Bounds on the Number of Identifiable Outliers in Source Localization by Linear Programming

Wan

et al. 2016

Sensors

Precise localization has attracted considerable interest in Wireless Sensor Networks (WSNs) localization systems. Due to the internal or external disturbance, the existence of the outliers, including both the distance outliers and the anchor outliers, severely decreases the localization accuracy. In order to eliminate both kinds of outliers simultaneously, an outlier detection method is proposed based on the maximum entropy principle and fuzzy set theory. Since not all the outliers can be detected in the detection process, the Maximum Entropy Function (MEF) method is utilized to tolerate the errors and calculate the optimal estimated locations of unknown nodes. Simulation results demonstrate that the proposed localization method remains stable while the outliers vary. Moreover, the localization accuracy is highly improved by wisely rejecting outliers.

Section: Related Workmentioning

confidence: 99%

“…However, facing with the undetected small outliers, the method would lose efficacy. In summary, based on the detection target, the literatures [ 14 , 15 , 16 , 17 ] mentioned above ignore the influence of anchor outliers. Therefore, these methods are one-sided.…”

Section: Related Workmentioning

confidence: 99%

An MEF-Based Localization Algorithm against Outliers in Wireless Sensor Networks

Wan

et al. 2016

Sensors

“…[ 26 ] proposed a robust structure total least-squares algorithm for BOSL by using the improved Danish weight function to suppress the impact of outlier data on the localization performance. In addition to the M-estimator, there are other algorithms that can handle outlier data, such as outlier detection [ 27 ], clustering [ 28 ] and game theoretic techniques [ 29 ]. The outlier detection method [ 27 ] is to detect suspected outlier data first, and separate it from the original data set.…”

Section: Introductionmentioning

confidence: 99%

A Total Lp-Norm Optimization for Bearing-Only Source Localization in Impulsive Noise with SαS Distribution

Luo

Xue

Rong

et al. 2021

Sensors

This paper considers the problem of robust bearing-only source localization in impulsive noise with symmetric α-stable distribution based on the Lp-norm minimization criterion. The existing Iteratively Reweighted Pseudolinear Least-Squares (IRPLS) method can be used to solve the least LP-norm optimization problem. However, the IRPLS algorithm cannot reduce the bias attributed to the correlation between system matrices and noise vectors. To reduce this kind of bias, a Total Lp-norm Optimization (TLPO) method is proposed by minimizing the errors in all elements of system matrix and data vector based on the minimum dispersion criterion. Subsequently, an equivalent form of TLPO is obtained, and two algorithms are developed to solve the TLPO problem by using Iterative Generalized Eigenvalue Decomposition (IGED) and Generalized Lagrange Multiplier (GLM), respectively. Numerical examples demonstrate the performance advantage of the IGED and GLM algorithms over the IRPLS algorithm.

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)

“…As an example of attackresistant localization, [4] resorts to a cooperative approach, using optimization with dedicated Huber loss function. Using linear equations to describe the localization problem with an l 1 norm, linear programming can be used to avoid outliers measurements in the agent location estimation [5]. Alternatively, since the effect of outliers can be treated as errors in the transmitted data, error correcting codes have also been proposed [6], where iteratively, the region of interest is split into regions and a hypothesis test is performed at the fusion center, which receives data from each sensor.…”

Section: Introductionmentioning

confidence: 99%

A comparison of Bayesian localization methods in the presence of outliers

Ferrara

Wymeersch

Lohan

et al. 2017

Localization of a user in a wireless network is challenging in the presence of malfunctioning or malicious reference nodes, since if they are not accounted for, large localization errors can ensue. We evaluate three Bayesian methods to statistically identify outliers during localization: an exact method, an expectation maximization (EM) method proposed earlier, and a new method based on Variational Bayesian EM (VBEM). Simulation results indicate similar performance for the latter two schemes, with the VBEM algorithm able to provide a statistical description of the user location, rather than an estimate as in the simpler EM case. In contrast to previous studies, we find that there is a significant gap between the approximate methods and the exact method, the cause of which is discussed.