Optimal path planning for DRSSI based localization of an RF source by multiple UAVs

Dehghan, Seyyed Mohammad Mehdi; Moradi, Hadi; Shahidian, Seyyed Ali Asghar

doi:10.1109/icrom.2014.6990961

Cited by 21 publications

(15 citation statements)

References 22 publications

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“…In noiseless environments, the RSS at UAV i in decibel (dB) subjected to the source at time step k (Pr i k ) is given by (1) in which Pt k indicates the transmit power in dB at time step k,…”

Section: Rss-based Localizationmentioning

confidence: 99%

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Single- and Multi-UAV Trajectory Control in RF Source Localization

Shahidian

Soltanizadeh

2016

Arab J Sci Eng

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In this paper, two trajectory control approaches are presented for any number of unmanned aerial vehicles (UAVs) in radio frequency (RF) source localization. The UAVs observe the received signal strength (RSS) in distinctive time intervals to localize a stationary RF source. The location of the source is estimated recursively applying the extended Kalman filter. The objective of the optimal trajectory control is to steer the UAVs to the locations which minimize the uncertainty about the target state. The Fisher information matrix (FIM) is inversely proportional to the estimation variance. Since the true target state is unknown, the FIM is approximated by the estimated target state. Two criteria based on the approximated FIM are applied to measure the information content of the observations to optimize the UAV waypoints: The D-optimality and the A-optimality. The objective of the present paper is to propose two trajectory control approaches for any number of UAVs in RSS-based localization to increase the target localization accuracy. The superiority of the trajectory optimization approach based on the D-optimality in terms of mean squared error is illustrated through simulation examples.

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Section: Rss-based Localizationmentioning

confidence: 99%

“…Passive localization of radio sources such as mobile phones is consistently used in variety of applications, e.g., search and rescue and surveillance [1][2][3][4]. Aerial localization is highly recommended in avalanches, mountains, forests, etc.…”

Section: Introductionmentioning

confidence: 99%

Single- and Multi-UAV Trajectory Control in RF Source Localization

Shahidian

Soltanizadeh

2016

Arab J Sci Eng

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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%

“…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. Authors in [10] and [11] tracked the targets using extended Kalman Filter (EKF) with different states of targets, i.e., static and dynamic.…”

Section: A Uav Based Multi-target Positioningmentioning

confidence: 99%

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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“…and is accomplished by taking a training data (SS collected from known locations), by fitting these data into a model using linear regression the unknown parameters are estimated [16].…”

Section: Estimation Of Environmental Parametersmentioning

confidence: 99%