AN RFID INDOOR POSITIONING SYSTEM BY USING PARTICLE SWARM OPTIMIZATION-BASED ARTIFICIAL NEURAL NETWORK

Indoor Location information service (ILS) has been the hot topics of research in recent years. However, localization cost and positioning accuracy is still a challenge for indoor positioning system (IPS). RFID positioning technology is low cost but high positioning accuracy which is usually used for an IPS. In this study, a RFID indoor positioning algorithm is proposed, which is based on the Particle Swarm Optimization Artificial Neural Network (PSO-ANN). The algorithm uses PSO to optimize the weight and threshold of ANN network, and establish an accurate classification model that can learn the relationship between the Received Signal Strength Indication (RSSI) and tag position. In addition, in order to reduce the impact of the environmental factors on the position estimation effectively, the Gaussian Filter is adopted to process the RSSI information. The experimental result demonstrates that the proposed algorithm has better performance than other artificial neural network.