贝叶斯估计器先验模型参数的迭代感知方法

邹鲲; 张斌; 王晓薇; 林澄清

doi:10.11999/JEIT141012

贝叶斯估计器先验模型参数的迭代感知方法

doi: 10.11999/JEIT141012

2.
(空军工程大学信息与导航学院西安 710077) ②(94816部队莆田 351100)

基金项目:

国家自然科学基金(61273408, 61302153)和航空创新基金资助课题

计量
- 文章访问数: 1408
- HTML全文浏览量: 91
- PDF下载量: 431
- 被引次数: 0
出版历程
- 收稿日期: 2014-07-28
- 修回日期: 2015-02-28
- 刊出日期: 2015-06-19

Iterated Cognition Method for Prior Model Parameters of Bayesian Estimator

1.
(School of Information and Navigation, Air Force Engineering University, Xi&rsquo
2.
(School of Information and Navigation, Air Force Engineering University, Xi&rsquo

摘要

摘要: 充分利用先验信息是提高统计推断性能的有效途径之一。贝叶斯估计的先验信息模型参数必须在设计阶段确定下来，与待探测环境模型参数之间必然存在不一致性，从而有可能导致估计质量的下降。该文首先给出了基于估计性能的先验模型参数感知的一般性框架。基于该框架，针对白高斯噪声中直流信号的贝叶斯估计器，分析了先验失配条件下的估计性能，给出了一种先验模型参数迭代感知的算法。利用计算机仿真分析了该估计器性能对先验模型参数的敏感性和稳健性，分析了不同条件下的迭代感知过程。计算机仿真结果表明，该文给出的迭代感知方法建立了从估计性能到先验模型参数的反馈，通过估计器与待探测场景的多次交互，可以使得先验模型与当前场景模型匹配。
- 雷达信号处理 /
- 贝叶斯估计器 /
- 先验模型失配 /
- 稳健性 /
- 敏感性 /
- 迭代感知方法
Abstract: Smart use of prior information is one of effective approaches to improve the performance of Bayesian estimator. At the design stage of Bayesian estimator, the prior model parameters must be specified, but these parameters may not be identical with parameters of environment at the applicant stage. The mismatched prior model can result to the performance degradation of Bayesian estimator. In this paper, a general framework of prior model parameters cognition based on the estimator performance is given at first. Base on the framework, for a Bayesian estimator of DC signal in WGN, the estimation performance is analyzed, and an iterated cognition method of prior model parameters is proposed. The computer simulation is used to analyze the sensitivity and robustness of the estimator under the mismatched prior model condition, and the iterated cognition procedure under different conditions. The computer simulation results show that, the feedback from the estimation performance to the prior model parameters is obtained with the cognitive method proposed in this paper, and the prior model can be matched with the current environment model after the repeated interactions between the estimator and environment.
- Radar signal processing /
- Bayesian estimator /
- Mismatched prior /
- Robustness /
- Sensitivity /
- Iterated cognitive method

HTML全文

参考文献(25)

Berger J O. Statistical Decision Theory and Bayesian Analysis[M]. New York: Springer, 1985: 1-77.

茆诗松, 汤银才. 贝叶斯统计[M]. 第2版, 北京: 中国统计出版社, 2012: 35-44.

Mao Shi-song and Tang Yin-cai. Bayes Statistics[M]. Second Edition, Beijing: China Statistics Press, 2012: 35-44.

Gini F and Rangaswamy M. Knowledge-based Radar Detection, Tracking, and Classification[M]. New York: Published by John Wiley Sons, Inc., 2008: 102-211.

Moya J C and Maio A D. Experimental performance analysis of distributed targets coherent radar detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2216-2238.

Ollila E, Tyler D E, Koivunen V, et al.. Compound Gaussian clutter modeling with an inverse Gaussian texture distribution[J]. IEEE Signal Processing Letters, 2012, 19(12): 876-879.

Abdelaziz M E M, Chonavel T, Aissa-El-Bey A, et al.. Sea clutter texture estimation: exploiting decorrelation and cyclostationarity[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2): 726-742.

Sangston K J, Gini F, and Greco M S. Coherent radar target detection in heavy-tailed compound Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64-77.

Gao Y, Liao G, Zhu S, et al.. A persymmetric GLRT for adaptive detection in compound Gaussian clutter with random texture[J]. IEEE Signal Processing Letters, 2013, 20(6): 615-618.

Bandiera F, Besson O, and Ricci G. Knowledge-aided covariance matrix estimation and adaptive detection in compound Gaussian noise[J]. IEEE Transaction on Signal Processing, 2010, 58(10): 5390-5396.

Bandiera F, Besson O, and Ricci G. Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: a Bayesian approach[J]. IEEE Transactions on Signal Processing, 2011, 59(12): 5698-5708.

Tang B, Tang J, and Peng Y N. Performance of knowledge aided space time adaptive processing[J]. IET Radar, Sonar Navigation, 2011, 5(3): 331-340.

Greco M, Stinco P, and Gini F. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1502-1513.

Bandiera F, Orlando D, and Ricci G. Advanced Radar Detection Schemes under Mismatched Signal Model[M]. Synthesis Lecture on Signal Processing, New York: Morgan Claypool Publishers, 2009: 15-24.

唐波, 张玉, 李科. 基于先验知识及其定量评估的自适应杂波抑制研究[J]. 航空学报, 2013, 34(5): 1174-1180.

Tang Bo, Zhang Yu, and Li Ke. Adaptive clutter suppression research based on prior knowledge and its accuracy evaluation[J].Acta Aeronautica et Astronautica Sinica, 2013, 34(5): 1174-1180.

邹鲲, 廖桂生, 李军, 等. 基于Bayes框架的复合高斯杂波下稳健检测[J]. 电子与信息学报, 2013, 35(7): 1551-1560.

Zou Kun, Liao Gui-sheng, Li Jun, et al.. Robust detection in compound Gaussian clutter based on Bayesian framework[J]. Journal of Electronics Information Technology, 2013, 35(7): 1551-1560.

邹鲲, 廖桂生, 李军, 等. 非高斯杂波下知识辅助检测器敏感性分析[J]. 电子与信息学报, 2014, 36(1): 181-186.

Zou Kun, Liao Gui-sheng, Li Jun, et al.. Sensitivity analysis of knowledge aided detector in non-Gaussian clutter[J]. Journal of Electronics Information Technology, 2014, 36(1): 181-186.

邹鲲, 廖桂生, 李军, 等. 非高斯杂波下知识辅助检测的认知方法[J]. 电子学报, 2014, 42(6): 1047-1054.

Zou Kun, Liao Gui-sheng, Li Jun, et al.. Cognitive method for knowledge aided detection in non-Gaussian clutter[J]. Acta Electronica Sinica, 2014, 42(6): 1047-1054.

Haykin S. Cognitive Dynamic Systems, Perception-action Cycle Radar, and Radio[M]. Cambridge: Cambridge University Press, 2012: 201-230.

Zhang X and Cui C. Signal detection for cognitive radar[J]. Electronics Letters, 2013, 49(8): 559-560.

Kay S M. Fundamental of Statistical Signal Processing, Volume I: Estimation Theory[M], New Jersy: Pearson Education Inc., 1993: 360-365.

施引文献

资源附件(0)

访问统计