Radio Sensing Configuration Using Background Clutter Knowledge
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Solution Overview
Problem
Current radio sensing technologies do not effectively utilize radio sensing intelligence for processing data, leading to inaccuracies and processing latency when interpreting environmental attributes.
Innovation Solution
Configuring sensing nodes with knowledge of background environment features to mitigate clutter reflections and enhance target object detection, using reference signal configurations, acquisition information, and measurement configurations to improve sensing precision and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radio sensing is performed without background environment knowledge, then device complexity is reduced, but measurement precision deteriorates due to clutter reflections
Solution Approach 1:
The system performs preliminary actions by configuring sensing nodes with background environment knowledge before actual target detection. This includes pre-procassing reference signals and preparing environmental models, which enables the nodes to accurately distinguish target objects from background clutter during subsequent sensing operations, thereby improving measurement precision without requiring complex real-time processing.
Solution Approach 2:
Background environment knowledge acts as an intermediary element that mediates between the raw radio sensing data and the final detection results. By incorporating this intermediary information about the environment into the sensing process, the system can filter out false detections from clutter reflections while maintaining relatively simple device architecture at the sensing nodes.
2Reliability
If radio sensing data is processed without background information, then processing time is reduced, but reliability deteriorates due to false detections
Solution Approach 1:
The system performs preliminary processing of reference signals and background environment modeling before actual target detection. By pre-computing environmental models and preparing reference signal databases, the system can quickly compare incoming sensing data against these pre-processed references, improving detection reliability while minimizing real-time processing time and latency.
Solution Approach 2:
The system incorporates feedback mechanisms where background environment knowledge is continuously refined and updated based on sensing results. This feedback loop allows the system to improve detection reliability over time by adapting the background models to actual environmental conditions, while the feedback information is processed efficiently to minimize additional latency.
3Measurement precision
If comprehensive radio sensing is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system applies local quality by tailoring the sensing and processing operations to the specific local environment being measured. By configuring sensing nodes with localized background environment knowledge and adjusting reference signal parameters according to the specific sensing scenario, the system achieves high measurement precision for local conditions while reducing overall energy consumption compared to comprehensive global sensing.
Solution Approach 2:
The system performs partial sensing actions focused on the specific area and objects of interest, rather than comprehensive omnidirectional sensing. By selectively applying background environment knowledge to relevant spatial regions and object types, the system achieves sufficient measurement precision for the task at hand while significantly reducing the total energy consumption associated with full-comprehensive sensing operations.
Data Source
AI summary
Various aspects of the present disclosure relate to methods, apparatuses, and systems that support configuration for radio sensing. For instance, implementations provide for configuration of sensing-related nodes with knowledge of background environments related to specific radio sensing scenarios to assist in sensing receiver processing. Further, knowledge of features of a background environment can be used as assistance information for extracting sensing information for target objects, such as based on information pertaining to signal interactions between a background environment and target objects.