A highly accurate indoor positioning under the effect of multipath reflections has been a prominent challenge for recent research. This paper proposes a novel indoor visible light communication (VLC) positioning model by connecting k-nearest neighbors (kNN) and random forest (RF) algorithms for reflective environments, namely, kNN-RF. In this fingerprint-based model, we first adopt kNN as a powerful solution to expand the number of input features for RF. Next, the importance rate of these features is ranked and the least effective one(s) may be removed to reduce the computation effort. Next, the training process using the RF algorithm is conducted. Finally, the estimation process is utilized to discover the final estimated position. Our simulation results show that this new approach improved the positioning accuracy, making it nearly five times better than other popular kNN algorithms.
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Tran et al. (2019) studied this question.
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