RF Object Localization with CER Images and Transformer Models
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Solution Overview
Problem
Existing RF sensing technologies face challenges in accurately obtaining localization information for target objects in dynamic environments due to difficulties in distinguishing between static objects, changing quantities of target objects, and varying device positions, leading to increased complexity in identifying and determining localization information.
Innovation Solution
Utilizing cooperative multi-static RF sensing combined with computer vision techniques, including visualization-based data representation and transformer architectures, to generate images that represent channel energy responses (CERs) for improved localization accuracy, supporting varying UE quantities and dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional RF sensing is used to detect objects in dynamic environments, then the system can identify target objects, but the localization accuracy deteriorates due to difficulty in distinguishing static objects and handling varying device positions
Solution Approach 1:
The patent transforms RF sensing data into image representations, adding a visual dimension to the data. Channel energy responses are converted into spatial images where objects appear as detectable patterns, enabling the use of computer vision techniques for more accurate localization in dynamic environments
Solution Approach 2:
The patent introduces transformer-based neural networks as intermediaries between the RF sensing data and localization output. These models process the image-represented channel energy responses and extract object localization information, effectively mediating the complex task of distinguishing targets from static background
2Adaptability or versatility
If RF sensing data is processed using traditional methods, then the processing is simpler, but the ability to handle varying quantities of target objects and dynamic environments is reduced
Solution Approach 1:
The patent employs dynamic neural network models (transformers) that can adapt to varying numbers of target objects and changing environmental conditions. The model processes image representations of channel energy responses and dynamically adjusts to different scene configurations, enabling versatile handling of dynamic environments with varying UE quantities
Data Source
AI summary
In some aspects, a user equipment (UE) may receive, from a network node, sensing configuration information for a sensing session, the sensing configuration information indicating one or more transmission reception points (TRPs). The UE may transmit, to the network node, sensing result information associated with one or more images that are representative of channel energy responses (CERs) for respective TRPs of the one or more TRPs. Numerous other aspects are described.