Scene-adaptive radar hyperparameter tuning via neural network

The patent implements dynamic hyperparameter selection by training a neural network model to automatically select optimal hyperparameters based on the current radar scene characteristics. The model processes radar data and outputs selected hyperparameters that adapt to varying environments, transforming the static hyperparameter configuration into a dynamic adaptation mechanism that maintains high radar performance across different scenes.

The patent introduces a neural network model as an intermediary component between radar data input and hyperparameter selection. This intermediary model, trained offline on diverse radar scenes, automatically maps scene characteristics to optimal hyperparameter configurations. The model acts as a mediator that handles the complexity of scene adaptation, allowing the main radar processing chain to benefit from adaptive hyperparameters without requiring complex real-time optimization algorithms.

The patent performs hyperparameter adaptation in advance by training the neural network model offline on a comprehensive dataset of diverse radar scenes. During offline training, the model learns to rapidly select optimal hyperparameters for different scene types. This preliminary action transfers adaptation knowledge to the deployed system, enabling fast online hyperparameter selection without requiring time-consuming real-time optimization or extensive processing during actual radar operation.