Radar-Based Hand Tracking with Neural Network Position Estimation

The patent introduces a neural network algorithm as an intermediary component between the radar sensor and the position estimation output. This neural network processes the raw radar measurement data, filtering out noise and clutter while extracting relevant position information. The neural network acts as a mediator that transforms noisy radar returns into accurate position estimates, resolving the contradiction between maintaining low system complexity and achieving high measurement precision.

The patent replaces traditional mechanical signal processing methods with a neural network-based computational approach. Instead of using conventional filtering and tracking algorithms, the system employs machine learning models that have been trained to recognize and estimate target positions from radar data. This substitution enables superior noise rejection and position estimation accuracy while maintaining computational efficiency through optimized neural network architectures.

The patent applies preliminary action by training the neural network algorithms offline before actual position estimation tasks. During the training phase, the neural networks learn to recognize patterns and estimate positions from large datasets of radar measurements with known ground truth. Once trained, the networks can perform rapid inference on new data without requiring complex real-time computations, thus reducing processing time while maintaining high measurement precision.

The patent optimizes the neural network parameters and architecture to balance computational complexity and processing speed. By carefully selecting network depth, width, and activation functions, the system achieves high accuracy while minimizing computational load. The training process also optimizes parameters like learning rate and batch size to enable efficient convergence, reducing the time required for both training and inference operations.

The patent develops universal neural network architectures that can handle multiple radar processing tasks simultaneously. The same neural network framework is used for noise filtering, position estimation, and potentially other functions like velocity estimation or gesture recognition. This multi-functionality reduces implementation complexity by reusing the same computational infrastructure across different applications, making the system easier to operate while maintaining high precision.

The patent implements self-service mechanisms where the neural networks automatically adapt to different radar configurations and environmental conditions. The training process enables the networks to learn optimal parameters and features specific to each application scenario without requiring manual tuning or configuration. This self-adaptation reduces the operational burden on users while maintaining high estimation accuracy across diverse conditions.