Abstract
Low-cost ranging techniques, such as received signal strength indicator(RSSI), often lead to inaccurate location estimation in wireless sensor networks. To reduce the errors caused by ranging techniques, this paper uses a non-ranging method based on RSSI. We arrange several access points(APs) with known coordinates in the indoor environment, and then select a large number of reference points(RPs) to collect RSSI. A weighted Gaussian hybrid filter algorithm is applied to received RSSI to obtain reference value and construct a RSSI database. We use the DBSCAN clustering algorithm to pre-process the database for classification, and then construct an environmental adaptive neural network localization model to achieve high-precision localization of target nodes in complex indoor environments.
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