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.

Localization has emerged as an attractive solution to enable new business models that rely on location-based services in wireless networks for communication, sensing, and control. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, the conventional TDOA requires precise time synchronization between a target node and anchor nodes for measuring the time difference, which leads to a large number of packets for communication. To reduce packet transmission, we propose a model for measuring the time difference without time synchronization called ASync-TDOA. Different from the conventional model, ASync-TDOA can measure the time difference in a one-way-based ranging by introducing the reference node. Specifically, the time difference between the target node and anchor nodes can be directly measured by the server based on the timestamps from the reference node. After that, the target node is accurately located using least squares and brute force for the time difference. We implement the ASync-TDOA model on a localization system with ultra-wideband signals to estimate the localization of the target node, which is easy to operate in practical engineering. The experiments show that the proposed ASync-TDOA is efficient in reducing the packet transmissions and improving the TDOA measurement and localization precision.

Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of target node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing target nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor nodes. In addition, an approach for target area determination is proposed, which can approximately determine the region of the target node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the target node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing target nodes.

Industrial Internet has become an important driving force for intelligent manufacturing, and indoor positioning technology has provided important location information for the monitoring of industrial production. However, the industrial environment is relatively complicated. There are a large number of industrial noises and industrial equipments, which easily cause interference to wireless positioning signals and affect positioning accuracy. TDOA (Time Difference-of-Arrival) is the current mainstream industrial indoor positioning technology, but it still has some problems of packet loss and floating positional coordinates. To resolve these problems, we propose an optimization scheme based on the asynchronous TDOA positioning algorithm model, which improves the positioning accuracy to some extent. Firstly, the positioning data of the unknown node are classified to determine whether the movement occurs, and further provides more accurate characteristic data for the TDOA prediction. Secondly, the positioning data are judged whether or not prediction processing is required. The experiments show that the optimization scheme can effectively improve the positioning accuracy and solve the problem of positioning data loss.