Sensor Bias Estimation for Track-to-Track Association

Tokta, Aybars; Hocaoglu, Ali Koksal

doi:10.1109/lsp.2019.2934596

Cited by 13 publications

(1 citation statement)

References 19 publications

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“…On the other hand, non-zero mean outliers are introduced by producing a shift in the measurement at some time instant that then persists for the defined period of time. This behavior is typical for sensors that produce biased measurements (Tokta and Hocaoglu, 2019).…”

Section: Resultsmentioning

confidence: 93%

Maximum likelihood-based robust state estimation over a horizon length during measurement outliers

Ali

Tahir

2020

Transactions of the Institute of Measurement and Control

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In this paper, a novel approach towards horizon-based maximum likelihood (ML) state estimator is proposed that makes the state estimation process more robust against unmodeled and unstructured noise and disturbances in the state-space models. State space models provide a powerful way to perform state and parameter estimation for dynamical systems. However, if the measurements are contaminated by outliers and disturbances with no known models, the estimation process is highly biased. A ML-based approach is used to find a batch solution for the filtering problem. Based on ML solution, a robust algorithm is proposed that seamlessly estimates dynamic state in the presence of zero or non-zero mean measurement outliers. The proposed algorithm that dictates this switching uses an explicit outlier detection mechanism that enables its seamless working. Simulations have been carried out for a moving target tracking application as an example to demonstrate the resilience of the proposed method against zero-mean and non-zero mean measurement outliers in comparison to state-of-the-art methods.

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Section: Resultsmentioning

confidence: 93%