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Volume 31 Issue 1
Dec.  2010
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Xu Mao-ge, Song Yao-liang. A Robust Frequency Tracking Technology for Chaotic Frequency Modulation[J]. Journal of Electronics & Information Technology, 2009, 31(1): 104-107. doi: 10.3724/SP.J.1146.2007.00979
Citation: Xu Mao-ge, Song Yao-liang. A Robust Frequency Tracking Technology for Chaotic Frequency Modulation[J]. Journal of Electronics & Information Technology, 2009, 31(1): 104-107. doi: 10.3724/SP.J.1146.2007.00979

A Robust Frequency Tracking Technology for Chaotic Frequency Modulation

doi: 10.3724/SP.J.1146.2007.00979
  • Received Date: 2007-06-15
  • Rev Recd Date: 2007-10-17
  • Publish Date: 2009-01-19
  • 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.
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  • 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.
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