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浙江大学学报(工学版)
J4  2014, Vol. 48 Issue (4): 727-733    DOI: 10.3785/j.issn.1008-973X.2014.04.024
电信技术、自动化技术     
非寻复域状态-空间滤波
项楠1, 赵航芳1, 宫先仪1,2
1. 浙江大学 浙江省综合信息网技术重点实验室,浙江 杭州 310027；
2. 杭州应用声学研究所, 浙江 杭州 310012
Improper complex-domain state-space filtering
XIANG Nan1, ZHAO Hang-fang1, GONG Xian-yi1,2
1. Zhejiang Province Key Laboratory of Information Network Technology, Zhejiang University, Hangzhou 310027, China;
2. Hangzhou Applied Acoustics Research Institute, Hangzhou 310012, China
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摘要：

针对实际非平稳海洋声数据存在非寻和非圆特性,在状态-空间模型下,对复信号作增广表示,建立复域状态-空间模型.从观察者的角度,推导了复高斯条件下的最佳瞬变观察者的线性表达式,即复域卡尔曼滤波器.当状态-空间模型为非线性非高斯时,最佳瞬态观察者须通过线性化或数值近似的方法近似得到.给出一种数值近似的方法——复域质点滤波,通过计算机仿真和波导实验证明了在信号非寻的情况下,复域卡尔曼滤波和复域质点滤波比常规的卡尔曼滤波和质点滤波具有更好的估计性能.
Abstract:

The complex-domain state-space model was established, which is developed from that in real-valued space through augmented expression of complex-valued random signals with improper and noncircular properties of non-stationary underwater acoustic data. The optimal transient observer, which termed as complex-domain Kalman filter, was derived under complex Gaussian and linear assumptions with knowledge of complemented covariance. While state-space model is nonlinear and non-Gaussian, the optimal transient observer can be derived by linearization and numerical approximation. A typical numerical approximation method names complex-domain particle filter. Simulation and waveguide experiment show that the complex-domain Kalman filter and complex-domain particle filter have better performance compared to the regular Kalman filter and particle filter in improper situation.
出版日期: 2014-09-03
:  TN 911  
基金资助:

国家自然科学基金资助项目（61171147,61379008）.
通讯作者: 赵航芳,女,教授.     E-mail: hfzhao@zju.edu.cn
作者简介: 项楠（1988—）,男,硕士生,从事水声信号处理的研究. E-mail: xiangnan0517@163.com
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引用本文:

项楠, 赵航芳, 宫先仪. 非寻复域状态-空间滤波[J]. J4, 2014, 48(4): 727-733.

XIANG Nan, ZHAO Hang-fang, GONG Xian-yi. Improper complex-domain state-space filtering. J4, 2014, 48(4): 727-733.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2014.04.024    或    http://www.zjujournals.com/eng/CN/Y2014/V48/I4/727

［1］ MOOERS C N. A technique for the cross spectrum analysis of pairs of complex-valued time series, with emphasis on properties of polarized components and rotational invariants ［J］. Deep Sea Research and Oceanographic, 1973, 20（12）: 1129-1141.
［2］ SCHREIER P J, SCHARF L L. Statistical signal processing of complex-valued data: the theory of improper and noncircular signals ［M］. Cambridge: Cambridge University Press, 2010.
［3］ HAYKIN S. Signal processing: where physics and mathematics meet ［J］. Signal Processing Magazine IEEE, 2001, 18(4): 6-7.
［4］ KAILATH T, SAYED A H, HASSIBI B. Linear estimation ［M］. New Jersey: Prentice-Hall, Englewood Cliffs, 2000.
［5］ CANDY J V. Bayesian signal processing: classical, modern and particle filtering methods ［M］. New York: Wiley, 2005.
［6］ ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking ［J］. IEEE Transaction on Signal Process. 2001, 50(2): 174-188.
［7］ EVENSEN G. The ensemble Kalman filter: theoretical formulation and practical implementation ［J］. Ocean Dynamics, 2003, 53(4): 343367.
［8］ ADALI T, SCHERIER P J, SCHARF L L. Complex-valued signal processing: the proper way to deal with impropriety ［J］. IEEE Transaction on Signal Processing, 2011, 59(11): 5101-5125.
［9］ PIEINBONO B, CHEVALIER P. Widely linear estimation with complex data ［J］. IEEE Transaction on Signal Process, 1995, 43(8): 20302033.［10］ ADALI T, LI H. Adaptive signal processing: next generation solutions ［M］.New York: Wiley, 2010.
［11］ SIMTH A F M, GELFAND A E. Bayesian statistics without tears: a sampling-resampling perspective ［J］. The American Statistician, 1992, 46(2): 84-88.
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