Method and system for generating radar reflection points

Abstract

The invention relates to a method for generating radar reflection points comprising the steps of: providing a plurality of predefined radar reflection points of at least one first object detected by aradar and at least one first scenario description describing a first environment related to the detected first object; converting the predefined radar reflection points into at least one first powerdistribution pattern image related to a distribution of a power returning from the detected first object; training a model based on the first power distribution pattern image and the first scenario description; providing at least one second scenario description describing a second environment related to a second object; generating at least one second power distribution pattern image related to a distribution of a power returning from the second object based on the trained model and the second scenario description; and sampling the second power distribution pattern image.

Description

technical field [0001] The invention relates to a method and a system for generating radar reflection points and a computer program carrier comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method. Background technique [0002] Radar simulation is of great significance for the development and verification of radar functions, such as driver assistance, sensor fusion, and vehicle autonomous driving. Existing radar models are usually black-box or white-box. The black-box model represents radar returns in a stochastic manner. White-box models use ray-tracing to estimate electromagnetic path propagation, and often rely on given target radar cross-section values, or extract virtual scattering centers, as e.g. K.Schuler et al. in the article "Extraction of virtual scattering centers of vehicles by ray-tracing simulations" introduced. White-box models require detailed environmental models to capture important radar-rela...

AI Technical Summary

Problems solved by technology

Although DSRM demonstrates the advantages of neural networks in radar data processing, since the input and output of the model are special raw data, that is, data before disambiguation, clustering, and / or target tracking, the model does not perform well on real radar equipment. Apps in still restricted

At this point, these raw data cannot be directly used for further development of radar simulation functions

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