Wireless STA Distance-Direction Estimation for Flip-Ambiguity Resolution
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Solution Overview
Problem
Existing indoor positioning technologies face inaccuracies and flip ambiguity issues due to insufficient measurements, especially in environments where ultra-wideband devices are scarce, limiting precise localization and direction estimation.
Innovation Solution
A station (STA) in a wireless network uses a tracking filter to estimate distance and direction by combining range measurements with displacement data, employing a bimodal direction model and particle filtering to resolve flip ambiguity without requiring a camera, suitable for Wi-Fi, UWB, or Bluetooth technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Wi-Fi-based positioning is used, then availability is improved, but measurement precision deteriorates due to insufficient measurements and flip ambiguity
Solution Approach 1:
The patent implements a tracking filter that continuously refines position estimates by incorporating feedback from multiple range measurements over time. The filter uses previously estimated positions and directions to constrain current measurements, progressively reducing ambiguity and improving precision through iterative refinement.
Solution Approach 2:
The patent resolves flip ambiguity by transitioning from two-dimensional positioning to three-dimensional orientation estimation. By incorporating direction-of-motion vectors and tracking the device's movement trajectory, the system adds a temporal/dimensional dimension that disambiguates the flip ambiguity inherent in two-dimensional range-based positioning.
2Measurement precision
If UWB technology is used, then measurement precision is improved, but availability deteriorates due to scarcity of UWB devices
Solution Approach 1:
The patent uses Wi-Fi-based range measurements as an intermediary to achieve positioning functionality. Instead of requiring direct UWB measurements, the system uses Wi-Fi access points and stations as mediators to obtain range data, which is then processed through the tracking filter to achieve positioning accuracy comparable to UWB systems.
Solution Approach 2:
The patent creates a virtual copy of UWB positioning capability using Wi-Fi technology. By implementing a tracking filter that processes Wi-Fi range measurements in a manner similar to how UWB systems process their measurements, the system replicates the positioning functionality without requiring actual UWB hardware.
3Measurement precision
If tracking filter is initialized with bimodal direction, then flip ambiguity is resolved, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-defining the bimodal direction distribution before processing range measurements. The tracking filter is initialized with a known bimodal distribution that represents the two possible directions, allowing the system to immediately begin resolving flip ambiguity without complex real-time analysis.
Solution Approach 2:
The patent simplifies the complexity by changing the parameter representation from continuous directional data to a discrete bimodal distribution. Instead of processing continuous angle measurements, the system uses a simplified two-mode probability distribution that captures the essential ambiguity, reducing computational complexity while maintaining precision.
Data Source
AI summary
A station (STA) in a wireless network comprising a memory and a processor coupled to the memory. The STA obtains, for a first step, a range distance to a target STA, a cumulative step size from a reference step and a first step heading. The STA determines a differential heading between the first step heading and a second step heading at a second step preceding the first step, based on a determination that a tracking filter is initialized. The STA predicts a first state using the tracking filter, based on the cumulative step size and, the differential heading. The STA updates the predicted first state using an estimator, based on the range distance. The STA determines a second state using an estimator, based on the updated predicted first state. The STA estimates a distance to the target STA and a direction to the target STA based on the second state.