Robust localization under NLOS environment in the presence of isolated outliers by full-Set TDOA measurements

Wang, Yuwei; Ho, K. C.; Wang, Zhi

doi:10.1016/j.sigpro.2023.109159

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…The TDOA algorithm is calculated by locating the difference in timestamps between the signal sent by the tag and the signal received at each base station [13]. Based on the knowledge of mathematical geometry, it is known that at least three distance differences are required to determine the position of a point in three-dimensional space.…”

Section: Srd Model Based On Tdoa Algorithmmentioning

confidence: 99%

An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function

Lv,

Liu,

Gao

et al. 2024

Applied Sciences

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In this paper, an improved cubature Kalman filtering (CKF) is proposed using the Sigmoid function to address the problems of positioning accuracy degradation and large deviations in ultra-wideband (UWB) indoor positioning in non-line-of-sight environments. The improved CKF is based on the squared range difference (SRD) model of the time difference of arrival (TDOA) algorithm. The inaccurate impact of model estimation under non-Gaussian noise is reduced by updating the measurement noise matrix in real time. The covariance matrix is estimated using singular value decomposition (SVD) to solve the problem of degraded state estimation performance. The filtering effect of the improved CKF algorithm is evaluated by referring to the checkpoints in the dynamic trajectory. The experimental results show that the proposed algorithm effectively mitigates the impact of UWB ranging outliers in the occluded experimental environment, which makes the dynamic positioning trajectory smoother, better fitted, and more stable. The algorithm improves the positioning accuracy by up to 39.29% compared with the SRD model used alone.

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Section: Srd Model Based On Tdoa Algorithmmentioning

confidence: 99%

An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function

Lv,

Liu,

Gao

et al. 2024

Applied Sciences

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Dealing with Outliers in Wireless Sensor Networks Localization: An Iterative and Selection-Minimization Strategy

Mederos-Madrazo,

Diaz-Roman,

Enriquez-Aguilera

et al. 2024

Int J Netw Distrib Comput

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In recent years, there has been considerable interest in robust range-based Wireless Sensor Network (WSN) localization due to the increasing importance of accurately locating sensors in various WSN applications. However, achieving precise localization is often hampered by the presence of outliers or underestimations in range measurements, particularly when employing the RSS technique. To tackle these issues, we introduce a Two-Step Localization (namely, the SelMin approach). In the initial phase, the approach utilizes Second-Order Cone Programming (SOCP) to minimize distance discrepancies. It does this by comparing a reference Euclidean Distance Matrix (EDM) with a weighted one derived from imprecise distances between sensor nodes. In the subsequent phase, a heuristic method is employed to identify a specific number of imprecise distances, referred to as outliers, that will be disregarded in the first phase, and this two-phase process continues iteratively. The experimental results demonstrate that the SelMin strategy performs better than the DSCL method when evaluated using the Root Mean Square Error (RMSE) metric. This superior performance is maintained even in challenging conditions, such as when there are many outliers (i.e, around 30$$\%$$ % ) in the network. This indicates that SelMin is a reliable and robust choice for these environments.

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Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

Fadakar,

Mansourian,

Akhavan

2024

Ad Hoc Networks

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Robust localization under NLOS environment in the presence of isolated outliers by full-Set TDOA measurements

Cited by 7 publications

References 39 publications

An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function

An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function

Dealing with Outliers in Wireless Sensor Networks Localization: An Iterative and Selection-Minimization Strategy

Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

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