TOA NLOS mitigation cooperative localisation algorithm based on topological unit

Zhang, Haiyang; Qian, Wei; Liu, Lifang

doi:10.1049/iet-spr.2020.0001

Cited by 12 publications

(9 citation statements)

References 45 publications

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“…Figure 4 makes it evident, nevertheless, that all of the network's sensor nodes must synchronize their clocks [19], [36]. Many strategies have been put forth to reduce the influence of clock differences, in [37] In an effort to lower In mixed line-of-sight (LOS)/NLOS scenarios, the cooperative localization problem based on time of arrival (TOA) is addressed, along with the NLOS errors. A cooperative localization algorithm based on the topological unit is proposed for TOA NLOS mitigation, by examining the topological relationship between nodes.…”

Section: Time Of Arrival (Toa)mentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.

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Section: Time Of Arrival (Toa)mentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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“…Research of TOA-based NLOS mitigation algorithms are aimed at mitigating the effects of NLOS errors on TOA measurement. One of the studies [ 12 ] advanced the TOA NLOS mitigation cooperative localization algorithm based on a topological unit, which has higher scalability and robustness, and can obtain the target node with fewer anchor nodes. Another study [ 9 ] proposed the TOA measurements localization algorithm, which uses a semidefinite programming problem to mitigate NLOS errors without the statistical information of the NLOS error.…”

Section: Related Workmentioning

confidence: 99%

Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments

Wang

Cui

et al. 2023

Sensors

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In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.

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“…Some of these approaches are hypothesis testing [4,14], filter-based [16], feature-based (UWB signal) classifiers (machine learning (ML) approaches) [17][18][19][20] and deep learning-based [21,22]. Cooperative localization in NLOS environments is investigated in [23,24]. In general, the measured features include energy, maximum amplitude, rise time, mean excess delay, RMS delay spread, kurtosis [4], entropy, variance [25], χ 2 goodness of fit, log mean distance, and feature distribution [13].…”

Section: Identification and Mitigation In Uwbmentioning

confidence: 99%

NLOS identification and mitigation in UWB positioning with bagging-based ensembled classifiers

Rayavarapu

Mahapatro

2021

Ann. Telecommun.

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The identification and mitigation of nonline-of-sight (NLOS) paths play crucial roles in effectively localizing sensor nodes deployed in both indoor and urban outdoor environments. In this work, the NLOS identification and mitigation tasks are performed using the bagging-based ensembled classifier. The proposed method is compared to other stateof-the-art approaches, and promising results, such as higher classification accuracy, are obtained. The received signal waveform is used as raw data, and further statistical features are extracted from the channel impulse response (CIR). The classification performances are analyzed over varying feature subsets, and associated hyperparameters are validated on three datasets. The obtained results strongly suggest implementing the bagging classifier with a proper selection of features and hyperparameters.

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TOA NLOS mitigation cooperative localisation algorithm based on topological unit

Cited by 12 publications

References 45 publications

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments

NLOS identification and mitigation in UWB positioning with bagging-based ensembled classifiers

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