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Sensors 2013, 13(8), 10386-10417; doi:10.3390/s130810386

Sensor Networks for Optimal Target Localization with Bearings-Only Measurements in Constrained Three-Dimensional Scenarios

1
Department of Computer Science and Automatic Control, National University Distance Education (UNED), Juan del Rosal 16, Madrid 28040, Spain
2
Institute for Systems and Robotics (ISR), Instituto Superior Tecnico (IST), Univ. Lisboa, Av. Rovisco Pais 1, Lisbon 1049-001, Portugal
*
Author to whom correspondence should be addressed.
Received: 4 July 2013 / Revised: 6 August 2013 / Accepted: 7 August 2013 / Published: 12 August 2013
(This article belongs to the Section Sensor Networks)
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Abstract

In this paper, we address the problem of determining the optimal geometric configuration of an acoustic sensor network that will maximize the angle-related information available for underwater target positioning. In the set-up adopted, a set of autonomous vehicles carries a network of acoustic units that measure the elevation and azimuth angles between a target and each of the receivers on board the vehicles. It is assumed that the angle measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Using tools from estimation theory, the problem is converted into that of minimizing, by proper choice of the sensor positions, the trace of the inverse of the Fisher Information Matrix (also called the Cramer-Rao Bound matrix) to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. It is shown that the optimal configuration of the sensors depends explicitly on the intensity of the measurement noise, the constraints imposed on the sensor configuration, the target depth and the probabilistic distribution that defines the prior uncertainty in the target position. Simulation examples illustrate the key results derived.
Keywords: position estimation; positioning systems; estimation theory; localization; information analysis; optimization; autonomous vehicles; sensor networks position estimation; positioning systems; estimation theory; localization; information analysis; optimization; autonomous vehicles; sensor networks
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Moreno-Salinas, D.; Pascoal, A.; Aranda, J. Sensor Networks for Optimal Target Localization with Bearings-Only Measurements in Constrained Three-Dimensional Scenarios. Sensors 2013, 13, 10386-10417.

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