一种稳健的混沌调频信号频率跟踪技术

徐茂格; 宋耀良

doi:10.3724/SP.J.1146.2007.00979

一种稳健的混沌调频信号频率跟踪技术

doi: 10.3724/SP.J.1146.2007.00979

计量
- 文章访问数: 3389
- HTML全文浏览量: 87
- PDF下载量: 657
- 被引次数: 0
出版历程
- 收稿日期: 2007-06-15
- 修回日期: 2007-10-17
- 刊出日期: 2009-01-19

A Robust Frequency Tracking Technology for Chaotic Frequency Modulation

摘要

摘要: 频率跟踪问题是一个复杂的非线性问题，混沌调频方式的引入更加大了频率跟踪的难度，传统的频率跟踪技术扩展卡尔曼滤波(EKF)已无法跟踪如此复杂的频率变化。为此，该文首先建立了频率跟踪问题的状态空间模型，在此基础上引入了新颖的粒子滤波技术，分析了该技术的可行性，推导了混沌调频信号频率跟踪的后验克拉美-罗(PCRB)下界，实验仿真验证了该技术的优越性。
- 混沌调频;频率跟踪;粒子滤波;扩展卡尔曼滤波
Abstract: Frequency tracking is a complex nonlinear problem, which is more difficult for the chaotic frequency modulation signal in which the traditional Extended Kalman Filter (EKF) can not work well. Thus, a new state space model for the frequency tracking is proposed, and the particle filter which can be used in nonlinear and non-Gaussian environments is introduced. Further more, the feasibility of the particle filter is analyzed, also the Posterior Cramer-Rao Bounds (PCRB) for the frequency tracking of the chaotic frequency modulation signal is derived. The simulation demonstrates the superiorities of particle filtering at last.

HTML全文

参考文献(1)

Kennedy M P, Kolumban G, and Kis G, et al.. Performanceevaluation of FM-DCSK modulation in multipathenvironments [J]. IEEE Trans. on Circuits Systems - I, 2001,48(12): 1702-1717.[2]Callegari S, Rovatti R, and Setti G. Chaos-based FM signals:application and implementation issues [J]. IEEE Trans. onCircuits Systems-I, 2003, 8(50): 1141-1147.[3]Snyder D L. The State-Variable Approach to ContinuousEstimation with Applications to Analog CommunicationTheory [M]. Boston, MA: MIT Press, 1969: 129-135.[4]LA S B F and Robert B R. Design of an extended Kalmanfilter frequency tracker [J].IEEE Trans. on Signal Processing.1996, 44(3):739-742[5]Bittanti S and Savaresi S M. Frequency tracking viaextended Kalman filter: parameter designProceedings of theAmerican Control Conference, Chicago, IL, USA, 2000, 4:2225-2229.[6] Doucet A, Godsill S, and Andrieu C. On sequential MonteCarlo sampling methods for Bayesian filtering [J].. Statist.Comput.2000,10(3):197-208[6]Doucet A, Godsill S, and Andrieu C. Particle methods forchange detection, system identification, and control [J].Proc.of the IEEE.2004, 92(3):423-438[7]Amtlard P P, Brossier J M, and Moissan E. Phase tracking:what do we gain from optimality? Particle filtering versusphase-locked loops [J].Signal Processing.2003, 83(1):151-167[8]Fischler E and Bobrovsky B Z. Mean time to loose lock ofphase tracking by particle filtering [J].Signal Processing.2006, 86(1):3481-3485[9]Renate M and Nelson C. Bayesian reconstruction of chaoticdynamical systems [J].Physical Review E.2000, 62(3):3535-3542[10]徐茂格, 宋耀良, 刘力维. 基于修正扩展卡尔曼滤波和基于粒子滤波的混沌信号检测与跟踪 [J]. 南京理工大学学报, 2007,31(4): 514-517.[11]Peter T and Carlos M H. Posterior Cramer-Rao bounds fordiscrete time nonlinear filtering [J]. IEEE Trans. on SignalProcessing, 1998, 16(5): 1386-1396.

施引文献

资源附件(0)

访问统计