Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements
Abstract Joint Cramér‐Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non‐linear systems, in which the current measurement only depends on the current state. However, in reality, the non‐linear systems with two‐adjacent‐states d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-06-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12025 |
| _version_ | 1826820181749399552 |
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| author | Xianqing Li Zhansheng Duan Uwe D. Hanebeck |
| author_facet | Xianqing Li Zhansheng Duan Uwe D. Hanebeck |
| author_sort | Xianqing Li |
| collection | DOAJ |
| description | Abstract Joint Cramér‐Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non‐linear systems, in which the current measurement only depends on the current state. However, in reality, the non‐linear systems with two‐adjacent‐states dependent (TASD) measurements, that is, the current measurement is dependent on the current state as well as the most recent previous state, are also common. First, the recursive JCRLB for the general form of such non‐linear systems with unknown deterministic parameters is developed. Its relationships with the posterior CRLB for systems with TASD measurements and the hybrid CRLB for regular parametric systems are also provided. Then, the recursive JCRLBs for two special forms of parametric systems with TASD measurements, in which the 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 JCRLB for the performance evaluation of parametric TASD systems. |
| first_indexed | 2024-03-09T07:22:00Z |
| format | Article |
| id | doaj.art-7e276b5ec18b46f98c79dfcd753758a9 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2025-02-16T06:24:34Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-7e276b5ec18b46f98c79dfcd753758a92025-02-03T06:47:26ZengWileyIET Signal Processing1751-96751751-96832021-06-0115422123710.1049/sil2.12025Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurementsXianqing Li0Zhansheng Duan1Uwe D. Hanebeck2Center for Information Engineering Science Research School of Automation Science and Engineering Xi'an Jiaotong University Xi'an ChinaCenter for Information Engineering Science Research School of Automation Science and Engineering Xi'an Jiaotong University Xi'an ChinaIntelligent Sensor‐Actuator‐Systems Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe GermanyAbstract Joint Cramér‐Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non‐linear systems, in which the current measurement only depends on the current state. However, in reality, the non‐linear systems with two‐adjacent‐states dependent (TASD) measurements, that is, the current measurement is dependent on the current state as well as the most recent previous state, are also common. First, the recursive JCRLB for the general form of such non‐linear systems with unknown deterministic parameters is developed. Its relationships with the posterior CRLB for systems with TASD measurements and the hybrid CRLB for regular parametric systems are also provided. Then, the recursive JCRLBs for two special forms of parametric systems with TASD measurements, in which the 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 JCRLB for the performance evaluation of parametric TASD systems.https://doi.org/10.1049/sil2.12025nonlinear systemsperformance evaluationradar trackingrecursive estimationtarget tracking |
| spellingShingle | Xianqing Li Zhansheng Duan Uwe D. Hanebeck Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements IET Signal Processing nonlinear systems performance evaluation radar tracking recursive estimation target tracking |
| title | Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements |
| title_full | Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements |
| title_fullStr | Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements |
| title_full_unstemmed | Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements |
| title_short | Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements |
| title_sort | recursive joint cramer rao lower bound for parametric systems with two adjacent states dependent measurements |
| topic | nonlinear systems performance evaluation radar tracking recursive estimation target tracking |
| url | https://doi.org/10.1049/sil2.12025 |
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