Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems
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| Titel: | Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems |
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| Autoren: | Xianqing Li, Zhansheng Duan, Qi Tang, Mahendra Mallick |
| Quelle: | Sensors, Vol 22, Iss 4667, p 4667 (2022) |
| Verlagsinformationen: | MDPI AG |
| Publikationsjahr: | 2022 |
| Bestand: | Directory of Open Access Journals: DOAJ Articles |
| Schlagwörter: | Bayesian Cramér-Rao lower bound (BCRLB), two-adjacent-states dependent (TASD) measurements, autocorrelated noises, cross-correlated noises, prediction, smoothing, Chemical technology, TP1-1185 |
| Beschreibung: | 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. |
| Publikationsart: | article in journal/newspaper |
| Sprache: | 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 |
| Verfügbarkeit: | https://doi.org/10.3390/s22134667 https://doaj.org/article/304ca6d63f8040c3867261bd0c68fe9f |
| Dokumentencode: | edsbas.F7CFD9D |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | 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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| DOI: | 10.3390/s22134667 |