Covariance-Matrix LLS Estimator for Mobile Terminal Location
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless localization techniques, such as linear least squares (LLS) methods, face challenges in achieving accurate location estimation of mobile terminals due to random selection of reference fixed terminals (FTs) and neglecting covariance matrices, leading to correlated observations and sub-optimal performance in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments.
Innovation Solution
A method is proposed that selects a reference FT based on the smallest measured distance and incorporates a covariance-matrix based LLS estimator to improve accuracy, using map-based two-stage estimation under NLOS conditions to account for correlated observations and NLOS biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a random reference FT is selected for LLS estimation, then the computational complexity is reduced, but the location estimation accuracy deteriorates due to correlated observations and sub-optimal performance
Solution Approach 1:
The patent changes the parameter of reference FT selection from random to systematic (selecting the FT with the smallest measured distance). This parameter change resolves the contradiction by maintaining computational simplicity while significantly improving location estimation accuracy through more optimal reference selection, thereby eliminating correlated observations and sub-optimal performance.
2Device complexity
If the covariance matrix is neglected in LLS estimation, then the computational complexity is reduced, but the location estimation accuracy deteriorates due to correlated observations
Solution Approach 1:
The patent introduces the covariance matrix as an intermediary element to handle correlated observations in the LLS estimation. By incorporating the covariance matrix into the estimation process, the patent resolves the contradiction between computational complexity and accuracy, achieving improved location estimation while maintaining reasonable computational requirements through structured covariance handling.
3Device complexity
If NLOS biases are not accounted for, then the estimation method is simpler, but the location estimation accuracy deteriorates in NLOS environments
Solution Approach 1:
The patent changes the approach to NLOS bias handling by incorporating bias correction terms into the distance measurements. This parameter change allows the estimation method to account for NLOS biases systematically, resolving the contradiction between simplicity and accuracy by maintaining a relatively simple framework while improving performance in NLOS environments through bias-aware estimation.
Data Source
AI summary
A linear least squares (LLS) estimator provides a low complexity estimation of the location of a mobile terminal (MT), using one of the fixed terminals (FTs) as a reference FT to derive a linear model. A method for selecting a reference FT is disclosed, which improves the location accuracy relative to an arbitrary approach to selecting the reference FT. In addition, a covariance-matrix based LLS estimator is proposed in line-of-sight (LOS) and non-LOS (NLOS) environments to further provide accuracy, taking advantage of the correlation of the observations. Different techniques for selecting the reference FT under non-LOS (NLOS) conditions are disclosed. A map-based two-stage LLS estimator assists in selecting the reference FT under NLOS conditions.