AI Model for Wireless Location Accuracy

The patent introduces an AI network model as an intermediary component between the positioning signal measurement and the location determination. This AI model processes the measurement information and provides optimized location results, especially in complex environments where direct measurement fails. The AI model acts as a mediator that transforms inaccurate direct measurements into accurate location information through learning and optimization.

The patent utilizes AI network model parameters that can be adjusted and optimized based on different environmental conditions. By changing and optimizing these parameters, the system adapts to complex multi-path or non-direct path environments, improving location accuracy without requiring a complete redesign of the positioning system architecture.

The AI network model is pre-trained and deployed before actual positioning operations. The model parameters are optimized in advance through training data, so that during actual use, the system only needs to input the positioning signal measurement information and receive optimized location results. This preliminary action reduces the computational burden during real-time positioning while maintaining high accuracy.

The patent employs a simplified interface where the AI model is represented by its parameters and structure rather than the full complex AI computation graph. By using parameter-based representation, the system maintains the intelligence and accuracy of AI models while reducing the visible and computable complexity in the positioning system architecture.

The AI network model performs optimization in advance during the training phase, learning the relationships between positioning signal measurements and accurate location information from extensive training data. During actual positioning operations, the system only needs to input the measurement information and receive optimized results, which is much faster than performing complex optimization calculations in real-time. This preliminary action trades training time for operational speed.

The patent replaces complex real-time mathematical optimization calculations with AI-based parameter transformation. Instead of performing computationally intensive optimization algorithms during positioning operations, the system uses pre-optimized AI model parameters to directly transform measurement information into accurate location results, significantly reducing processing time while maintaining high reliability.