robust Localization
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Solution Overview
Problem
Existing time-of-arrival-based ranging methods face inaccuracies due to multipath fading and hardware imperfections, leading to unreliable distance measurements.
Innovation Solution
A hybrid method that combines time-based, phase-based, and phase-amplitude-based time-of-arrival estimates, using a multiple signal classification algorithm to mitigate these inaccuracies and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If time-based time-of-arrival estimation is used, then the method is simple to implement, but accuracy deteriorates due to multipath fading and hardware imperfections
Solution Approach 1:
The patent combines three different time-of-arrival estimation methods (time-based, phase-based, and phase-amplitude-based) into a hybrid approach. Each method has different strengths and weaknesses, and by merging them with appropriate weighting, the system achieves higher overall accuracy while maintaining reasonable implementation complexity. The weighted combination allows the benefits of multiple methods to complement each other.
Solution Approach 2:
The hybrid time-of-arrival estimation acts as a composite solution, combining multiple estimation techniques with different characteristics. Just as composite materials combine different substances to achieve superior properties, the hybrid estimator combines multiple estimation methods to achieve superior accuracy while mitigating the individual weaknesses of each method.
2Measurement precision
If phase-based estimation is used, then accuracy improves, but reliability deteriorates due to sensitivity to multipath and hardware non-linearities
Solution Approach 1:
The patent merges phase-based estimation with time-based and phase-amplitude-based estimation methods. The phase-based method provides high accuracy under ideal conditions, while the other methods provide robustness under adverse conditions. By combining them with dynamically adjusted weights, the system achieves both accuracy and reliability.
Solution Approach 2:
The system dynamically changes the weighting parameters of different estimation methods based on channel conditions. When the channel is favorable, phase-based estimation is weighted more heavily for accuracy. When the channel is adverse, the weights shift toward more robust methods, maintaining reliability while preserving accuracy when possible.
3Measurement precision
If sampling rate or bandwidth is increased, then measurement precision improves, but use of energy and device complexity increase
Solution Approach 1:
The patent achieves improved localization precision through signal processing parameter optimization rather than increasing physical parameters like sampling rate or bandwidth. By using a hybrid estimation approach with optimized weighting, the system attains high precision while maintaining the original sampling rate and bandwidth, thus avoiding increased energy consumption.
Data Source
AI summary
A method includes: receiving a ranging signal from the transmitter comprising a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a time-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the time-based uncertainty and the phase-based uncertainty.