Radar Data Enhancement via AI Inference
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Solution Overview
Problem
Low cost imaging radars have limited angular/range resolutions and are unable to output data with high resolution, which affects the accuracy of object recognition, especially in adverse conditions.
Innovation Solution
A system and method that enhance point cloud data of an imaging radar by using a pretrained artificial intelligence model to infer a higher resolution image from the initial radar data, thereby improving angular and range resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a low cost imaging radar is used, then the price benefit is improved, but the angular resolution and range resolution are degraded
Solution Approach 1:
The patent introduces an artificial intelligence model as an intermediary component between the low-cost radar and the final output. This AI model processes the low-resolution radar data and transforms it into high-resolution output, effectively mediating between the limited hardware capabilities and the desired performance without requiring expensive radar hardware modifications
Solution Approach 2:
The patent replaces the mechanical/hardware-based resolution improvement approach with a software/AI-based approach. Instead of using complex radar hardware with more antennas or higher precision components, the system uses artificial intelligence algorithms to achieve high-resolution output from low-cost radar data
2Ease of manufacture
If a low cost imaging radar is used, then the price benefit is improved, but the range resolution is degraded
Solution Approach 1:
The artificial intelligence model serves as an intermediary that processes range data from the low-cost radar and enhances the resolution through learned patterns and transformations, bridging the gap between affordable hardware and precise measurement requirements
Solution Approach 2:
The patent substitutes hardware-based range resolution improvement with AI-based processing. Rather than using expensive high-resolution radar components, the system employs neural networks and machine learning models to achieve superior range resolution from low-cost radar inputs
3Measurement precision
If high cost imaging radar is used, then the angular resolution and range resolution are improved, but the hardware resources and cost increase
Solution Approach 1:
The patent creates a virtual copy or representation of high-resolution radar data through AI processing. Instead of physically having expensive high-resolution radar hardware, the system generates synthetic high-resolution data that mimics what such hardware would produce, effectively copying the desired output without the costly hardware
Solution Approach 2:
The patent uses inexpensive low-cost radar hardware as a disposable or sufficient base component, accepting that the raw hardware is simple but compensating through software intelligence. The cheap hardware performs the basic sensing, while the AI processing provides the high-resolution output that would otherwise require expensive equipment
4Measurement precision
If high cost imaging radar is used, then the range resolution is improved, but the hardware resources and cost increase
Solution Approach 1:
The AI model generates a virtual copy of high-resolution range data, creating the appearance of expensive radar performance through software processing of inexpensive hardware output, eliminating the need for costly range-resolution-enhanced hardware
5Measurement precision
If the radar data resolution is improved through AI processing, then the object recognition accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the artificial intelligence model offline using large datasets. This preprocessing of the AI model during the training phase enables it to quickly process real-time radar data during deployment, separating the computationally intensive learning phase from the time-critical inference phase
Solution Approach 2:
The system dynamically adapts the AI processing based on operational needs. The model can adjust its processing intensity and resolution enhancement levels depending on the specific situation, balancing between processing time and recognition accuracy in real-time operations
Data Source
AI summary
A system for enhancing radar data includes a radar device, at least one processor, and at least one memory including a computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the system to acquire first radar data corresponding to a target area through the radar device, convert the acquired first radar data into a first image, infer a second image from the first image based on a pretrained artificial intelligence model, and generate a second radar data corresponding to the target area that are enhanced to have a higher resolution than the first radar data based on the inferred second image. The pretrained artificial intelligence model has been trained based on first training radar data and second training radar data with a higher resolution than the first training radar data.