Radar Inter-Pulse Doppler Phase Generation Using BVH Micro-Step Scene Interpolation
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Solution Overview
Problem
Current radar simulation methods are computationally intensive and impractical for generating simulated radar reflection data quickly and efficiently, making them unsuitable for training machine learning or artificial intelligence systems effectively.
Innovation Solution
The proposed method employs an interpolation framework that reduces computational burden by simulating radar reflections using micro-steps and a bounding volume hierarchy (BVH) data structure, limiting simulations to specific reference objects, and using Doppler phase shifts to accelerate the simulation process, allowing for faster generation of coherent measurements and Doppler velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radar simulation methods are used to generate simulated radar reflection data, then measurement precision and reliability are maintained, but computational time and resources increase significantly
Solution Approach 1:
The patent segments the continuous scene into discrete micro-steps (e.g., 100 micro-steps per scene) and processes radar reflections at specific intervals rather than continuously. This segmentation allows the system to maintain measurement precision at key points while dramatically reducing computational burden by skipping intermediate calculations, directly resolving the contradiction between accuracy and computational time.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing scene geometry data, object positions, and radar parameters before the actual simulation. By preparing bounding volume hierarchies and spatial indexes in advance, the system reduces real-time computational requirements while maintaining data accuracy, effectively reducing computational time without sacrificing measurement precision.
2Quantity of substance
If traditional radar simulation methods simulate all objects in the scene, then comprehensive data coverage is achieved, but device complexity and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific reference objects that are most relevant to the radar simulation rather than uniformly processing all objects. By identifying and prioritizing objects based on their radar cross-section, position, and motion characteristics, the system achieves comprehensive data coverage for critical targets while reducing device complexity by excluding less important objects from detailed simulation.
Solution Approach 2:
The patent implements partial action by simulating radar reflections for only a subset of objects (reference objects) rather than all objects in the scene. This selective approach provides sufficient data coverage for training machine learning systems while significantly reducing simulation complexity and computational resources required, as the patent demonstrates that simulating all objects is excessive for the intended application.
3Measurement precision
If full scene re-rending is performed at pulse rate for Doppler phase coherent return signals, then signal accuracy is maintained, but productivity decreases
Solution Approach 1:
The patent implements periodic action by performing full Doppler phase coherent simulations only at specific pulse intervals rather than at every pulse. The system maintains measurement precision by ensuring phase coherence at these periodic intervals while improving productivity by using interpolated or approximated values for intermediate pulses, thereby reducing the frequency of computationally intensive full scene re-rending operations.
Solution Approach 2:
The patent uses copying by creating simplified representations or proxies of the full scene for intermediate pulse calculations. Instead of re-rendering the complete scene at every pulse, the system copies and interpolates from previously computed phase coherent data, maintaining adequate signal accuracy for training purposes while dramatically improving simulation speed and productivity.
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 significantly reduces computational time and resources required for simulating radar reflections, enabling faster and more efficient training of AI systems to identify objects encountered by autonomous vehicles.
Implementation Method 1
motion data that identifies start times, start locations, end times, and end locations of a set of reference objects in a scene-scenario... simulations may be performed to estimate the appearance of reflected radar signals... These simulations may identify patterns of reflected radar energy
Data Source
AI summary
The present disclosure is directed to simulating patterns of reflected radar energy off of reference objects using motion data associated with these reference objects. This motion data may identify start times, start locations, end times, and end locations of a limited number reference objects in a set of discrete scenes. Each of these discrete scenes may also have a same time duration. Motion of these specific objects between a start time and an end time of each discrete scene may be interpolated. Once the locations of the objects are interpolated for a given scene, simulations may be performed to estimate the appearance of reflected radar signals that would be received by a radar apparatus. These simulations may identify patterns of reflected radar energy after radar signals have been emitted from the radar apparatus and these patterns may then be provided to train a machine learning apparatus.


