Autonomous trajectory control for limited number of aerial platforms in RF source localization

Shahidian, Seyyed Ali Asghar; Soltanizadeh, Hadi

doi:10.1109/icrom.2015.7367877

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…To complete the task of positioning, some works use UAVs equipped with RSSI sensors to track the targets. Assuming the transmission power of target to be unknown, authors in [9]- [11] tracked the target with different estimation methods. Based on the theory of Differential Received Signal Strength Indication (DRSSI), Dehghan et al [9] compared three different estimation filters to track single target without considering the scenario of multiple targets.…”

Section: A Uav Based Multi-target Positioningmentioning

confidence: 99%

“…Based on the theory of Differential Received Signal Strength Indication (DRSSI), Dehghan et al [9] compared three different estimation filters to track single target without considering the scenario of multiple targets. Authors in [10] and [11] tracked the targets using extended Kalman Filter (EKF) with different states of targets, i.e., static and dynamic. However, they all ignored the energy consumption of the UAVs and can track only one target at a time.…”

Section: A Uav Based Multi-target Positioningmentioning

confidence: 99%

“…UAVs track the signals of the targets to locate them in many current works [10]- [14]. Authors in [10], [11] tracked targets by using single UAV flying towards them. Instead of flying towards targets, a few sensors or UAVs adopted angle of arrival (AOA) and RSSI methods to locate targets in [12], [14].…”

Section: Introductionmentioning

confidence: 99%

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Fair-Energy Trajectory Planning for Multi-Target Positioning Based on Cooperative Unmanned Aerial Vehicles

Dong

Zhu

et al. 2020

IEEE Access

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Owing to the flexibility and low cost, cooperative Unmanned Aerial Vehicles(UAVs) have been attractive in multi-target positioning recently. Although it is popular and easy to accomplish, positioning based on trilateration method still faces challenges under scenarios with multiple UAVs. First, large accumulated errors will be brought if a single UAV is used to perform trilateration on same targets. Second, due to the mobility of targets, the time interval between UAVs performing twice successive distance measurement on one target cannot be long for positioning precision. Finally, the limited energy provided by onboard battery limits the time for UAVs to perform tasks. Once the energy used by some of the UAVs reaches limitation, the whole positioning mission will fail. Thus, to complete the mission of locating multiple targets, this paper is intended to minimize the maximum energy consumption among all UAVs. We formulate the problem, and decompose it into two subproblems, one of which plans the routes for UAV groups and the other plans the routes for UAVs in a group. To solve the first subproblem, a heuristic algorithm called adjusted genetic algorithm (AGA) is proposed to plan trajectories for all UAV groups under constraints on maximum energy consumption. To guarantee stable performance and reduce computation complexity, we propose an approximation algorithm, Tree Decomposition united with Christofides Algorithm (TDCA), and the approximation ratio is proved to be (3 * N /(2 * (N − 1))), where N denotes the number of UAV groups. For the second subproblem, a two-step greedy heuristic algorithm is proposed to plan trajectories for UAVs in same groups. Extensive simulations show that compared to existing algorithms, the proposed algorithms can reduce up to 26.6% maximum and 26.3% average energy consumption.

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Section: A Uav Based Multi-target Positioningmentioning

confidence: 99%

Section: A Uav Based Multi-target Positioningmentioning

confidence: 99%

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Fair-Energy Trajectory Planning for Multi-Target Positioning Based on Cooperative Unmanned Aerial Vehicles

Dong

Zhu

et al. 2020

IEEE Access

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show abstract

“…By fusing real‐time RSSI information, the estimation accuracy and robustness of UAV flight state and directional antenna headings can be significantly improved in GPS unstable and denied environment. A few studies have been conducted on RSSI measurement and RSSI‐based localisation [15, 16]. For instance, an RSSI‐based UAV trajectory control algorithm was developed in [15] to localise a stationary source.…”

Section: Introductionmentioning

confidence: 99%

“…A few studies have been conducted on RSSI measurement and RSSI‐based localisation [15, 16]. For instance, an RSSI‐based UAV trajectory control algorithm was developed in [15] to localise a stationary source. However, the modelling of directional RSSI and the use of it to directly inform real‐time decentralised control have not been explored per knowledge of the authors.…”

Section: Introductionmentioning

confidence: 99%