UAV Path Planning for Passive Emitter Localization

Doğançay, Kutluyıl

doi:10.1109/taes.2012.6178054

Cited by 150 publications

(114 citation statements)

References 23 publications

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“…The emitted, from the targets, signals a ρ (t) could correspond to communication radio signals that possibly the targets are transmitting, e.g., targets could correspond to cell phone users moving in an area or unnamed aerial/ground vehicles or military vehicles that move within the monitored area and need to be tracked, see e.g., [16]. The deployed sensors are listening for these signals to track the moving entities.…”

Section: Problem Settingmentioning

confidence: 99%

“…Thus, the communication radio signals that the targets may be emitting are utilized to perform tracking and move the sensors appropriately. Applications include localization and tracking of mobile users in wireless networks, as well as tracking of vehicles in tactical environments [16].…”

Section: Problem Settingmentioning

confidence: 99%

“…A path planning strategy for a setting involving a fixed-location target and a single moving sensor is designed in [15] by maximizing the determinant of the Fisher information matrix corresponding to the configuration. In [16], an approach is proposed for controlling the trajectories of multiple UAVs by minimizing the localization uncertainty for a fixedlocation target setting where the target is emitting a radio signal. The work in [17] rigorously presents how sensor mobility can increase spatial resolution when tracking a target with mobile sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The novelties of the proposed framework with respect to existing work involves the following: (i) utilization of covariance sparsity and properly designed kinematic rules to perform dynamic spatio-temporal sensor-to-target association and tracking using mobile sensors not present in [11,23,24]; (ii) different from existing approaches [12][13][14], it enables only the sensors acquiring informative measurements about the targets to move instead of moving all sensors to track a single target; (iii) different from [11], it does not rely on probabilistic models or state estimates to associate sensors with targets; (iv) it takes local sensor network connectivity into account to ensure that the tracking process carries out continuously; and (v) different from [15,16] that consider fixed-location target(s), here, kinematic strategies are developed for tracking multiple moving targets. The proposed approach here achieves this task only by using the sensor data and sparsity-imposing mechanisms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Ren

Maroulas

Schizas

2016

EURASIP J. Adv. Signal Process.

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The problem of distributed tracking of multiple targets is tackled by exploiting sensor mobility and the presence of sparsity in the sensor data covariance matrix. Sparse matrix decomposition relying on norm-one/two regularization is integrated with a kinematic framework to identify informative sensors, associate them with the targets, and enable them to follow closely the moving targets. Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering. Different from existing approaches which force all sensors to move, here, local updating recursive rules are obtained only for the target-informative sensors that can update their location and follow closely the corresponding targets while staying connected. Simulations advocate that the proposed scheme outperforms alternative tracking schemes while accurately tracks multiple targets by imposing mobility only on the target-informative sensors.

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Section: Problem Settingmentioning

confidence: 99%

Section: Problem Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Ren

Maroulas

Schizas

2016

EURASIP J. Adv. Signal Process.

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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%

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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Fixed‐time disturbance observer based distributed cooperative containment control with fixed‐time anti‐saturation compensator for multi‐unmanned aerial vehicles

Cui

Zhou

Huang

2023

Intl J Robust & Nonlinear

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This article proposes a fixed‐time backstepping distributed cooperative containment control scheme for multiple unmanned aerial vehicles (UAVs). A fixed‐time compensator is designed for the fixed‐wing UAVs actuator saturation problem. Based on this, an improved fixed‐time disturbance observer is proposed for the problem of the excessive initial peak. The problem of “complexity explosion” is solved by the fixed‐time differential filter. For the distributed containment control of multiple UAVs, a cooperative control architecture based on a fixed‐time anti‐saturation compensation system is proposed. The proposed algorithm can be shown practicable by using Lyapunov stability theory and graph theory. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.

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UAV Path Planning for Passive Emitter Localization

Cited by 150 publications

References 23 publications

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

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

Fixed‐time disturbance observer based distributed cooperative containment control with fixed‐time anti‐saturation compensator for multi‐unmanned aerial vehicles

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