Joint TOA and AOA Estimation for UWB Localization Applications

Taponecco, Lucio; D’Amico, Antonio A.; Mengali, U.

doi:10.1109/twc.2011.042211.100966

Cited by 154 publications

(78 citation statements)

References 25 publications

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“…[28] [29] [30] combine both TOA and AOA to localize mobile nodes in celluar networks. [31] [32] [33] analysis localization performance of AOA and TOA with UWB radio. However, to the best of our knowledge, no paper has implemented a localization system using both RSSIs and TOA like ours.…”

Section: Related Workmentioning

confidence: 99%

RoamingHART: A Collaborative Localization System on WirelessHART

Zhu

Huang

Han

et al. 2012

2012 IEEE 18th Real Time and Embedded Technology and Applications Symposium

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Abstract-Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., in-door environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this paper, we present the design and implementation of RoamingHART: a collaborative localization system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, RoamingHART adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, RoamingHART employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. RoamingHART adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI pre-survey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes. Our implementation of RoamingHART can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete RoamingHART system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.

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Section: Related Workmentioning

confidence: 99%

RoamingHART: A Collaborative Localization System on WirelessHART

Zhu

Huang

Han

et al. 2012

2012 IEEE 18th Real Time and Embedded Technology and Applications Symposium

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“…The currently available indoor location systems, such as Wi-Fi [2,3], Radiofrequency Identification (RFID) [4][5][6], Ultra-WideBand (UWB) [7][8][9][10][11], Bluetooth [12][13][14], and Ultrasound [15,16], have achieved great progress in terms of transferring data within a short range, but when it comes to the indoor environment, these systems have various problems, including high cost, high power consumption, low accuracy, and low precision. The advent of Bluetooth Low Energy (BLE) provided opportunities to overcome the above problems.…”

Section: Introductionmentioning

confidence: 99%

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

Han

Xing

Deng

et al. 2018

IJGI

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Abstract:In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments.

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“…But AOA is different from that which localizes through measuring the angle information of sending and receiving signals. While even relatively simple receivers can measure TOA with high accuracy, it is much more complex to have precise AOA measurements [3].…”

Section: Introductionmentioning

confidence: 99%