Improving indoor positioning system using weighted linear least square and neural network Online publication date: Fri, 17-Mar-2023
by Ngoc-Son Duong; Thanh-Phuc Nguyen; Quoc-Tuan Nguyen; Thai-Mai Dinh-Thi
International Journal of Sensor Networks (IJSNET), Vol. 41, No. 2, 2023
Abstract: Indoor positioning has grasped great attention in recent years. Many of those technologies are related to the problem of determining the position of an object in space, such as the robot, people, and so on. In this paper, we combine a range-free method, i.e., fingerprinting, and a range-based method, i.e., multi-lateration, to propose a novel indoor positioning system using the received signal strength indicator (RSSI). First, we apply multi-layer perceptron neural network (MLP-NN) on a time series of RSS readings to coarsely estimate the target location. From the knowledge of the coarse location, we select reliable beacons and apply least square-based multi-lateration to their estimated distance to finely estimate the target position. We also proposed a novel weighted least square method based on uncertainty propagation to improve localisation accuracy. Experiments have shown that our proposed system, which is implemented on Raspberry Pi (RPi), is highly precise and deployable.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook