A Train-Once Approach for Location Estimation Using the Directional Propagation Model

Ng, Joseph Kee‐Yin; Zhou, Junjie; Chu, Kenneth Man-Kin; Leung, Kwok Wa

doi:10.1109/tvt.2007.912602

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Traditional train positioning technology mainly includes track circuit, query/balise, and speed measurement positioning, as well as positioning based on odometer cumulative ranging. These positioning methods often have the problems of single structure, low positioning accuracy and error accumulation, which can not meet the increasingly complex system structure and high-speed running of trains [3][4][5]. With the continuous development of GPS, GLONASS and the Beidou satellite, navigation technology is applied to the field of train positioning to achieve high precision, real-time and strong continuous self-positioning of the train, which can greatly reduce the use of ground equipment, cost of construction and operation, and improve the efficiency of train operation [6].…”

Section: Introductionmentioning

confidence: 99%

The Assisted Positioning Technology for High Speed Train Based on Deep Learning

Song

Wen

2020

Applied Sciences

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In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

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Section: Introductionmentioning

confidence: 99%

The Assisted Positioning Technology for High Speed Train Based on Deep Learning

Song

Wen

2020

Applied Sciences

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“…Routers such that our software can collect signal strength readings when wireless devices, especially those hacker devices, that are communicating with any of the surrounding APs. [5] With these signal strength readings collected in real-time and stored in the data store at our data centre, together with the known positions of the APs, we can reuse our previous algorithms [6][7][8][9][10][11][12] to find out the whereabouts of the hacker devices.…”

Section: Introductionmentioning

confidence: 99%

Detecting, Locating, and Tracking Hacker Activities within a WLAN Network

Shum

2010

2010 IEEE 16th International Conference on Embedded and Real-Time Computing Systems and Applications

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1With a good location estimation integrated into a Wi-Fi surveillance system, system administrator can closely monitor the network traffic as well as the behavior of the mobile users. Hence, there is a growing demand to have a quick and efficient way to indentify a specific group of people, or devices or asset within a controlled wireless network. In our proposed system, all the Wi-Fi traffic and information especially the MAC addresses and RSSI from the mobile clients (i.e. Wi-Fi devices) can be sniffed by an open-source Wi-Fi access point (AP) with custom-made embedded software program and without preloading any client program on the mobile user devices. These sniffed information is then analyzed and stored in a database which will help network administrator to monitor the wireless network for surveillance purpose and security concerns. In summary, this paper proposes a wireless LAN system that can detect, locate and track down wireless communication within the system by modifying the embedded software in off-the-shelf WLAN APs. Experiment results have shown that abnormal wireless activities can be detected and by our signal strength based localization algorithm, positions of these wireless mobile devices can be identified and be tracked within meters inside our WLAN system.

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A Signal Strength Based Location Estimation Algorithm within a Wireless Network

Shum

Cheng

et al. 2011

2011 IEEE International Conference on Advanced Information Networking and Applications

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A Train-Once Approach for Location Estimation Using the Directional Propagation Model

Cited by 5 publications

References 10 publications

The Assisted Positioning Technology for High Speed Train Based on Deep Learning

The Assisted Positioning Technology for High Speed Train Based on Deep Learning

Detecting, Locating, and Tracking Hacker Activities within a WLAN Network

A Signal Strength Based Location Estimation Algorithm within a Wireless Network

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