Abstract
The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements are not yet complete. Like any other process, localization also has security requirements. The use of ultra-wideband (UWB) indoor localization systems have recently grown quickly in industries, with a reliable, fast, and have high accuracy performances. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, as TDOA measurement errors increase, the accuracy of the localization decreases. The accuracy of the TDOA measurement is influenced the accuracy of the localization and affected by multiple factors such as time synchronization, errors in sensor positions, missing data is caused by network attack. To reduce the influence of a sensor measurement error in localization, this paper proposes an improved localization algorithm for source localization using deep learning to address a TDOA measurements error or missing data in an asynchronous localization called DeepTAL. Unlike the conventional algorithm, DeepTAL can obtain highly accurate localization data in the presence of TDOA measurement errors or missing data. The algorithm starts with TDOA measurements without time synchronization. A network based on Long Short-Term Memory (LSTM) is then applied to achieve a stronger learning and better representation of the determined target state and TDOA prediction. The network can express features more abstractly at higher levels and increase recognition accuracy. After that, the target node is accurately located by TDOAs. We implement the DeepTAL algorithm on an asynchronous localization system with UWB signals. The experiments show that the proposed DeepTAL algorithm is efficient in improving the localization precision as measurement errors or missing data.
Highlights
With the improvement of wireless technology, localization has emerged as an attractive solution, mainly in industrial settings for communication, sensing, and control of robotics using wireless networks [1]
In this paper, we presented the DeepTAL model for the time difference prediction between the target node and anchor nodes in the presence of time difference-of-arrival (TDOA) measurement errors or data missing and implemented it on a platform with UWB signals
In the TDOA range-based localization, the accuracy of TDOA measurement affected the precision of the localization, and the TDOA measurement is affected by multiple factors such as time synchronization, errors in sensor positions, data missing and so on
Summary
With the improvement of wireless technology, localization has emerged as an attractive solution, mainly in industrial settings for communication, sensing, and control of robotics using wireless networks [1]. Among the available range-based localization systems, a popular positioning approach is the time-difference-of-arrival (TDOA) approach, which uses the time difference of reception of signals received by the various sensors and the network reference sensor. The TDOA measure is affected by multiple factors such as the accuracy of synchronization, errors in sensor positions, missing data induced by communication failure or network attack and so on [5], [6]. We propose DeepTAL, an algorithm for TDOA localization with sensor measurement errors or missing data, which achieves the localization using a deep learning approach. Indoor localization has been gradually shifting to computational deep learning approaches, especially in the presence of range measurement errors or missing data. In ASync-TDOA model, we measure the time difference in a one-way-based ranging by introducing the reference node XR without time synchronization. With multiple TDOAs as well as locations of anchors or other data, we can set a TDOA-database T TDOA
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