Outlier-Robust Iterative Extended Kalman Filtering

Tao, Yangtianze; Yau, Stephen S.-T.

doi:10.1109/lsp.2023.3285118

Cited by 15 publications

(1 citation statement)

References 24 publications

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“…The Kalman filtering method used by Student's t-distribution model incorporates noise with heavy tail characteristics. Various nonlinear Kalman-filtering algorithms based on Student's t-distribution have been proposed, depending on the integration operation [16]. Although Student's t-distribution provides good tracking performance, it requires fixed-point iteration to calculate various parameters in variational Bayesian inference, which increases computational complexity.…”

Section: Introduction 1literature Review and Motivationmentioning

confidence: 99%

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

Wei,

Lin,

Wang

et al. 2024

Remote Sensing

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To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian noise and struggle to accurately establish target trajectories when faced with heavy-tailed non-Gaussian distributions. Heavy-tailed noise leads to significant estimation errors and filter dispersion. Moreover, the exact trajectory of the target is crucial for tracking and prediction. Our proposed GSTM-TCPHD filter utilizes the GSTM distribution to model heavy-tailed noise, reducing modeling errors and generating a set of potential target trajectories. Since single sensors have a limited field of view and limited measurement information, we extend the filter to a multi-sensor scenario. To tackle the issue of data explosion from multiple sensors, we employed a greedy approximation method to assess measurements and introduced the MS-GSTM-TCPHD filter. The simulation results demonstrate that our proposed filter outperforms the CPHD/TCPHD filter and Student’s t-based TCPHD filter in terms of accurately estimating the trajectories of multiple targets during tracking while also achieving improved accuracy and shorter processing time.

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Section: Introduction 1literature Review and Motivationmentioning

confidence: 99%

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

Wei,

Lin,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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Weighted information entropy‐based Kalman filter for outliers and structural noise

Ma,

Yao,

Huang

et al. 2024

Asian Journal of Control

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Structural noise and outliers are widely present in real‐world state estimate scenarios, and they significantly degrade the performance of most filtering algorithms based on minimum mean square error (MMSE) criterion. To address this problem, this paper first models structural noise and outliers as independent and piecewise identical distribution (IPID). Then, a minimum error weighted entropy‐based Kalman filter (MEWE‐KF) is proposed, where a new cost function is constructed by introducing a weight function related to error location distances in an original information space into the minimum error entropy (MEE) criterion. Further, the iterative formulations of the proposed filter are derived, and the computational complexity and the convergence are also analyzed. Simulation results show that the proposed filter with adaptive weights has the superior performance for suppressing structural noise and outliers.

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