Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

2021/06/10 by Aamir Hussain Chughtai, Chughtai, Aamir Hussain, Muhammad Tahir +3 · 1 citation
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #FOS: Electrical engineering #Signal Processing (eess.SP) #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #Water Systems and Optimization #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2106.05706

openalex publication_date 2021/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Considering a common case where measurements are obtained from independent sensors, we present a novel outlier-robust filter for nonlinear dynamical systems in this work. The proposed method is devised by modifying the measurement model and subsequently using the theory of Variational Bayes and general Gaussian filtering. We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted data during inference. By carrying out simulations for variable number of sensors we verify that an implementation of the proposed filter is computationally more efficient as compared to the proposed modifications of similar baseline methods still yielding similar estimation quality. In addition, experimentation results for various real-time indoor localization scenarios using Ultra-wide Band (UWB) sensors demonstrate the practical utility of the proposed method.

Cited by

• EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise

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