Centralized path planning for unmanned aerial vehicles with a heterogeneous mix of sensors

Doğançay, Kutluyıl; Hmam, Hatem; Drake, Samuel Picton; Finn, Anthony

doi:10.1109/issnip.2009.5416851

Cited by 11 publications

(23 citation statements)

References 8 publications

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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].…”

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%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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 all [5]- [9] it is assumed that there is prior information about the approximate location of the transmitter, and that receivers can lie in any place around the source. Researchers, in [10] and [11], presented two different methods of path planning for Unmanned Aerial Vehicles (UAV) with heterogeneous mixture of sensors for passive localization while the UAVs encountered connectivity constraints in their flight path and the emitter was assumed to be fixed during the measurements. Following the above mentioned studies, the purpose of this work has been set to find optimum sensor trajectories when the source is moving continuously and the receivers are to be kept in a bounded region.…”

Section: Introductionmentioning

confidence: 99%

Constrained optimization of sensors trajectories for moving source localization using TDOA and FDOA measurements

Adelipour

Hamdollahzadeh

Behnia

2015

2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM)

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This paper examines the problem of determining optimal sensors trajectories for localization of a moving radio source based on Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements in situations in which sensors are constrained both in their movements and regions of operation. By considering the movement of the source and constrained movement of the sensors, a constraint problem is formed which is solved to determine optimal trajectories of the sensors for source tracking. The validity of the proposed algorithm is assessed by two different simulation scenarios and the results verify its proper operation with estimation error decreasing in consecutive steps.

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“…In the aerial localization with multiple UAVs and a single stationary emitter, which is considered in this paper, the waypoints of UAVs are selected in such a way that the relative geometry of the estimated location of the target and the waypoints to be optimal. The criterion which is used in several papers for this optimization is Cramer-Rao lower bound (CRLB) of the estimation [16][17][18][19]. The CRLB is the inverse of Fisher Information Matrix and shows the uncertainty region of the localization.…”

Section: Introductionmentioning

confidence: 99%

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

Dehghan

Moradi

Shahidian

2014

2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

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The Radio Frequency (RF) source localization accuracy depends not only on the measurement performance of sensors, but also on the relative location of the sensors and the source. This paper investigates the impact of UAVs, equipped with RSSI sensors, formation and trajectory on the aerial RF source localization performance for Differential Received Signal Strength Indication (DRSSI) based approach in None Line Of Sight (NLOS) propagation condition. To eliminate the need for knowing the power of the signal source the DRSSI approach is applied. The collected measurements in each waypoint are used to estimate the location of the source iteratively by the use of Extended Kalman Filter (EKF). The Cramer-Rao Lower Bound (CRLB), which expresses a lower bound on the variance of any unbiased estimator, is used as the objective function for the proposed algorithm. Due to the complexity of Jacobin calculation to perform global CRLB optimization, the local values of CRLBs in the current waypoint and the next probable waypoints are used to determine the best path. Maximizing the determinant of the inverse of CRLB, i.e. the Fisher Information Matrix, in each measurement instance over the next probable waypoint, minimizes the estimation uncertainty area. The effectiveness of the proposed algorithm is illustrated by Monte-Carlo simulations and compared with the basic bio-inspired approach of going toward the estimated direction of the source.

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Centralized path planning for unmanned aerial vehicles with a heterogeneous mix of sensors

Cited by 11 publications

References 8 publications

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

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

Constrained optimization of sensors trajectories for moving source localization using TDOA and FDOA measurements

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

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