Optimal Sensor Placement for Agent Localization

Jourdan, Damien; Roy, Nicholas

doi:10.1109/plans.2006.1650596

Cited by 55 publications

(60 citation statements)

References 21 publications

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“…Among various approaches for obtaining location information, time-based techniques are quite popular due to their accuracy and practicality [9], [10]. The main factors that affect the performance of time-based localization systems are accuracy of time delay estimation, the number of localization devices and their positions, and channel conditions, such as multipath and non-line-of-sight propagation [9], [11]- [13]. In this paper, theoretical limits on time delay estimation are studied for dispersed spectrum cognitive radio systems.…”

Section: Introductionmentioning

confidence: 99%

Fundamental limits on time delay estimation in dispersed spectrum cognitive radio systems

Gezici

Celebi

Poor

et al. 2009

IEEE Trans. Wireless Commun.

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Abstract-In this paper, fundamental limits on time delay estimation are studied for cognitive radio systems, which facilitate opportunistic use of spectral resources. First, a generic CramerRao lower bound (CRLB) expression is obtained in the case of unknown channel coefficients and carrier-frequency offsets (CFOs) for cognitive radio systems with dispersed spectrum utilization. Then, various modulation schemes are considered, and the effects of unknown channel coefficients and CFOs on the accuracy of time delay estimation are quantified. Finally, numerical studies are performed in order to verify the theoretical analysis.Index Terms-Dispersed spectrum utilization, time delay estimation, Cramer-Rao lower bound (CRLB), cognitive radio, carrier frequency offset (CFO).

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Section: Introductionmentioning

confidence: 99%

Fundamental limits on time delay estimation in dispersed spectrum cognitive radio systems

Gezici

Celebi

Poor

et al. 2009

IEEE Trans. Wireless Commun.

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“…Jourdan and Roy [11] consider a fixed set of possible target positions. They place sensors on the walls of buildings to minimize the average position error bound in the sensor network.…”

Section: Related Workmentioning

confidence: 99%

Effective landmark placement for accurate and reliable mobile robot navigation

Beinhofer

Müller

Burgard

2013

Robotics and Autonomous Systems

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Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulation and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.

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“…For example, Jourdan et al [4] considered the case of deploying a network of static sensors that provide range measurements to the agent for localization. Most notably, they developed a locally optimal algorithm, significantly outperformed Simulated Annealing, to position the sensors on the boundaries of buildings to minimize the average position error bound over multiple agent locations.…”

Section: Introductionmentioning

confidence: 99%

“…The sensor deployment problem considered in this paper is most similar to those considered in [4] and [5]. The problem involves a vehicle attempting to follow a preplanned trajectory through the environment.…”

Section: Introductionmentioning

confidence: 99%

“…The problem involves a vehicle attempting to follow a preplanned trajectory through the environment. Although [4] briefly touched on incorporating uncertainty, the proposed work presents a unifying framework for handling uncertainty of the vehicle's execution of the pre-planned trajectory. To increase the vehicle's likelihood of successfully following the trajectory and reaching its goal location, sensors are deployed in the environment to provide the vehicle with measurements when within range.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Placement for Improved Robotic Navigation

Vitus

Tomlin

2010

Robotics: Science and Systems VI

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Abstract-Consider a set of fixed sensors used to estimate the state of a vehicle (e.g. position, orientation and velocity) while it attempts to follow a pre-planned trajectory. Since the sensor can only provide a measurement to the vehicle when it is within range, the deployment of the sensors will have a major impact on the ability of the vehicle to follow the trajectory. The problem addressed here is to optimally place the sensors in the environment such that the weighted function of the estimation error at each time-step is minimized. An optimization formulation is proposed that accounts for the uncertainty of the vehicle's state in determining whether it can receive a measurement from a sensor. A confidence level is introduced as a tuning parameter that controls the conservativeness of the solution. Consequently, the resulting solution increases the likelihood of the vehicle successfully following its intended trajectory. Finally, due to the interdependence between the sensors' positions, a novel incremental optimization algorithm is presented which significantly outperforms a standard nonlinear optimization procedure. Experimental and simulation results are shown which characterize the performance of the proposed algorithm.

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Optimal Sensor Placement for Agent Localization

Cited by 55 publications

References 21 publications

Fundamental limits on time delay estimation in dispersed spectrum cognitive radio systems

Fundamental limits on time delay estimation in dispersed spectrum cognitive radio systems

Effective landmark placement for accurate and reliable mobile robot navigation

Sensor Placement for Improved Robotic Navigation

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