Abstract: [Purposes]In complex application scenarios such as the industrial internet of things (IIoT),factors including wall penetration,multi-source signal interference,and human obstruction can affect electromagnetic wave signals,resulting in indoor positioning errors.[Methods]To address these issues,this paper proposes an indoor positioning algorithm based on CNN-LSTM occlusion detection model,which improves positioning accuracy by rapidly eliminating abnormal signal fluctuations caused by occlusion.Using a signal loss model as a reference,the algorithm inputs RSSI feature vectors into CNN-LSTM model for training to construct the occlusion detection model,and introduces the crested Porcupine optimizer (CPO) to search for optimal hyperparameters and enhance model performance.[Findings]To verify the adaptability of the model,comparative experiments are conducted by incorporating the occlusion detection model into the WKNN and IWKNN positioning algorithms.Experimental results show that the positioning accuracy of the CNN-LSTM-IWKNN algorithm is improved by 19.4%,11.0%,and 5.6% compared with WKNN,IWKNN,and CNN-LSTM-WKNN respectively.[Conclusions]Therefore,the CNN-LSTM occlusion detection model effectively mitigates positioning errors caused by occlusion and significantly improves indoor positioning accuracy.

Key words: convolutional neural network (CNN), long short-term memory (LSTM), crested Porcupine optimizer (CPO), occlusion detection, indoor positioning