Optimal trajectories for two UAVs in localization of multiple RF sources

Shahidian, Seyyed Ali Asghar; Soltanizadeh, Hadi

doi:10.1177/0142331214566026

Cited by 25 publications

(18 citation statements)

References 27 publications

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“…There are various RF signal localization methods based on signal parameters such as received signal strength indication (RSSI) [17], time-of-arrival (TOA) [18]. Additionally, UAV navigation strategies have been proposed in order to minimize localization errors [19], [20]. However, these were designed for searching moving signal emitting targets.…”

Section: Introductionmentioning

confidence: 99%

Trilateration-Inspired Sensor Node Position Estimation for UAV-Assisted Microwave Wireless Power Transfer

Shigeta

Suzuki

Okuya

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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: Unmanned aerial vehicle (UAV)-assisted microwave wireless power transfer (WPT) enables the deployment of radio frequency (RF)-power-driven, battery-less sensor nodes in rural areas such as farm fields. Unlike in urban areas, suitable ambient radio waves for RF energy harvesting are not available in rural areas, therefore, we propose using UAVs to carry active RF sources. The UAVs will cover large fields and function as movable power feeders. To feed power to sensor nodes or any power feeding target efficiently, a UAV needs to be navigated to the desired power feeding point, which is usually right above the target; however, a Global Positioning Services (GPS) system is not accurate enough for this purpose. Therefore, this paper presents two trilateration-inspired sensor node position estimation methods for UAV WPT, based on the relationship between the sensor-UAV distance and power transmission efficiency. After the UAV collects data of several distances, measured by the sensor node from different UAV locations, the UAV then estimates the position of a sensor node by utilizing the data. In the direction-based approach, circles with the radius set as the measured distance are first centered at the measurement positions. Then, all the intersections of the two circles are calculated. Further, by relying on the assumption that the sensor node would be in the direction where the largest number of intersections is observed, the average position of the intersections included in the direction is regarded as the estimated position of the sensor. In the least squares approach, the position that minimizes the sum of squares of errors, obtained from the measurement results, is assumed to be the sensor position. By comparing the direction-based and least squares approaches to the conventional hill climbing method, we found that the least squares and direction-based approaches can complete power feeding faster in average by 52% and 26%, respectively, compared to the hill climbing method. Combining GPS with our least squares approach will enable the UAV to reach the appropriate zone rapidly and complete the power supply process quickly so that power may be delivered to more sensor nodes in less time.

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Section: Introductionmentioning

confidence: 99%

Trilateration-Inspired Sensor Node Position Estimation for UAV-Assisted Microwave Wireless Power Transfer

Shigeta

Suzuki

Okuya

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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“…Mounting cameras on the UAVs provide the required information from images and allow the UAVs to complete autonomous missions [17][18][19][20]. Also, the UAVs equipped with RF sensors have been consistently applied in RF source localization missions [21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Maximizing the localization accuracy through optimizing the localization geometry is deeply investigated in literature [24][25][26][27][28][29][30][31]. Optimization of sensor-target geometry has been investigated in localization of a stationary target applying stationary sensors [24][25][26] or moving sensors [29,30]. Analysis of the optimal geometry in aerial localization with moving platforms leads to the trajectory control problem.…”

Section: Introductionmentioning

confidence: 99%

“…Some real-valued functions defined on the position error covariance could be applied in controlling the trajectories of the UAVs [30,31]. The FIM is inversely proportional to the location estimation variance which has been consistently applied as the observer path control objective function [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed trajectory control approach in [29] steers two UAVs in localization of multiple stationary RF emitters. At each time step the state of the targets are updated and the determinant of the approximated FIM is maximized to specify the next waypoints of the UAVs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Shahidian

Soltanizadeh

2016

Arab J Sci Eng

Self Cite

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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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A geometrical closed form solution for RSS based far-field localization: Direction of Exponent Uncertainty

Uluskan

Filik

2017

Wireless Netw

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Optimal trajectories for two UAVs in localization of multiple RF sources

Cited by 25 publications

References 27 publications

Trilateration-Inspired Sensor Node Position Estimation for UAV-Assisted Microwave Wireless Power Transfer

Trilateration-Inspired Sensor Node Position Estimation for UAV-Assisted Microwave Wireless Power Transfer

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

A geometrical closed form solution for RSS based far-field localization: Direction of Exponent Uncertainty

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