Generative Model for Synthetic Radar Data Generation
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Solution Overview
Problem
Existing radar systems face challenges in generating sufficient and diverse training data for machine-learning models, which are data-hungry and require extensive efforts to adapt to specific radar devices or tasks.
Innovation Solution
A method and apparatus for generating synthetic radar data using a trained generative model, which is trained based on real raw radar data, to produce synthetic raw radar data of sampled chirps, thereby simplifying the data generation process and improving data diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real raw radar data is collected manually for training machine-learning models, then the training data quantity increases, but the time consumption and effort increase significantly
Solution Approach 1:
The patent employs a generative model to create synthetic copies of real radar data. The model learns the underlying distribution of real raw radar data during training and then generates synthetic training data that mimics the statistical properties and characteristics of real data, eliminating the need for manual data collection while providing sufficient training samples
Solution Approach 2:
The generative model is trained in advance on a relatively small set of real radar data to learn the data distribution. Once trained, it can autonomously generate large quantities of synthetic training data without requiring further manual collection efforts, thus performing the data generation action preliminarily and efficiently
2Reliability
If machine-learning models are adapted to specific radar devices or tasks, then the model performance improves, but the training effort and complexity increase
Solution Approach 1:
The generative model enables efficient adaptation to specific radar devices by learning device-specific data characteristics during training. The model can be retrained or fine-tuned with data from a specific radar device, and the synthetic data generation automatically adapts to that device's characteristics, simplifying the adaptation process while maintaining specialized performance
Solution Approach 2:
The generative model serves multiple functions: it can generate synthetic training data for different radar devices, simulate various radar tasks and scenarios, and provide diverse data augmentations. This multi-functionality reduces the overall complexity by using a single system for multiple adaptation needs
3Adaptability or versatility
If diverse training data is generated to improve model adaptability, then the model versatility increases, but the data generation complexity increases
Solution Approach 1:
The generative model dynamically adapts to generate diverse training data by learning the underlying data distribution and its variations. It can adjust the generated synthetic data to match different scenarios, devices, and tasks based on the training data it receives, providing versatility without requiring complex manual data generation processes
Data Source
AI summary
In accordance with an embodiment, a method includes: obtaining a trained generative model; and using the trained generative model to generate synthetic radar data, wherein the synthetic radar data is synthetic raw radar data of sampled chirps.