The increasing demand for precise indoor navigation has accelerated research into advanced positioning technologies capable of overcoming the limitations of conventional satellite-based localization systems. This research presents an Adaptive Spatial Positioning Model for Real-Time Indoor Navigation using Ultra-Wideband (UWB) IoT systems integrated with the Unity cross-platform development environment. The proposed approach combines high-resolution UWB ranging capabilities, IoT-based spatial data communication, and Unity-driven visualization optimization to develop an interactive and adaptive indoor positioning framework. The model focuses on improving localization reliability through efficient anchor-tag coordination, spatial data processing, and real-time three-dimensional environment representation. Existing UWB localization approaches demonstrate strong accuracy potential; however, challenges related to environmental interference, computational optimization, and seamless cross-platform deployment remain significant. The proposed framework addresses these limitations by integrating adaptive positioning algorithms with Unity-based spatial rendering mechanisms. Experimental analysis indicates that the architecture can enhance indoor navigation performance by improving positioning stability, reducing latency, and supporting scalable IoT applications. The study contributes a unified methodology for intelligent indoor navigation systems applicable to smart buildings, industrial automation, augmented reality environments, and location-aware IoT services.
Adaptive Spatial Positioning Model for Real-Time Indoor Navigation Using Ultra-Wideband IoT Systems and Unity Engine Optimization
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Abstract
References
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