Augmented Memory Module for 3D Angle of Arrival Adaptability

The system performs preliminary actions by collecting training data in advance for different environments and pre-training the neural network classifier. The augmented memory module stores environment-specific training data that is prepared beforehand, allowing the system to quickly adapt when entering a new environment without requiring real-time learning, thus maintaining measurement precision while preserving positioning capability.

The system implements feedback mechanisms where the neural network classifier continuously receives measurement data and adjusts its predictions based on environmental conditions. The augmented memory module provides feedback by storing and retrieving environment-specific patterns, allowing the system to learn from past measurements and improve AoA and range determination accuracy in response to signal corruption and environmental interference.

The system segments the learning function into a dedicated augmented memory module that is separate from the main processor. The neural network classifier is implemented as a distinct component that interfaces with the augmented memory module, allowing the complex learning operations to be isolated and managed independently. This segmentation reduces overall device complexity by organizing the adaptive learning system into modular, manageable components.

The augmented memory module serves as an intermediary between the environment and the neural network classifier. It stores environment-specific training data and provides it to the classifier, acting as a buffer that simplifies the interaction between the complex learning mechanism and the rest of the system. This intermediary structure reduces device complexity by abstracting the learning process into a dedicated memory-based interface.

The system performs preliminary learning actions by pre-collecting training data and pre-training the neural network classifier for various environments before actual operation. The augmented memory module stores this pre-processed environment-specific data, allowing the system to rapidly adapt to new environments without extensive real-time learning, thus reducing the time loss while maintaining high detection reliability.

The system implements dynamic adaptation where the neural network classifier can quickly switch between pre-trained environment models stored in the augmented memory module. When the environment changes, the system dynamically selects or updates the appropriate model without requiring complete retraining, balancing the need for high reliability with minimized adaptation time through flexible, on-the-fly model switching.