Survey-Free Indoor Localization via Cooperative RSS and Learning-Based DOP Estimation

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This paper presents an automated wireless indoor localization system that eliminates the need for site surveying and reduces the dependency on complete anchors' location knowledge. The proposed approach leverages cooperative Received Signal Strength (RSS) measurements and a hybrid logarithmic path-loss model to automatically localize network anchors and build radio maps that can be used to localize user nodes. Starting from a limited initial number of known anchor nodes, position estimates are propagated to the rest of the network using an iterative strategy guided by a learning-based Dilution of Precision (DOP) metric. To address the challenge of unknown anchor locations at initialization, a novel Gaussian Process Regression (GPR)-based method is introduced to estimate the DOP values without requiring ground-truth coordinates. This enables a self-organizing deployment process that adapts to varying indoor deployments. The system is validated through Cramér–Rao Lower Bound (CRLB) analysis, simulations, and real-world experiments in ZigBee-based networks. Results demonstrate a root-mean-square localization error of 3 meters in a typical indoor 15m x 30m office environment covered by seven partially known anchors without prior site survey using only RSS measurements, providing a balance between cost, deployment complexity, and localization accuracy. The code and data of the work has been made available on GitHub on the following link: https://github.com/SensorFusionBook/SensorFusionProjects/tree/ main/IndoorPositioningProject