Abstract
In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI) method for positioning and tracking applications using strapdown inertial navigation system (SINS) and wireless sensor network (WSN). The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR) algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS), based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.
Highlights
In the past several years, mobile target localization in an enclosed environment has received a lot of attention in many fields, such as indoor pedestrian navigation [1], coal mine automation [2], mobile robot navigation, and other fields
Anchor nodes detect the wireless signal, which is transmitted by mobile node; the wireless signal is liable to be influenced by the barrier, floor, and ceiling through multipath wireless channels and non-line-of-sight (NLOS) [3]
If the number of anchor nodes that have received the wireless signal from the mobile node is less than four on a two-dimensional plane, the wireless sensor network (WSN) cannot solve a final position result using the time of arrival (TOA) model
Summary
In the past several years, mobile target localization in an enclosed environment has received a lot of attention in many fields, such as indoor pedestrian navigation [1], coal mine automation [2], mobile robot navigation, and other fields. Yang [11] proposed a fuzzy adaptive Kalman filter positioning system based on INS and WSN integration to estimate the position of a mobile target indoors. The above integrated positioning systems based on a tightly-coupled integration scheme utilize the differences between the distances from the mobile node to the anchor nodes measured by SINS and those measured by WSN. As for the tightly-coupled integrated positioning model, the statistical covariance of observation noise can reflect the measured accuracy for the distance between anchor node and mobile node. Aiming at different measured accuracies for WSN, this paper proposed a fuzzy adaptive tightly-coupled integration (FATCI) positioning system based on the distance statistical covariance matrix with a fuzzy adaptive Kalman filter (FAKF).
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