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A Simple Introduction to Ultra-Wideband Indoor Positioning Via Artificial Intelligence: Multi-Layer…
- Artificial Intelligence (AI) techniques are promising for information processing in indoor positioning (IP) systems.
- An AI-based architecture, 'Multi-Layer Perceptron (MLP) Decomposition,' is introduced for mobile IoT indoor positioning.
- The architecture uses a bank of MLPs in the first stage and a main MLP block in the second stage for processing position and distance information.
- The design based on MLP decomposition for indoor positioning shows improved accuracy over benchmark techniques like MLP and Linear Regression.
- Accurate indoor positioning is crucial for applications like navigation, warehouse management, and location-based promotions.
- Challenges in indoor positioning include Non-Line of Sight conditions and multipath signals affecting accuracy.
- The novel processing architecture employs Machine Learning principles to address complex relationships in positioning data.
- The architecture breaks down the problem into two stages: Individual Anchor Processing and Data Fusion with a Main MLP.
- Results demonstrate that the MLP Decomposition architecture outperforms other techniques, reducing mean positioning error by 14.5% compared to MLP.
- The architecture is applicable to various positioning technologies and shows promise for high-precision indoor positioning applications.
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