An algorithm for indoor positioning based on LightGBM + ExtraTrees chaotic weighted ensemble: evaluation and comparison

Abstract

To address the issues of insufficient positioning accuracy and poor stability in existing indoor positioning algorithms, this paper proposes an indoor fingerprint positioning algorithm based on LightGBM and ExtraTrees chaotic weighted ensemble. First, the received signal strength indicator (RSSI) signal strengths of wireless fidelity (WiFi) at different locations are collected using a mobile device to construct a fingerprint database. Kalman filtering is then applied to preprocess the fingerprint data, removing outliers and noise to improve the data quality. The preprocessed dataset is subsequently divided into training and testing sets. Light gradient boosting machine (LightGBM) and extremely randomized trees (ExtraTrees) are used for modeling, and the chaos particle swarm optimization (CPSO) algorithm is employed to optimize the key parameters of both LightGBM and ExtraTrees. The optimal parameter configuration is determined based on comprehensive evaluation metrics. Finally, the optimal weight ratio of LightGBM and ExtraTrees models is determined through the CPSO algorithm for weighted fusion. Experimental results demonstrate that the proposed algorithm achieves an average positioning error of 1.1 m, which represents a reduction of 7.5–26.7% in average positioning error compared to LightGBM, ExtraTrees, and the LightGBM + ExtraTrees algorithms. After introducing random noise, the proposed algorithm exhibits the smallest variation in average positioning error, validating its significant advantages in positioning accuracy and anti-interference ability. However, the algorithm in this study is primarily based on static WiFi fingerprint data and has not yet been validated in dynamic environments. Future research should further explore its applicability and robustness in more complex environments.