Rank-Based Positioning Engine for NLOS Multipath Error Correction
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Solution Overview
Problem
Conventional satellite-based positioning methods, such as GPS and GNSS, face significant accuracy issues in non-line-of-sight (NLOS) environments due to the multipath effect, where signal reflections and blockages from urban structures cause measurement errors that conventional methods like the Newton-Raphson method struggle to correct.
Innovation Solution
A pure software solution using a rank-based approach within a positioning engine to process satellite signals, which estimates pseudo-range values and generates a residual vector to improve positioning accuracy by applying a robust estimation method, specifically a rank-based norm, to minimize errors caused by multipath effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional positioning methods (Newton-Raphson) are used, then the positioning system can operate with simple algorithms, but positioning accuracy deteriorates in NLOS environments due to multipath effects
Solution Approach 1:
The patent changes the mathematical parameters of the positioning algorithm by replacing the conventional Newton-Raphson method with a rank-based approach. This involves transforming the objective function from a standard least-squares form to a rank-based norm minimization problem, which fundamentally alters how measurement residuals are processed. The rank-based method reweights satellite measurements based on their reliability indicators, effectively changing the parameter weighting scheme to prioritize line-of-sight signals over multipath-affected signals.
Solution Approach 2:
The patent substitutes the conventional iterative mechanical optimization process (Newton-Raphson with its gradient-based updates) with a rank-based mathematical approach. Instead of relying on iterative mechanical adjustments to converge to a solution, the system uses rank-based norm minimization that directly addresses the objective function, replacing the mechanical iteration process with a more robust mathematical framework that handles NLOS conditions inherently.
2Measurement precision
If conventional positioning methods are used, then the system can process satellite signals directly, but measurement precision deteriorates due to uncorrected multipath errors
Solution Approach 1:
The patent converts the harmful multipath effect into a beneficial filtering mechanism. By introducing reliability indicators that quantify the likelihood of multipath contamination for each satellite measurement, the system identifies and downweights corrupted pseudo-range data. The rank-based approach transforms the presence of multipath errors from a detrimental factor into an opportunity to selectively enhance measurement quality by prioritizing clean LOS signals over contaminated NLOS signals.
Solution Approach 2:
The patent introduces reliability indicators as an intermediary layer between raw satellite measurements and the positioning calculation. These indicators serve as mediators that assess the quality of each pseudo-range measurement and translate it into appropriate weighting for the rank-based objective function. This intermediary mechanism allows the system to indirectly handle multipath effects without directly measuring or eliminating the physical multipath signals themselves.
3Measurement precision
If a robust estimation method is implemented, then positioning accuracy in NLOS environments is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively processing satellite measurements based on their reliability. Rather than uniformly processing all satellite signals with equal computational resources, the rank-based method focuses computational effort on the most reliable line-of-sight measurements while applying reduced processing to potentially corrupted measurements. This partial processing approach achieves robust positioning accuracy without the full computational overhead of processing every measurement at maximum detail.
Data Source
AI summary
A method for determining a location of a target includes: obtaining a plurality of guess distance values of a plurality of satellites, respectively; utilizing satellite signals received from the plurality of satellites to estimate a plurality of pseudo range values of the plurality of satellites, respectively; comparing the guess range values with the pseudo range values to generate a residual vector; and utilizing a processing unit for applying a rank-based approach based on the residual vector to generate a calculation result. The location of the target is derived from the calculation result.