TY - GEN
T1 - Recursive Joint Cramér-Rao Lower Bound for Nonlinear Parametric Systems with Colored Noise
AU - Li, Xianqing
AU - Duan, Zhansheng
AU - Hanebeck, Uwe D.
N1 - Publisher Copyright:
© 2022 International Society of Information Fusion.
PY - 2022
Y1 - 2022
N2 - The performance evaluation for joint state and parameter estimation (JSPE) is of great significance. Joint Cramér-Rao lower bound (JCRLB) has been widely studied for JSPE of nonlinear parametric systems with white noise. However, in practice, the noise is often colored due to high measurement frequency and bandlimited signal channels. In this paper, a recursive JCRLB is developed for JSPE of nonlinear parametric systems with colored noise, characterized by auto-regressive (AR) models. First, we propose a unified recursive JCRLB for JSPE of general nonlinear parametric systems with higher-order autocorrelated process noises and autocorrelated measurement noise simultaneously. Then its relationship with the posterior Cramér-Rao lower bound (PCRLB) for filtering of nonlinear systems with colored noise and the hybrid Cramér-Rao lower bound (HCRLB) for JSPE of regular parametric systems with white noise are provided. Illustrative examples in radar target tracking verify the effectiveness of the proposed JCRLB for the performance evaluation for JSPE of nonlinear parametric systems with colored noise.
AB - The performance evaluation for joint state and parameter estimation (JSPE) is of great significance. Joint Cramér-Rao lower bound (JCRLB) has been widely studied for JSPE of nonlinear parametric systems with white noise. However, in practice, the noise is often colored due to high measurement frequency and bandlimited signal channels. In this paper, a recursive JCRLB is developed for JSPE of nonlinear parametric systems with colored noise, characterized by auto-regressive (AR) models. First, we propose a unified recursive JCRLB for JSPE of general nonlinear parametric systems with higher-order autocorrelated process noises and autocorrelated measurement noise simultaneously. Then its relationship with the posterior Cramér-Rao lower bound (PCRLB) for filtering of nonlinear systems with colored noise and the hybrid Cramér-Rao lower bound (HCRLB) for JSPE of regular parametric systems with white noise are provided. Illustrative examples in radar target tracking verify the effectiveness of the proposed JCRLB for the performance evaluation for JSPE of nonlinear parametric systems with colored noise.
KW - Auto-regressive models
KW - Colored noise
KW - JCRLB
KW - Nonlinear parametric systems
KW - Radar target tracking
UR - https://www.scopus.com/pages/publications/85136538584
U2 - 10.23919/FUSION49751.2022.9841284
DO - 10.23919/FUSION49751.2022.9841284
M3 - 会议稿件
AN - SCOPUS:85136538584
T3 - 2022 25th International Conference on Information Fusion, FUSION 2022
BT - 2022 25th International Conference on Information Fusion, FUSION 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th International Conference on Information Fusion, FUSION 2022
Y2 - 4 July 2022 through 7 July 2022
ER -