RF Object Localization Using ML Subspace Models
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Solution Overview
Problem
Existing object location systems are costly, require significant infrastructure, and are prone to environmental changes, making them inefficient for real-time tracking in large, multi-dimensional spaces like warehouses and hospitals, and they often rely on RF fingerprinting which is susceptible to environmental interference.
Innovation Solution
A system using machine learning models, such as Support Vector Machines, to predict the location of objects within predefined subspaces by analyzing RF signals from prepositioned beacons and tags, which can adapt to environmental changes and minimize infrastructure requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If active RF location systems are used, then real-time tracking capability is improved, but infrastructure cost and complexity increase significantly
Solution Approach 1:
The patent uses passive RFID tags as copies or representations of objects to be tracked. Instead of equipping each object with active RF transmitters, the system uses passive tags that reflect RF signals from fixed transmitters, significantly reducing the cost and complexity of tracking infrastructure while maintaining real-time location capability.
Solution Approach 2:
The patent extracts the active RF transmitting function from the tracked objects and concentrates it in fixed transmitters positioned throughout the space. This separation allows objects to be tracked without carrying power sources or active electronics, reducing their complexity and cost.
2Ease of operation
If RF fingerprinting techniques are used for location, then deployment ease is improved, but measurement precision deteriorates due to environmental interference
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors RF signal characteristics and uses this information to dynamically adjust location predictions. The machine learning model processes feedback about signal strength, timing, and environmental conditions to refine location accuracy despite changes in the RF environment.
Solution Approach 2:
The patent transitions from static RF fingerprinting to dynamic location determination using machine learning models that adapt to changing environmental conditions. The system continuously learns from new data about signal propagation patterns, allowing it to maintain high measurement precision despite environmental variations.
3Measurement precision
If multiple RF beacons are deployed throughout the space, then location accuracy is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent makes the fixed RF transmitters serve multiple functions: they act as both location reference points for passive RFID tags and as environmental sensors for machine learning training. This multi-functionality reduces the total number of dedicated infrastructure components needed while maintaining high location accuracy.
Solution Approach 2:
The patent merges the location infrastructure with environmental monitoring and machine learning training functions. The same RF beacons and transmitters used for location determination also collect data for training the machine learning models, consolidating infrastructure requirements into a single integrated system.
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
The system provides accurate, cost-effective, and adaptable object location at a subspace level, minimizing maintenance and infrastructure costs while maintaining high accuracy despite environmental changes.
Implementation Method 1
A machine learning system is used to predict the location of an object within a master space based on RF signals from a plurality of RF beacons and/or other identifiable RF signal sources (IRFSS) whose locations are known or are otherwise used for location purposes
Data Source
AI summary
In embodiments, an object location system is configured to receive a transmitted tag package from a Radio Frequency (RF) tag associated with an object. The transmitted tag package includes a representation of RF signals received by the RF tag from respective RF signal sources. The object location system is configured to access multiple data models associated with a respective subspace and generated by associating a RF signal sample received by a tag in the associated subspace at a prior time. The object location system is configured to compare the representation of RF signals to each of the plurality of data models. The object location system is configured to select a candidate data model having a highest correlation with the representation of the RF signals from the comparison. The object location system is configured to predict that the object is located in a subspace associated with the candidate data model.