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.
Mao Shi-song and Tang Yin-cai. Bayes Statistics[M]. Second Edition, Beijing: China Statistics Press, 2012: 35-44.
[3]
Gini F and Rangaswamy M. Knowledge-based Radar Detection, Tracking, and Classification[M]. New York: Published by John Wiley & Sons, Inc., 2008: 102-211.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
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.
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.
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.
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.
[18]
Haykin S. Cognitive Dynamic Systems, Perception-action Cycle Radar, and Radio[M]. Cambridge: Cambridge University Press, 2012: 201-230.
[19]
Zhang X and Cui C. Signal detection for cognitive radar[J]. Electronics Letters, 2013, 49(8): 559-560.
[20]
Kay S M. Fundamental of Statistical Signal Processing, Volume I: Estimation Theory[M], New Jersy: Pearson Education Inc., 1993: 360-365.