Enhanced Relative Localization Based on Persistent Excitation for Multi-UAVs in GPS-Denied Environments

She, Fujiang; Zhang, Yongjun; Shi, Dianxi; Zhou, Hao; Ren, Xiuyan; Xu, Tianqi

doi:10.1109/access.2020.3015593

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…The optimization problem for LM placement and vehicle routing is posed as an integer program, and two fast heuristics are presented to find feasible solutions. The paper [32] tackles the issue of persistent excitation-based relative localization for multi-UAVs in GPS-denied environments. It introduces synchronized sensor sample prediction and redesigns RL estimation to avoid error accumulation and achieve greater precision.…”

Section: Topology Control In Uav Swarmsmentioning

confidence: 99%

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

Gaydamaka

Samuylov

Moltchanov

et al. 2024

IEEE Trans. on Mobile Comput.

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The swarms of unmanned aerial vehicles (UAV) are nowadays finding numerous applications in different fields. While performing their missions, UAVs have to rely on external positioning information to maintain connectivity and communications between units in a swarm. However, some of the critical applications such as rescue missions are performed in locations, where this information is partially or fully not available, e.g., deep woods, mountains, indoors. In this paper, we propose a method for dynamic topology organization and maintenance in UAV swarms. In addition to the baseline functionality, we also design advanced features required for dynamic swarms merging and disjoining, making it suitable for practical applications. Specifically, the proposal is based on the virtual coordinates system allowing for the utilization of conventional geographical routing algorithms. We test the proposed algorithm in different swarm conditions to illustrate that: (i) it is insensitive to distance estimates up to at least 30% allowing for simple estimation techniques, (ii) the accuracy of the topology inference is at least 90% even under impairments caused by mobility and temporal loss of connectivity, and (iii) the impact of the developed merging algorithm for swarms lasts for multiple tens of time steps that correspond to just few seconds in practice. The set of developed algorithms can be utilized to ensure always connected topology in conditions where positioning information is partially or fully unavailable.

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Section: Topology Control In Uav Swarmsmentioning

confidence: 99%

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

Gaydamaka

Samuylov

Moltchanov

et al. 2024

IEEE Trans. on Mobile Comput.

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“…Some recent research has shown that civil UAVs can be easily deceived [11][12][13]. A simple GPS spoofing attack has been successfully implemented by researchers from Los Alamos National Laboratory [10].…”

Section: Related Workmentioning

confidence: 99%

A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

Meng

Zhang²,

Yang³

et al. 2023

Drones

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The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of GPS spoofing attacks faced by submachines has become an urgent security problem for the UAV cluster. In this paper, a GPS-adaptive spoofing detection (ASD) method based on UAV cluster cooperative positioning is proposed to solve the above problem. The specific technical scheme mainly includes two detection mechanisms: the GPS spoofing signal detection (SSD) mechanism based on cluster cooperative positioning and the relative security machine optimal marking (RSOM) mechanism. The SSD mechanism starts when the cluster enters the task state, and it can detect all threats to the cluster caused by one GPS signal spoofing source in the task environment; when the function range of the mechanism is exceeded, that is, there is more than one spoofing source and more than one UAV is attacked by different spoofing sources, the RSOM mechanism is triggered. The ASD algorithm proposed in this work can detect spoofing in a variety of complex GPS spoofing threat environments and is able to ensure the cluster formation and task completion. Moreover, it has the advantages of a lightweight calculation level, strong applicability, and high real-time performance.

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“…Russell et al [22] proposed a cooperative localization technique, where they considered GPS-denied situations as urban areas and battlefields (enemy-controlled airspace). She et al [23] also proposed a relative localization for multi-UAV networks in GPS-denied environments. The authors in [24] considered GPS-refused situations in UAV networks due to net-work jamming and GPS spoofing.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

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In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

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Enhanced Relative Localization Based on Persistent Excitation for Multi-UAVs in GPS-Denied Environments

Cited by 9 publications

References 33 publications

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

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