An Outlier Detection Method Based on Mahalanobis Distance for Source Localization

Abstract

This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtained by the proposed division and greedy replacement method. The Mahalanobis distance based on robust mean and covariance matrix estimation method is then introduced to identify the outliers from the position sets. Finally, the weighted least squares method based on the reliable probabilities and distances is proposed for source localization. The simulation and experimental results show that the proposed method outperforms representative methods when unreliable AOAs are present.

Anchor Selection In Angle-Of-Arrival Estimation-Based Localization Using Polynomial Chaos Expansions

“…It was demonstrated in [11] that the Sobol' sensitivity indices can be obtained from the expansion coefficients in (5) and (6). These sensitivity indices translate how each of the input variables of the problem contributes to the output uncertainty.…”

“…Anchor selection for AoA localization in the literature mostly concerns acoustic sensors [5], and do not rely on the uncertainty of AoA measurements. Instead, methods of outlier detection that are able to detect if one of the measurements is strongly biased are proposed [6]. In fact the uncertainty on the AoA measurement can be modelled as a sum of two effects: the measurement noise, which can be modelled as a Gaussian distribution around the true AoA, and outlier measurements which can be modelled as a Uniform distributed random variable [7].…”

Two Efficient and Easy-to-Use NLOS Mitigation Solutions to Indoor 3-D AOA-Based Localization

“…calization that acquires merely the one-dimensional bearing measurements as AOA observations [8]- [10]. Depending on how the error-prone data are treated, these methods can be roughly divided into the robust statistics/outlier detection [8], [9] and expectation maximization [10] ones. In the general 3-D setting of the localization system, mitigating the bias errors in both azimuth and elevation angle measurements has been considered by the authors of [4] and [11].…”

Sensor Selection via Maximizing Hybrid Bayesian Fisher Information and Mutual Information in Unreliable Sensor Networks

“…It has been demonstrated that they exhibit a good consistency when the estimation error of each individual sensor follows a Gaussian distribution. However, sensor failures, data loss, NLOS propagation or unexpected interference may impose uncertainty on sensor networks which result in the presence of unreliable measurements [16,17].…”