A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

Mei, Xiaojun; Wu, Huafeng; Xian, Jiangfeng; Chen, Bowen; Zhang, Hao; Liu, Xia

doi:10.3390/s19122708

Cited by 19 publications

(16 citation statements)

References 42 publications

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“…In recent years, Outlier Detection has been increasingly applied in sensor data processing [1][2][3][4][5][6][7][8][9]. Despite the countless contributions made over the years, there is continuing research on the subject, mainly because there has been an increase in computational power.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

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Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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“…The general system architecture of UWSNs is shown in Figure 1 . With the knowledge of some internode distances (ranges), the targets’ position would be determined, and thus a considerable body of research has been investigated in the literature regarding localization in UWSNs [ 4 , 6 , 7 , 8 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…The hybrid attenuation of the signal may dramatically degrade localization accuracy and limit the RSS-based technology capability in UWSNs. For this reason, researchers have explored various estimators to improve the localization accuracy under such signal loss [ 4 , 6 , 11 , 13 , 14 , 19 , 22 , 24 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

“…When it comes to a relatively significant absorption loss, the localization accuracy of NWLS and SD/SOCP cannot be guaranteed. In [ 14 ], the authors have proposed a robust, non-cooperative localization algorithm (RNLA), wherein a robust function is developed. The three-dimensional localization problem has been transformed into a two-dimensional localization problem in [ 14 ] with prior knowledge of the sea depth.…”

Section: Introductionmentioning

confidence: 99%

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An Absorption Mitigation Technique for Received Signal Strength-Based Target Localization in Underwater Wireless Sensor Networks

Mei

Saeed

et al. 2020

Sensors

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Localization is an indispensable technology for underwater wireless sensor networks (UWSNs). In what concerns UWSNs, the accurate location information is not only the requirement of the marine field applications but also the basis of the other corresponding research, for instance, network routing and topology control. Recently, an astonishing surge of interest has been drawn in the received signal strength (RSS)-based scheme due to cost-effectiveness and synchronization-free compared with others. However, unlike the terrestrial wireless sensor networks (WSNs), the acoustic signal may suffer the absorption loss in the underwater environment besides the path loss, which degrades the localization accuracy and limits the capability of the RSS-based technology in UWSNs. In this context, a robust localization method with an absorption mitigation technique (AMT) is developed. First, an RSS-based analytically tractable measurement model is conducted, where the maximum likelihood estimator (MLE) is derived. Nevertheless, it is quite challenging to solve the problem using MLE under a non-convex expression. Therefore, by exploiting certain approximations, the considered localization problem is converted into an optimization expression with a maximum absorption loss involved. A min–max strategy is then presented, with which the problem is turned to minimize the worst situation of the absorption loss. After a simple manipulation, the problem is further investigated as a generalized trust region sub-problem (GTRS) framework. Although the GTRS is a non-convex scheme, the solution can be obtained through an iteration method by introducing a multiplier. In addition, the closed-form expression of the Cramer–Rao lower bound (CRLB) of the analytically tractable measurement model is derived. Numerical simulations demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches in different scenarios.

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Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Rofatto¹,

Matsuoka²,

Klein³

et al. 2020

Preprint

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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of the modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions, but must be computed numerically by means of Monte Carlo. This paper provides the first results about Monte Carlo-based critical value inserted to different scenarios of correlation between the outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, overidentifications and statistical overlap associated with IDS in the presence of a single outlier. Based on such probability levels we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for the case where IDS is in play. MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances that the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection, but the lower the rates of outlier identification. For that case, the larger the Type I Error, the larger the ratio between MIB and MDB. We also highlight that an outlier becomes identifiable when the contribution of the measures to the wrong exclusion rate decline simultaneously. In that case, we verify that the effect of the correlation between the outlier statistics on the wrong exclusion rates becomes insignificant from a certain outlier magnitude, which increases the probability of identification.

show abstract

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

Cited by 19 publications

References 42 publications

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

An Absorption Mitigation Technique for Received Signal Strength-Based Target Localization in Underwater Wireless Sensor Networks

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

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