Recursive Phase Vector Subspace Estimation for Wireless Client Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current location techniques using Angle-of-Arrival (AoA) measurements in wireless communications face accuracy issues due to missed packets, erroneous measurements, and client device mobility, which conventional methods fail to adequately address, leading to reduced location accuracy.
Innovation Solution
The implementation of a recursive phase vector subspace estimation method that identifies and corrects for missing and erroneous AoA measurements by generating a phase observation matrix, computing an estimated subspace matrix, and reconstructing phase vectors, thereby improving location accuracy for both static and mobile client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AoA measurement techniques are used for location determination, then location functionality is provided, but location accuracy deteriorates when packets are missed, erroneous measurements occur, or client devices are mobile
Solution Approach 1:
The patent implements a feedback mechanism where the processor compares reconstructed phase vectors with originally received phase vectors to identify discrepancies. When errors or missing values are detected, the system uses recursively updated subspace information to correct these measurements, feeding the corrected data back into the location calculation process. This continuous feedback loop maintains measurement reliability while preserving location accuracy.
Solution Approach 2:
The system performs preliminary subspace estimation and reconstruction of phase vectors before final location determination. By pre-processing the phase measurements to correct errors and fill missing values using recursive subspace methods, the system prepares improved measurement data in advance, ensuring both reliability and accuracy are maintained before the critical location calculation step.
2Measurement precision
If phase vectors are updated frequently to track mobile clients, then tracking accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent employs dynamic subspace tracking where the signal subspace is recursively updated as new phase vectors arrive from mobile clients. Instead of recalculating the entire subspace from scratch, the system dynamically adapts the subspace estimate using incoming measurements, allowing accurate tracking of mobile devices while reducing computational burden through incremental updates rather than full recalculations.
3Measurement precision
If recursive phase vector subspace estimation is implemented to correct measurement errors, then location accuracy improves, but computational processing requirements increase
Solution Approach 1:
The system maintains continuous subspace estimation and phase vector reconstruction through recursive updates. By continuously processing incoming phase vectors through the established subspace model, the system keeps the location determination process active and accurate without requiring periodic full recalculations, thereby improving location accuracy while managing computational power requirements through efficient continuous operation.
Data Source
AI summary
Techniques for determining a location of a client device using recursive phase vector subspace estimation are described. One technique includes receiving a plurality of angle-of-arrival (AoA) measurements from a plurality of access points (APs). Each AoA measurement includes a plurality of entries for phase values measured from a signal received from a client device at the plurality of APs. At least one AoA measurement of the plurality of AoA measurements that includes at least one of: (i) one or more entries with missing phase values and (ii) one or more entries with erroneous phase values is identified, based on a recursive phase estimation. The plurality of AoA measurements are updated based on the identified at least one AoA measurement. The location of the client device is determined, based on the updated plurality of AoA measurements.