A Refined Localization Method for Underwater

A Refined Localization Method for Underwater - This full...

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Abstract — An accurate localization scheme is essential to many underwater sensor applications. However, due to the persistent existence of uncertainties and measurement errors, an accurate localization is very difficult to achieve. The communication cost is much higher in underwater networks compared to terrestrial networks and this calls for more accurate localization schemes even if they involve more computational burden. In the paper, a scheme based on minimum mean absolute error (MMAE) is introduced and extensive simulation results are presented to compare this and the commonly used minimum mean squared error (MMSE) method. Both uniform error distribution and normal error distribution are considered. Our results indicate that MMAE clearly result in better localization accuracy when compared to MMSE. Index Terms — localization, minimum mean absolute error (MMAE), minimum mean squared error (MMSE) I. INTRODUCTION UNDERWATER tetherless sensor networks have attracted significant research interest in recent years due to their potential applications in environmental monitoring, assisted navigation, disaster warning, offshore oil and gas exploration [1][2][3][4]. An accurate localization scheme is essential to many of these applications. It is very challenging to achieve accurate localization in underwater applications. Most of the existing localization schemes rely on the measured distance between nodes for localization calculation [5]. However, obtaining an accurate distance in underwater environment is not easy. The widely used GPS system will not function due to the large path loss for underwater radio transmission. The accuracy of distance measurement can be greatly affected by underwater acoustic channel properties, time synchronization jitters, time-varying sound speed, and possible drifting/swings of the anchor nodes, whose positions are supposed to be known and steady. In order to improve the localization precision, least squares estimate is often adopted [6][7][8][9]. Least squares estimation can be also used in probabilistic localization schemes [9][10][11], where the probability distribution of the measured error are considered to further improve the localization accuracy. MMSE scheme (minimum mean squared error), is often used for position estimation that minimizes the average of squared errors. In the paper, we consider utilizing minimum mean absolute error (MMAE) to improve localization accuracy. The performance of the two schemes is elaborately compared in term of (a) exhaustive localization error distribution over a smaller region, where the localization error is the distance between estimated location and the real location; (b) localization error distribution for two dimensional (2D) Monte Carlo simulations (a grid of 40 by 40), where the localization error is the root mean square distance (RMSD) value based on 1000 simulation trials at each point; (c) uniform distribution and normal distribution in distance measurement uncertainties; (d) rectangle pre-deployed anchors
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This note was uploaded on 10/01/2010 for the course ELEC 6111 taught by Professor Brown during the Spring '10 term at E. Illinois.

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A Refined Localization Method for Underwater - This full...

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