Sensing Entity Signaling for Target Detection Accuracy
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently sensing objects within an area, particularly in distinguishing target objects from background objects, due to the need for extensive resource allocation and analysis to understand environmental RF signatures.
Innovation Solution
The implementation of a method where sensing entities exchange sensing information messages containing attributes of objects within an area, allowing one entity to transmit sensing signals, receive reflected signals, and measure attributes, while leveraging background information from other entities to minimize resource usage and enhance target detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sensing entity performs comprehensive sensing analysis to distinguish target objects from background objects, then detection accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent applies preliminary action by having sensing entities pre-analyze and characterize background RF signatures before target detection. The background information including RF signatures of stationary objects is collected and stored in advance, so that when target detection is needed, the entity can directly compare against pre-established background profiles rather than performing comprehensive analysis from scratch, thereby reducing real-time resource consumption while maintaining detection accuracy
Solution Approach 2:
The patent uses background RF signature profiles as an intermediary between the complex RF environment and the target detection process. By creating intermediate representations of background characteristics (RF signatures, signal patterns, spatial-temporal features), the system mediates the detection process to distinguish targets more efficiently without requiring full comprehensive analysis of all RF signals, thus reducing resource consumption while preserving detection accuracy
2Area of stationary object
If multiple sensing entities operate independently to detect objects in an area, then detection coverage is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent applies merging by having multiple sensing entities share and combine their sensing information and background profiles. Instead of each entity operating completely independently with duplicate resource allocation, the system merges background information across entities, allowing them to leverage collective knowledge about the environment. This reduces redundant resource consumption while maintaining comprehensive detection coverage across the entire area
Solution Approach 2:
The patent implements universality by creating a shared background information repository that serves all sensing entities. The background RF signatures and environmental characteristics are established once and made universally available to multiple entities, eliminating the need for each entity to independently acquire and maintain separate background profiles. This multi-functional approach improves resource allocation efficiency while preserving detection coverage
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 reduces the resources required for sensing by utilizing exchanged attributes to differentiate target objects from background objects, improving detection accuracy and efficiency in both stationary and moving object scenarios.
Implementation Method 1
The apparatus may receive a set of reflected sensing signals based on the set of sensing signals and the target object
Data Source
AI summary
A first sensing entity may receive, from a second sensing entity, a sensing information message that may include a first set of sensing attributes associated with a set of objects within an area associated with the first sensing entity. The first sensing entity may transmit a set of sensing signals at a target object. The first sensing entity may receive a set of reflected sensing signals based on the set of sensing signals and the target object. The first sensing entity may measure a second set of sensing attributes associated with the target object based on the set of reflected sensing signals and the first set of sensing attributes. The measured second set of sensing attributes may be more accurate than a set of sensing attributes that are not based on the first set of sensing attributes.