Remote Localization Using Structural and Antenna Mode Scattering
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Solution Overview
Problem
Existing remote localization and radio-frequency identification (RFID) systems face challenges in accurately localizing objects in environments with obstacles, particularly due to the non-uniqueness of structural mode responses from tags and the ambiguity in distinguishing between multiple tags with the same radar signature.
Innovation Solution
The implementation of a Bayesian estimation-based multi-target localization algorithm that utilizes both structural and antenna mode responses from transmitter-reader pairs, combined with background clutter removal, to determine the blocking likelihoods at intersection points and update location estimates, enabling accurate localization of objects even when multiple tags have the same radar signature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only antenna mode responses are used for localization, then the system is simpler to implement, but multiple tags with the same radar signature cannot be distinguished
Solution Approach 1:
The patent combines structural mode responses and antenna mode responses into a unified localization framework. By merging these two previously separate response types, the system achieves better distinction between multiple tags while maintaining manageable complexity through integrated processing.
Solution Approach 2:
The patent segments the backscattered signal into distinct structural mode and antenna mode components. This segmentation allows independent analysis and processing of each mode, enabling the system to leverage the complementary information from both modes to resolve ambiguities in multi-tag environments.
2Loss of information
If structural mode responses are used for localization, then more information is available for distinction, but the non-uniqueness of responses creates ambiguity
Solution Approach 1:
The patent employs Bayesian estimation with feedback mechanisms to continuously update the probability distributions of tag locations based on observed structural and antenna mode responses. This feedback loop allows the system to resolve ambiguities by incorporating prior knowledge and updating beliefs based on new measurements.
Solution Approach 2:
The patent transforms the localization problem into a parameter estimation problem by modeling the responses in terms of probability distributions and likelihood functions. By changing the approach from direct signal matching to statistical parameter estimation, the system handles non-unique responses systematically.
3Measurement precision
If multiple transmitter-reader pairs are used to improve localization accuracy, then more data is collected, but the computational complexity increases
Solution Approach 1:
The patent processes data from multiple transmitter-reader pairs but selectively uses only the necessary information for localization. By using partial action principles, the system collects extensive data but processes only the relevant portions, reducing computational burden while maintaining 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
This approach enhances the signal-to-noise ratio for better location estimates, allowing for the precise localization of multiple tags with the same structural mode response, overcoming previous ambiguities and improving localization performance in cluttered environments.
Implementation Method 1
the backscattered signal includes a structural mode component and an antenna mode component
Data Source
AI summary
The present disclosure generally relates to localization and, more particularly, to remote localization and radio-frequency identification using a combination of structural and antenna mode scattering responses. A method and system include receiving location data of an object from a plurality of transmitter-reader pairs (TRPs); generating a plurality of first ellipses representing the received location data; determining blocking likelihoods at points of intersection between the plurality of first ellipses; generating additional ellipses representing additional location data received from additional TRPs; and updating the blocking likelihoods at points of intersection between the plurality of first ellipses and the additional ellipses.