Distributed Source Localization of Multi-Agent Systems With Bearing Angle Measurements

Lin, Che; Lin, Zhiyun; Zheng, Ronghao; Yan, Gangfeng; Mao, Guoqiang

doi:10.1109/tac.2015.2457112

Cited by 37 publications

(10 citation statements)

References 20 publications

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“…As one kind of the typical distributed systems, multiagent systems have been widely used in many applications [28][29][30][31]. The PHD filter including both of the proposed DCPPHD and DSPHD can be applied for the multi-agent systems with switching network topologies and periodic sampling by an extension to the consensus PHD filter.…”

Section: Remarkmentioning

confidence: 99%

Computation-distributed probability hypothesis density filter

Wang

Zhao

et al. 2016

EURASIP J. Adv. Signal Process.

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Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularly when numerous targets and clutter exist in the surveillance region. To address this issue, we present a distributed computation particle probability hypothesis density(PHD) filter for target tracking. It runs several local decomposed particle PHD filters in parallel while processing elements. Each processing element takes responsibility for a portion of particles but all measurements and provides local estimates. A central unit controls particle exchange among the processing elements and specifies a fusion rule to match and fuse the estimates from different local filters. The proposed framework is suitable for parallel implementation. Simulations verify that the proposed method can significantly accelerate and maintain a comparative accuracy compared to the standard particle PHD filter.

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Section: Remarkmentioning

confidence: 99%

Computation-distributed probability hypothesis density filter

Wang

Zhao

et al. 2016

EURASIP J. Adv. Signal Process.

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“…Therefore, we utilize the bearing angles relative to the target in the vehicle's own coordinate, and derive an optimizationbased controller for the global position system (GPS) -denied target search problem together with the vehicle's orientation angles from a compass. Similarly, the GPS information is not required in [30], [31] to estimate the target position, whereas they employ a group of vehicles to measure the bearing angles relative to their neighbors and to communicate individual estimates with their neighbors. In particular, the communication graphs have to be connected.…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Control for Bearing-Only Target Search With a Mobile Vehicle

You

Song

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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This work aims to design an optimization-based controller for a discrete-time Dubins vehicle to approach a target with unknown position as fast as possible by only using bearing measurements. To this end, we propose a bi-objective optimization problem, which jointly considers the performance of estimating the unknown target position and controlling the mobile vehicle to a known position, and then adopt a weighted sum method with normalization to solve it. The controller is given based on the solution of the optimization problem in ties with a least-square estimate of the target position. Moreover, the controller does not need the vehicle's global position information. Finally, simulation results are included to validate the effectiveness of the proposed controller.

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“…Based on conventional methods : Localization of a stationary source based on continuous time estimation, 5,6 vision-based localization, 7 angle of arrival information, 8 bearing angle measurements, 9 time difference of arrival measurements, 10 gradient-based computation, 11 radio frequency (RF)-based geolocation techniques, 12 maximum likelihood algorithm, 13 acoustic source localization in the presence of Echo, 14 under variable speed of sound conditions, 15 and error bounds for localization with noise diversity 16 have been described.…”

Section: Introductionmentioning

confidence: 99%

Multi-unmanned aerial vehicle multi acoustic source localization

Manickam

Swar

Casbeer

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper addresses a multisource localization problem with multiple unmanned aerial vehicles equipped with appropriate sensors coordinating with each other, wherein the sources are simultaneously emitting identical acoustic signals. Distributed coordinated localization algorithms based on multiple range and direction measurements are presented and performances are evaluated in different practically significant mission scenarios. Non-deterministic polynomial (NP) hardness to determine optimal number of unmanned aerial vehicles for a given mission scenario is discussed. Group coordination, tactical path, and goal replan strategies to enable efficient localization of single and multiple acoustic sources have been designed. The localization algorithm along with coordination strategy is verified in the presence of realistic error conditions through simulation.

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Distributed Source Localization of Multi-Agent Systems With Bearing Angle Measurements

Cited by 37 publications

References 20 publications

Computation-distributed probability hypothesis density filter

Computation-distributed probability hypothesis density filter

Optimization-Based Control for Bearing-Only Target Search With a Mobile Vehicle

Multi-unmanned aerial vehicle multi acoustic source localization

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