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Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems

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Názov: Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems
Autori: Xianqing Li, Zhansheng Duan, Qi Tang, Mahendra Mallick
Zdroj: Sensors, Vol 22, Iss 4667, p 4667 (2022)
Informácie o vydavateľovi: MDPI AG
Rok vydania: 2022
Zbierka: Directory of Open Access Journals: DOAJ Articles
Predmety: Bayesian Cramér-Rao lower bound (BCRLB), two-adjacent-states dependent (TASD) measurements, autocorrelated noises, cross-correlated noises, prediction, smoothing, Chemical technology, TP1-1185
Popis: The performance evaluation of state estimators for nonlinear regular systems, in which the current measurement only depends on the current state directly, has been widely studied using the Bayesian Cramér-Rao lower bound (BCRLB). However, in practice, the measurements of many nonlinear systems are two-adjacent-states dependent (TASD) directly, i.e., the current measurement depends on the current state as well as the most recent previous state directly. In this paper, we first develop the recursive BCRLBs for the prediction and smoothing of nonlinear systems with TASD measurements. A comparison between the recursive BCRLBs for TASD systems and nonlinear regular systems is provided. Then, the recursive BCRLBs for the prediction and smoothing of two special types of TASD systems, in which the original measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the proposed recursive BCRLBs for the prediction and smoothing of TASD systems.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/1424-8220/22/13/4667; https://doaj.org/toc/1424-8220; https://doaj.org/article/304ca6d63f8040c3867261bd0c68fe9f
DOI: 10.3390/s22134667
Dostupnosť: https://doi.org/10.3390/s22134667
https://doaj.org/article/304ca6d63f8040c3867261bd0c68fe9f
Prístupové číslo: edsbas.F7CFD9D
Databáza: BASE
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  Data: Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems
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  Data: <searchLink fieldCode="AR" term="%22Xianqing+Li%22">Xianqing Li</searchLink><br /><searchLink fieldCode="AR" term="%22Zhansheng+Duan%22">Zhansheng Duan</searchLink><br /><searchLink fieldCode="AR" term="%22Qi+Tang%22">Qi Tang</searchLink><br /><searchLink fieldCode="AR" term="%22Mahendra+Mallick%22">Mahendra Mallick</searchLink>
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  Data: Sensors, Vol 22, Iss 4667, p 4667 (2022)
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  Data: 2022
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  Data: The performance evaluation of state estimators for nonlinear regular systems, in which the current measurement only depends on the current state directly, has been widely studied using the Bayesian Cramér-Rao lower bound (BCRLB). However, in practice, the measurements of many nonlinear systems are two-adjacent-states dependent (TASD) directly, i.e., the current measurement depends on the current state as well as the most recent previous state directly. In this paper, we first develop the recursive BCRLBs for the prediction and smoothing of nonlinear systems with TASD measurements. A comparison between the recursive BCRLBs for TASD systems and nonlinear regular systems is provided. Then, the recursive BCRLBs for the prediction and smoothing of two special types of TASD systems, in which the original measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the proposed recursive BCRLBs for the prediction and smoothing of TASD systems.
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  Data: 10.3390/s22134667
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