Optimal 3-D Landmark Placement for Vehicle Localization Using Heterogeneous Sensors

Perez-Ramirez, Javier; Borah, Deva K.; Voelz, David G.

doi:10.1109/tvt.2013.2255072

Cited by 47 publications

(23 citation statements)

References 24 publications

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“…Jourdan et al [9] used a cost function based on the square root of the trace of the FIM inverse and a coordinate-descent algorithm. Most of the other works uses the FIM determinant as a basis for the cost function [6], [10], [11], [12]. The FIM determinant expression was derived in 2D and 3D and for different range measurement modelizations.…”

Section: Related Workmentioning

confidence: 99%

Limits of trilateration-based sensor placement algorithms

Génevé

Kermorgant

Laroche

2017

2017 IEEE Sensors Applications Symposium (SAS)

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Abstract-To localize a mobile vehicle in a predefined area, it is possible to resort to a positioning system using beacons. The questions arising then are: what is the right number of beacons to deploy, and where should they be positioned to accurately localize the target. With a sensor placement algorithm, it is possible to generate a configuration of the beacons that optimize some quality criteria. However, designing the objectives to optimize is not simple, and the solution of the sensor placement algorithm may not always ensures a good localization in practice. This work evaluates the impact of the objectives of a sensor placement algorithm on different localization techniques. We show that criteria based on trilateration are not sufficient anymore when the localization is done with other techniques than trilateration. Indeed, data fusion-based localization algorithms use additional information such as odometry, and are less sensitive to poor coverage or poor beacon configurations than trilateration. Thus, designing new criteria taking into account the dynamics of the vehicle would probably improve further the placements and use less beacons for the same performance.

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Section: Related Workmentioning

confidence: 99%

Limits of trilateration-based sensor placement algorithms

Génevé

Kermorgant

Laroche

2017

2017 IEEE Sensors Applications Symposium (SAS)

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“…Minimum localization error is obtained if the anchors are placed at network boundary. Optimal deployment of these four anchors results in higher localization accuracy [64], [65]. As the anchors move towards center of the network, localization error increases.…”

Section: D) Variation Of Localization Error With Anchor Placement Planmentioning

confidence: 99%

An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

Kumar

Hegde

2015

IEEE Trans. Signal Process.

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In this paper, an efficient compartmental model for real-time node tracking over cognitive wireless sensor networks is proposed. The compartmental model is developed in a multi-sensor fusion framework with cognitive bandwidth utilization. The multi-sensor data attenuation model using radio, acoustic, and visible light signal is first derived using a sum of exponentials model. A compartmental model that selectively combines the multi-sensor data is then developed. The selection of individual sensor data is based on the criterion of bandwidth utilization. The parameters of the compartmental model are computed using the modified Prony estimator, which results in high tracking accuracies. Additional advantages of the proposed method include lower computational complexity and asymptotic distribution of the estimator. Cramer-Rao bound and elliptical error probability analysis are also discussed to highlight the advantages of the compartmental model. Experimental results for real-time node tracking in indoor environment indicate a significant improvement in tracking performance when compared to state-of-the-art methods in literature.

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“…For a fixed location of target, the sensors are placed such that |I(v,s)| is maximized at that location. Accordingly, we have [11].…”

Section: B Flip Ambiguitymentioning

confidence: 99%

Fast range-based localization of targets using particle swarm optimization

Viswanathan

Jana

Swarup³

2015

2015 International Conference on Control, Automation and Robotics

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Target localization is an active area of research which has several applications in the fields of robotics, defense and geology. In this paper, our goal is to localize a target based on range measurements obtained using a network of sensors scattered in the 3D continuum. To this end, we make use of the biologically inspired particle swarm optimization (PSO) algorithm. In this context, we propose a novel modification of PSO algorithm that leads to faster convergence, and eliminates the flip ambiguity encountered by coplanar sensors. Our initial results over several simulation runs highlight the accuracy and speed of the proposed approach. This paper also proposes a statistical approach to optimally place a given set of sensors such that the localization error is minimized over certain trajectories of the target. The optimal locations of the sensors are estimated using the Cramer-Rao lower bound (CRLB) as the cost function.

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Optimal 3-D Landmark Placement for Vehicle Localization Using Heterogeneous Sensors

Cited by 47 publications

References 24 publications

Limits of trilateration-based sensor placement algorithms

Limits of trilateration-based sensor placement algorithms

An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

Fast range-based localization of targets using particle swarm optimization

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