20240027600. Smart-Device-Based Radar System Performing Angular Position Estimation simplified abstract (GOOGLE LLC)
Contents
Smart-Device-Based Radar System Performing Angular Position Estimation
Organization Name
Inventor(s)
Muhammad Muneeb Saleem of Mountain View CA (US)
Smart-Device-Based Radar System Performing Angular Position Estimation - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240027600 titled 'Smart-Device-Based Radar System Performing Angular Position Estimation
Simplified Explanation
The patent application describes a radar system that uses a smart device to estimate the angular positions of objects. The system utilizes a machine-learned module that analyzes complex range data to generate angular position estimates. The module is implemented using a multi-stage architecture, where in the local stage, the range data is split into intervals and processed separately using individual branch modules. In the global stage, the feature data generated from the branch modules is merged using a symmetric function to generate angular position data. This radar system achieves higher angular resolutions compared to other radar systems that use different techniques.
- The patent describes a smart-device-based radar system for angular position estimation.
- The system uses a machine-learned module to analyze complex range data.
- The machine-learned module has a multi-stage architecture with local and global stages.
- In the local stage, the range data is split into intervals and processed using individual branch modules.
- In the global stage, the feature data from the branch modules is merged using a symmetric function.
- The system achieves higher angular resolutions compared to other radar systems.
- The system directly processes complex range data, unlike other radar systems that use analog or digital beamforming.
Potential applications of this technology:
- Automotive industry: The radar system can be used for advanced driver assistance systems (ADAS) to improve object detection and tracking, enabling features like adaptive cruise control and collision avoidance.
- Robotics: The radar system can be integrated into robots for navigation and obstacle avoidance, enhancing their ability to perceive and interact with the environment.
- Security and surveillance: The system can be used for perimeter security, detecting and tracking intruders or suspicious activities in real-time.
Problems solved by this technology:
- Higher angular resolution: The radar system overcomes the limitations of other radar systems by achieving higher angular resolutions, allowing for more accurate object detection and tracking.
- Direct processing of complex range data: By directly processing complex range data, the system eliminates the need for additional processing steps, reducing complexity and improving efficiency.
Benefits of this technology:
- Improved object detection and tracking: The radar system provides more accurate angular position estimates, enhancing the ability to detect and track objects in various applications.
- Higher resolution imaging: The system's higher angular resolutions result in clearer and more detailed imaging of objects, improving situational awareness and decision-making.
- Cost-effective implementation: By utilizing a smart device and machine learning techniques, the system offers a cost-effective solution compared to traditional radar systems.
Original Abstract Submitted
techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular position estimation. a machine-learned module analyzes complex range data generated to estimate angular positions of objects. the machine-learned module is implemented using a multi-stage architecture. in a local stage, the machine-learned module splits the complex range data into different range intervals and separately processes subsets of the complex range data using individual branch modules. in a global stage, the machine-learned module merges the feature data generated from the individual branch modules using a symmetric function and generates angular position data. by using machine-learning techniques and processing the complex range data directly, the radar system can achieve higher angular resolutions compared to other radar systems that utilize other techniques, such as analog or digital beamforming.
