Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Abstract

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

Location Anomalies Detection for Connected and Autonomous Vehicles

“…The RSSI represents the beacon's signal strength and it is dependent on the maximum broadcasting power, the antenna gains, and the attenuation from the channel and the distance. RSSI has been widely used for indoor localization [8]- [10], human activity recognition [11] and movement tracking [12] in wireless networks. For CAVs, when a packet is received, the RSSI along with the transmitter-reported locations and the receiver self-location could form a strong state description for the selfreported location anomaly detection.…”

Improving Indoor Localization Based on Artificial Neural Network Technology

“…In particular, the BS received the RSSI values from any of the three anchors and these signals were used to compute a dynamic distance coefficient to support the localization process. The authors in [7] proposed an RSSI real-time correction method based on the particle swarm optimization -back propagation neural network (PSO-BPNN) RSSI-distance model presented in [8].In the proposed approach, a terminal was established to collect the RSSIs of the surrounding APs and the RSSI measurements were then adjusted intelligently in realtime using RSSI fluctuation data stored on a local server. However, BPNNs fail to properly converge when presented with nonlinear separable input data [9,10].…”

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

“…Other AI studies determine the BLE RSSI values for indoor environments [21] using more specific information such as direct distance between transmitters and receivers to optimize the RSSI values. In [22] an RSSI real-time correction method based on the particle a swarm optimization-back propagation neural network is proposed, however, the method needs a gateway to collect RSSI in real time on top of the receiver measurements.…”