Object Position Estimation Through Confidence-Guided Space Division
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Solution Overview
Problem
Conventional methods for estimating the position of a radio-frequency transmitter lack accuracy and are computationally inefficient, particularly when implemented on low-power devices.
Innovation Solution
A method that determines distances from an object to multiple sensor modules, performs successive division operations on a reference space, assigns confidence factors to subspaces, and selects a new reference space based on these factors to improve position estimation, using basic arithmetic operations and adjusting accuracy levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fingerprinting methods are used for position estimation, then the method is simple to implement, but the position accuracy is poor
Solution Approach 1:
The reference space is divided into multiple subspaces through successive division operations, allowing the algorithm to systematically narrow down the object's position by evaluating confidence factors for each subspace, thereby improving accuracy through structured spatial segmentation
Solution Approach 2:
The algorithm dynamically adjusts the division operations based on confidence factors calculated from distance measurements, adaptively refining the reference space until a termination criterion is met, enabling flexible control between accuracy and computational cost
2Measurement precision
If modern high-accuracy localization technologies are used, then position accuracy improves to 10 cm or better, but the computational efficiency decreases and power consumption increases
Solution Approach 1:
The algorithm uses computationally inexpensive basic arithmetic operations instead of complex mathematical formulas, creating a lightweight solution that can be executed on low-power devices without requiring high-performance computing resources
Solution Approach 2:
The method allows adjustment of the termination criterion parameters to control the number of division operations, enabling users to balance between position accuracy and power consumption based on specific application requirements
3Productivity
If conventional Linear Least Squares or Kernel-based solutions are used, then position estimation can be performed, but the computational efficiency is poor for real-time applications
Solution Approach 1:
The algorithm replaces complex mathematical computation systems (Linear Least Squares, Kernel methods) with a simpler geometric division approach using basic arithmetic operations, achieving both improved computational efficiency and maintained accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides accurate and computationally efficient position estimation suitable for low-power devices, allowing real-time localization with adjustable precision.
Implementation Method 1
determining, for each of the plurality of sensor modules, a distance from the object to the respective sensor module by exchanging signals between the object and the respective sensor module
Data Source
AI summary
The present disclosure relates to a method for estimating a position of an object by determining a distance from the object to a sensor module and determining a starting reference space indicative of a position estimate of the object. The method also includes performing a plurality of successive division operations of the starting reference space for improving the position estimate of the object until a predetermined criterion is met. A division operation includes dividing the current reference space into subspaces. Further, the method includes assigning, on each of the subspaces, a confidence factor indicating a confidence that the object is positioned in the respective subspace, and selecting a new reference space from the subspaces based on the confidence factors. The method further comprises determining the position of the object based on the new reference space according to the last division operation.