Radar-Based Object Detection for Transparent Objects
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Solution Overview
Problem
Existing object detection and classification technologies, such as LIDAR sensors and computer vision, struggle to effectively detect and classify spatially uniform or optically transparent objects.
Innovation Solution
The implementation of radar-based object detection, tracking, and classification systems in electronic devices, which utilize radar sensors to extract motion characteristics and surface features from radar signals for object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors or computer vision techniques are used for object detection, then detection capability for general objects is improved, but detection capability for spatially uniform or optically transparent objects deteriorates
Solution Approach 1:
The patent changes the detection parameter from optical wavelength (LIDAR/camera) to radio wavelength (radar). Radar waves have different interaction properties with transparent objects compared to light waves, allowing detection of objects that are invisible to optical sensors. This parameter change resolves the contradiction by maintaining high detection accuracy for general objects while adding the capability to detect transparent objects that optical methods miss.
Solution Approach 2:
The patent substitutes the optical detection system (LIDAR/camera) with a radar-based electromagnetic detection system. This replacement uses radio frequency electromagnetic waves instead of optical electromagnetic waves, fundamentally changing how objects are detected. The substitution resolves the technical contradiction because radar waves can penetrate or reflect from transparent materials in ways that visible light cannot, enabling reliable detection of transparent objects while maintaining effectiveness for opaque objects.
2Reliability
If radar sensors are used for object detection, then detection capability for transparent objects is improved, but system complexity increases
Solution Approach 1:
The patent implements a radar system that serves multiple functions: detecting transparent objects, detecting opaque objects, tracking object motion, and providing spatial information. By making the detection system universal, the radar can handle both transparent and opaque objects with a single technology platform, reducing overall system complexity compared to having separate specialized systems for different object types.
Solution Approach 2:
The patent combines object detection, motion tracking, and spatial mapping functions into a single radar-based system. Rather than using separate LIDAR, camera, and motion sensors that would require complex integration, the radar system performs multiple detection functions simultaneously, simplifying the overall system architecture while maintaining high reliability for detecting transparent objects.
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 enables accurate detection and classification of objects, including spatially uniform and optically transparent ones, by leveraging the unique properties of radar signals, thereby improving object recognition and notification systems.
Implementation Method 1
obtain radar signals from a radar sensor of an electronic device
Implementation Method 2
reflected portions of the emitted radar signals
Implementation Method 3
a Doppler information, and/or a range information
Data Source
AI summary
Implementations of the subject technology provide object detection and/or classification for electronic devices. Object detection and/or classification can be performed using a radar sensor of an electronic device. The electronic device may be a portable electronic device. In some examples, object classification using a radar sensor can be based on an identification of user motion using radar signals and/or based on extraction of surface features from the radar signals. In some examples, object classification using a radar sensor can be based on time-varying surface features extracted from the radar signals. Surface features that can be extracted from the radar signals include a radar cross-section (RCS), a micro-doppler signal, a range, and/or one or more angles associated with one or more surfaces of the object.