Scalability, Real-Time Capabilities, and Energy Efficiency in Ultra-Wideband Localization

Tiemann, Janis; Wietfeld, Christian

doi:10.1109/tii.2019.2892727

Cited by 33 publications

(18 citation statements)

References 25 publications

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“…Furthermore, the uncertainty in the drone position introduces additional errors in estimating the node position. In our work, considering all these real-world effects we obtain a sub-meter precision for the node position [13].…”

Section: Related Workmentioning

confidence: 99%

“…Multi-lateration is a method to determine the location of an object by knowing its distance to other known positions in space and its application to UWB measurements is widely investigated in literature [23], [24], [25], [13]. In [23] it is proposed a range-based multi-lateral measurement localization method (RB-MML).…”

Section: Related Workmentioning

confidence: 99%

“…Multi-lateration is a navigation technique, which uses at least three distance measurements from fixed and known positions (i.e., anchors) to estimate the location of a moving object. However, in contrast to this conventional scenario [13], this work addresses the dual problem, where the UAV behaves like a moving anchor [14], [15] to estimate the position of the nodes imprecisely deployed in its surrounding.…”

Section: Introductionmentioning

confidence: 99%

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An Energy-efficient Localization System for Imprecisely Positioned Sensor Nodes with Flying UAVs

Niculescu

Magno

Palossi

et al. 2020

2020 IEEE 18th International Conference on Industrial Informatics (INDIN)

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This work investigates the capability of unmanned aerial vehicles (UAVs) to find and communicate with wireless sensor nodes positioned at unknown locations. In this scenario, the UAV acts as a mobile gateway that estimates the sensor node position using multiple ultra-wideband (UWB) range measurements, before flying in its vicinity to perform energy-efficient data acquisition. In addition to UWB, we use wake-up radio (WUR) to improve the sensor node's energy efficiency, keeping it in the always-on "low-activity" state when the drone is not nearby. The paper proposes a localization algorithm that consists of an iterative, noise-robust and computationally lightweight approach based on multi-lateration. Experimental evaluations performed on synthetic data demonstrate that our approach achieves a submeter localization accuracy using only three range measurements. We confirm this with an extensive in-field evaluation. The multilateration algorithm runs in 4 ms, in low power microcontrollers such as the ARM Cortex-M4F. The WUR and our energy-efficient algorithm enable the sensor node to consume only 31 mJ during the whole localization-acquisition process. Our solution can be introduced in many other industrial applications where a mobile robot needs to estimate the location of imprecisely positioned objects.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Energy-efficient Localization System for Imprecisely Positioned Sensor Nodes with Flying UAVs

Niculescu

Magno

Palossi

et al. 2020

2020 IEEE 18th International Conference on Industrial Informatics (INDIN)

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show abstract

“…In our system, all the wireless nodes are equipped with UWB wireless technologies. IEEE 802.15.4a-2015 provides a UWB PHY layer based on impulse radio [34], which allows precision ranging and is very robust even at low transmission powers. In addition to the PHY layer, the MAC layer strongly impacts the performance of the system.…”

Section: System Modelmentioning

confidence: 99%

RMLNet—A Reliable Wireless Network for a Multiarea TDOA-Based Localization System

Yuan

Yang

et al. 2019

Sensors

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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.

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“…Several RF technologies, such as Bluetooth low energy (BLE) [ 11 ], ultra-wideband (UWB) [ 12 ], Wi-Fi [ 13 ], and cellular [ 14 ], have been used in indoor localization. Among these technologies, the BLE technology is designed for short-range wireless communication with low energy consumption, low cost, and easy deployment.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Feature Extraction for Indoor Localization

Jiang

Subakti

Liang

2021

Sensors

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This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.

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Scalability, Real-Time Capabilities, and Energy Efficiency in Ultra-Wideband Localization

Cited by 33 publications

References 25 publications

An Energy-efficient Localization System for Imprecisely Positioned Sensor Nodes with Flying UAVs

An Energy-efficient Localization System for Imprecisely Positioned Sensor Nodes with Flying UAVs

RMLNet—A Reliable Wireless Network for a Multiarea TDOA-Based Localization System

Fingerprint Feature Extraction for Indoor Localization

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