Stable and Fast Fixed-Point Algorithm for Time-of-Arrival-Based Location with Suppression of Outliers

Conventional time-of-arrival (TOA) localization methods often suffer from performance degradation in the presence of outliers. To address this issue, a robust framework is proposed to mitigate the effects of impulsive noise, including both Gaussian noise and outliers. This framework introduces a sof...

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Published in:IEEE transactions on aerospace and electronic systems pp. 1 - 21
Main Authors: Du, Hao-xuan, Feng, Da-Zheng, Wang, Meng
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Accuracy
Approximation algorithms
Cost function
Location awareness
Maximum likelihood estimation
Noise
Noise measurement
Robustness
Signal processing algorithms
Time measurement
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Abstract Conventional time-of-arrival (TOA) localization methods often suffer from performance degradation in the presence of outliers. To address this issue, a robust framework is proposed to mitigate the effects of impulsive noise, including both Gaussian noise and outliers. This framework introduces a soft-decision criterion (SDC) designed to suppress impulsive noise by satisfying three essential conditions. Building on SDC, a robust cost function (RCF) is formulated to reduce the impact of outliers on position estimation, enhancing the tolerance to large biases and false detections while utilizing all candidate inliers as much as possible. Furthermore, a stable and fast fixed-point algorithm (FPA) is derived from the maximum likelihood estimator (MLE) by forcing an approximation of the RCF gradient to zero. Simulation results confirm that the FPA outperforms several state-of-the-art convex relaxation algorithms, especially in the presence of strong and numerous outliers.
AbstractList Conventional time-of-arrival (TOA) localization methods often suffer from performance degradation in the presence of outliers. To address this issue, a robust framework is proposed to mitigate the effects of impulsive noise, including both Gaussian noise and outliers. This framework introduces a soft-decision criterion (SDC) designed to suppress impulsive noise by satisfying three essential conditions. Building on SDC, a robust cost function (RCF) is formulated to reduce the impact of outliers on position estimation, enhancing the tolerance to large biases and false detections while utilizing all candidate inliers as much as possible. Furthermore, a stable and fast fixed-point algorithm (FPA) is derived from the maximum likelihood estimator (MLE) by forcing an approximation of the RCF gradient to zero. Simulation results confirm that the FPA outperforms several state-of-the-art convex relaxation algorithms, especially in the presence of strong and numerous outliers.
Author Feng, Da-Zheng
Du, Hao-xuan
Wang, Meng
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Snippet Conventional time-of-arrival (TOA) localization methods often suffer from performance degradation in the presence of outliers. To address this issue, a robust...
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SubjectTerms Accuracy
Approximation algorithms
Cost function
Location awareness
Maximum likelihood estimation
Noise
Noise measurement
Robustness
Signal processing algorithms
Time measurement
Title Stable and Fast Fixed-Point Algorithm for Time-of-Arrival-Based Location with Suppression of Outliers
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