Detection of Transport Object Coordinates Under Signal Scarcity Using Takagi — Sugeno Fuzzy Inference Model

The significance of real-time detection of transport object coordinates has increased in conditions of information “blindness” when traditional navigation methods become unavailable. This is especially important in rail transport where positioning errors can lead to catastrophic consequences. Purpose: to develop an algorithm based on Takagi — Sugeno fuzzy inference to improve the accuracy and reliability of detecting the transport object coordinates in conditions of data scarcity. Methods: an adaptive model integrating fuzzy logic for processing incomplete and contradictory signals is proposed. The algorithm implements multi-level data analysis taking into account the dynamics of movement and external interference for the infrastructure critical sections. Results: computer experiments have been conducted for railway sections with a limited number of track sensors. Recommendations on the algorithm application to support decision-making have been developed. Discussion: the effectiveness of using fuzzy logic to compensate for information “blindness” has been proven. The prospects for implementing the algorithm in security and dispatching systems, as well as the need for further optimization of computational complexity for high-speed objects have been outlined.