高动态环境下卫星导航信号跟踪技术研究
|
摘要
锁相环(Phase-Locked Loop, PLL)在全球定位系统(Global Positioning System, GPS)接收机中用来实现对输入信号的跟踪并给出精确的载波相位测量值。PLL的环路性能包括噪声性能（对噪声的滤除能力）、动态性能（对动态的跟踪能力）以及跟踪性能(跟踪精度)。这几种性能之间相互联系又充满了矛盾。跟踪性能和噪声性能有关，表现为静态误差。噪声性能越好，静态误差越小，跟踪精度越高。跟踪性能在一定的时候和动态性能也有关，当环路不能无误差地跟踪信号动态时，跟踪精度将会受到影响，表现为动态误差。静态误差和动态误差对环路带宽的要求相互矛盾。减小环路带宽，一方面可以减小静态误差，另一方面也增大了动态误差。在高动态环境下，传统的锁相环方法通常会牺牲PLL的跟踪性能以满足其动态性能，通过采取这种折衷的办法以实现PLL对高动态信号的跟踪能力。因此，本文围绕PLL的静态误差和动态误差对环路带宽的要求相互矛盾的问题，旨在研究如何在不损失PLL跟踪性能的前提下满足其对高动态信号的跟踪能力，以实现提高PLL在高动态环境下环路性能的目的。
为了改善高动态环境下PLL的环路性能，本文分别针对环路滤波器、环路噪声和环路带宽三个影响PLL环路性能的主要因素，提出了三种解决方法：基于UKF的PLL跟踪算法、基于小波降噪技术的PLL跟踪算法以及基于自适应带宽方法的PLL跟踪算法。其中，针对环路滤波器对PLL环路性能的影响，基于UKF的PLL跟踪算法利用卡尔曼滤波器代替传统锁相环中的环路滤波器，从根本上解决了PLL的静态误差和动态误差对环路带宽的要求相互矛盾的问题。而针对环路噪声对PLL环路性能的影响，小波降噪技术则通过对环路噪声进行降噪处理，使得在允许扩展PLL环路带宽的前提下，能够有效地降低环路滤波器带宽内的噪声功率，以达到跟踪高动态信号的目的。针对环路带宽对PLL环路性能的影响，基于自适应带宽方法的PLL跟踪算法根据对输入信号动态特性的估计，利用接收信号的载噪比信息通过迭代的方法计算得到最优带宽值，使得环路带宽能够随着输入信号动态的变化而进行实时调整，从而改善了高动态环境下PLL的环路性能。
为了分析本文所提出的三种算法对PLL环路性能的改善效果，在Matlab平台下利用GPS信号在不同动态模型和不同仿真参数(不同载噪比和不同环路带宽)的条件下对本文所提出的算法进行了仿真实验。仿真结果表明，在高动态环境下，本文所提出三种算法的噪声性能、动态性能以及跟踪性能均优于传统PLL的环路性能。基于UKF的PLL跟踪算法在具有优异跟踪精度同时也具有良好的噪声滤除能力和动态跟踪能力，但是由于该算法利用卡尔曼滤波器代替了传统锁相环中的环路滤波器，所以算法复杂度的提升较为明显，在硬件接收机中实现该算法存在一定的困难，在软件接收机中实现该算法所需的计算量明显高于本文所提出的另外两种算法。因此，对于实时性要求较高以及系统复杂度要求较低的高动态卫星导航信号跟踪的情况而言，基于小波降噪技术的PLL跟踪算法和基于自适应带宽方法的PLL跟踪算法显然更为适用。其中，基于小波降噪技术的PLL跟踪算法适用于信号动态以及输入载噪比未知的情况，而基于自适应带宽方法的PLL跟踪算法则适用于信号动态以及输入载噪比已知的情况，并且这两种算法的环路动态性能均能够满足高动态环境的要求。
The Phase-locked loop (PLL) is used in GPS receivers to track an incoming signal and to provide accurate carrier phase measurements. Some performances, such as noise performance (the ability to filter out noise), high dynamic tracking performance (the ability to track signal whose carrier frequency varies rapidly) and tracking accruacy are criterias to evaluate the PLL. However, these criterias are not only related to each other but also always conflicted. Tracking accuracy is related to noise performance which is reprented by static error. That means better noise performance is with less static error and higher accuracy. In addition, tracking performance is to some extnet effected by the high dynamic tracking ability. If the tracking loop can not track the dynamic of the signal, the tracking accuracy becomen worse and the dynamic error emerges. The static error and dynamic error are contradictory in term of bandwith. Narrow loop bandwith can reduce the static error when the dynamic error increases. In high dynamic situations, traditional PLL has to sacrafice tracking accuracy to satisfy the requirement of dynamic performance. With this compromise, traditional PLL could track high dynamic signal. Thus, this thesis focus on the conflict,i.e the different bandwith requirement by the PLL static error and dynamic error. In order to resolve this bandwith conflict and improve PLL tracking performance under high dynamic circumstance, this thesis study on how the tracking loop can not lose racking accuracy and can track the high dynamic signal at the same time
Three methods, aiming at the loop filter, loop noise and loop bandwith respectively which are the three main factors affecting the PLL performance, are investigated to improve PLL tracking performance in high dynamic applications: a Kalman filter-based tracking algorithm, application of a wavelet denoising technique in PLL, and an adaptive bandwidth algorithm. About the loop filter affecting the PLL performance, the Uncented Kalman filter-based tracking algorithm, replacing of the traditional PLL, is used to resolve the bandwith confilct between static and dynamic error. About the loop noise affecting the PLL performance, the wavelet de-noising technique is emploied to effectively decreases the noise level and allows broadening of the PLL bandwidth to track high dynamics signals. About the loop bandwith affecting the loop performace, the adaptive bandwidth PLL algorithm adapts the bandwith of the PLL according to the estimation of the incoming signal dynamics and caculate the optimum bandwith with iteration processing based on the SNR so as to improve the tracking performance under high dynamic conditions.
To analyze the performance improvement by the three proposed algorithms, simulation is conducted with different dynamic models and parameters in terms of signal-to-noise ratios and dynamic variations using simulating GPS signals in Matlab. The proposed algorithms are found to be better in noise performance, dynamic performance and tracking performance under high dynamic variations. PLL based on UKF does not only have better tracking performance but also has better ablity to filtering noise and dynamic tracking ability. However, since the traditional loop filer is replaced by UKF, the complexity increases which make the application a little difficult in hardware. In softeware receiver this algorithm need more caculation amount than the other two algorithms. Thus, wavelet denoising technique in PLL and adaptive bandwidth algorithm are more suitable for real-time and low complexity applications under high dynamic circumstance. Wavelet denoising technique in PLL is better without the knowledge of the dynamic and SNR,while adaptive bandwidth algorithm is better with the knowledge of the dynamic and SNR. In additon, these two algorithms are able to meet the requiremens of high dynamic signal tracking.
为了改善高动态环境下PLL的环路性能，本文分别针对环路滤波器、环路噪声和环路带宽三个影响PLL环路性能的主要因素，提出了三种解决方法：基于UKF的PLL跟踪算法、基于小波降噪技术的PLL跟踪算法以及基于自适应带宽方法的PLL跟踪算法。其中，针对环路滤波器对PLL环路性能的影响，基于UKF的PLL跟踪算法利用卡尔曼滤波器代替传统锁相环中的环路滤波器，从根本上解决了PLL的静态误差和动态误差对环路带宽的要求相互矛盾的问题。而针对环路噪声对PLL环路性能的影响，小波降噪技术则通过对环路噪声进行降噪处理，使得在允许扩展PLL环路带宽的前提下，能够有效地降低环路滤波器带宽内的噪声功率，以达到跟踪高动态信号的目的。针对环路带宽对PLL环路性能的影响，基于自适应带宽方法的PLL跟踪算法根据对输入信号动态特性的估计，利用接收信号的载噪比信息通过迭代的方法计算得到最优带宽值，使得环路带宽能够随着输入信号动态的变化而进行实时调整，从而改善了高动态环境下PLL的环路性能。
为了分析本文所提出的三种算法对PLL环路性能的改善效果，在Matlab平台下利用GPS信号在不同动态模型和不同仿真参数(不同载噪比和不同环路带宽)的条件下对本文所提出的算法进行了仿真实验。仿真结果表明，在高动态环境下，本文所提出三种算法的噪声性能、动态性能以及跟踪性能均优于传统PLL的环路性能。基于UKF的PLL跟踪算法在具有优异跟踪精度同时也具有良好的噪声滤除能力和动态跟踪能力，但是由于该算法利用卡尔曼滤波器代替了传统锁相环中的环路滤波器，所以算法复杂度的提升较为明显，在硬件接收机中实现该算法存在一定的困难，在软件接收机中实现该算法所需的计算量明显高于本文所提出的另外两种算法。因此，对于实时性要求较高以及系统复杂度要求较低的高动态卫星导航信号跟踪的情况而言，基于小波降噪技术的PLL跟踪算法和基于自适应带宽方法的PLL跟踪算法显然更为适用。其中，基于小波降噪技术的PLL跟踪算法适用于信号动态以及输入载噪比未知的情况，而基于自适应带宽方法的PLL跟踪算法则适用于信号动态以及输入载噪比已知的情况，并且这两种算法的环路动态性能均能够满足高动态环境的要求。
The Phase-locked loop (PLL) is used in GPS receivers to track an incoming signal and to provide accurate carrier phase measurements. Some performances, such as noise performance (the ability to filter out noise), high dynamic tracking performance (the ability to track signal whose carrier frequency varies rapidly) and tracking accruacy are criterias to evaluate the PLL. However, these criterias are not only related to each other but also always conflicted. Tracking accuracy is related to noise performance which is reprented by static error. That means better noise performance is with less static error and higher accuracy. In addition, tracking performance is to some extnet effected by the high dynamic tracking ability. If the tracking loop can not track the dynamic of the signal, the tracking accuracy becomen worse and the dynamic error emerges. The static error and dynamic error are contradictory in term of bandwith. Narrow loop bandwith can reduce the static error when the dynamic error increases. In high dynamic situations, traditional PLL has to sacrafice tracking accuracy to satisfy the requirement of dynamic performance. With this compromise, traditional PLL could track high dynamic signal. Thus, this thesis focus on the conflict,i.e the different bandwith requirement by the PLL static error and dynamic error. In order to resolve this bandwith conflict and improve PLL tracking performance under high dynamic circumstance, this thesis study on how the tracking loop can not lose racking accuracy and can track the high dynamic signal at the same time
Three methods, aiming at the loop filter, loop noise and loop bandwith respectively which are the three main factors affecting the PLL performance, are investigated to improve PLL tracking performance in high dynamic applications: a Kalman filter-based tracking algorithm, application of a wavelet denoising technique in PLL, and an adaptive bandwidth algorithm. About the loop filter affecting the PLL performance, the Uncented Kalman filter-based tracking algorithm, replacing of the traditional PLL, is used to resolve the bandwith confilct between static and dynamic error. About the loop noise affecting the PLL performance, the wavelet de-noising technique is emploied to effectively decreases the noise level and allows broadening of the PLL bandwidth to track high dynamics signals. About the loop bandwith affecting the loop performace, the adaptive bandwidth PLL algorithm adapts the bandwith of the PLL according to the estimation of the incoming signal dynamics and caculate the optimum bandwith with iteration processing based on the SNR so as to improve the tracking performance under high dynamic conditions.
To analyze the performance improvement by the three proposed algorithms, simulation is conducted with different dynamic models and parameters in terms of signal-to-noise ratios and dynamic variations using simulating GPS signals in Matlab. The proposed algorithms are found to be better in noise performance, dynamic performance and tracking performance under high dynamic variations. PLL based on UKF does not only have better tracking performance but also has better ablity to filtering noise and dynamic tracking ability. However, since the traditional loop filer is replaced by UKF, the complexity increases which make the application a little difficult in hardware. In softeware receiver this algorithm need more caculation amount than the other two algorithms. Thus, wavelet denoising technique in PLL and adaptive bandwidth algorithm are more suitable for real-time and low complexity applications under high dynamic circumstance. Wavelet denoising technique in PLL is better without the knowledge of the dynamic and SNR,while adaptive bandwidth algorithm is better with the knowledge of the dynamic and SNR. In additon, these two algorithms are able to meet the requiremens of high dynamic signal tracking.
引文
[1] Kaplan E D, Hegarty C J. Understanding GPS: Principles and Applications[M].Norwood: Artech House,2005.
[2] Parkinson B W, Spilker J J. Global Positioning System: Theory andApplications[M]. Vol.1. Danvers: Aiaa,1996.
[3] Corbell P M, Miller M M. Design and Analysis of a Matlab Based Digitized IFGPS Signal Simulator and a Simulink Based Configurable GPSReceiver[C]//Proceedings of the13th International Technical Meeting of theSatellite Division of The Institute of Navigation. Salt Lake City: ION,2000:1906–1915.
[4] Shen N, Zhang X. The Design for High Dynamic GPS Receiver in aCombinated Method of FLL and PLL[M]//Emerging Technologies forInformation Systems, Computing, and Management. New York: Springer,2013:3–11.
[5] Su Y T, Wu R C. Frequency Acquisition and Tracking in High DynamicEnvironments[J]. Vehicular Technology, IEEE Transactions on,2000,49(6):2419–2429.
[6]吕艳梅,井荣华,吴国庆,等.基于UKF的高动态GPS信号参数估计研究[J].系统仿真学报,2008,20(1):169–172.
[7]胡广书.数字信号处理导论[M].北京:清华大学出版社,2005.
[8] Liu J S, Chen R. Sequential Monte Carlo Methods for Dynamic Systems[J].Journal of the American statistical association,1998,93(443):1032–1044.
[9] Stirling-Gallacher R, Hulbert A, Povey G J. A Fast Acquisition Technique for aDirect Sequence Spread Spectrum Signal in the Presence of a Large DopplerShift[C]//Spread Spectrum Techniques and Applications Proceedings, IEEE4thInternational Symposium on. Mainz: IEEE,1996,1:156–160.
[10]Povey G J, Talvitie J. Doppler Compensation and Code Acquisition Techniquesfor LEO Satellite Mobile Radio Communications[C]//Satellite Systems forMobile Communications and Navigation, Fifth International Conference on.1996:16-19.
[11]唐学术.高动态GPS接收机的捕获与跟踪算法研究[D].北京:北京理工大学,2008.
[12]王伟,张廷新.高动态环境下GPS信号跟踪算法综述[J].空间电子技术,2000(1):1–9.
[13]Glisic S G, Poutanen T J, Wu W W, et al. New PN Code Acquisition Scheme forCDMA Networks with Low Signal-To-Noise Ratios[J]. Communications, IEEETransactions on,1999,47(2):300–310.
[14] Gold K, Brown A. Architectureand Performance Testing of a Software GPS Receiver for Space-BasedApplications[C]//Aerospace Conference,2004. Proceedings.2004IEEE. USA:IEEE,2004,4:2404–2416.
[15]R.F. Ormondroyd and V E. Comley, Limits on the Search Rate of a SlidingCorrelator Synchroniser Due to the Effects of Self-Noise andDecorrelation[C]//IEE Proceedings, Part F,1984,131(7):742-750.
[16]Alfred W. Fuxjaeger and Ronald A. Iltis, Acquisition of Timing andDoppler-Shift in a Direct-Sequence Spread-Spectrum System[J]. IEEE Trans onCommun,1994,42(10),:2870-2880.
[17]K.V Ravi and R. F. Ormondroyd, Simulation Performance of a QuantizedLog-Likelihood Sequential Detector for PN Code Acquisition in the Presence ofData Modulation and Doppler Shift[C]//IEEE MILCOM'91,1991:798-803.
[18]Dr. Ing. Petr Bezucha, Mean Acquisition Times of Serial Spread Spectrum PNAcquisition System in the Presence of Code Doppler[C]//IEEE7th Int. Sym. OnSpread-Spectrum Tech.&App., Prague,2002:750-755.
[19]Unjeng Cheng, William J. Hurd and J.I. Statman, Spread-Spectrum CodeAcquisition in the Presence of Doppler Shift and Data Modulation[J]. IEEETrans. on Commun,1990,38(2):241-250.
[20]Szu-Lin Su, Nan-Yang Yen and Sheng-Cheng Hsieh, Code Acquisition ofDirect-Sequence Spread Spectrum Communication with Doppler Shift[C]//IEEEInternational Conference on Communications,1995,3:1663-1667.
[21]Povey CzJ.R., Talvitie J., Doppler Compensation and Code AcquisitionTechniques for LEO Satellite Mobile Radio Communications[C]//5thInternational Conference on Satellite Systems for Mobile Communications andNavigation,1996:16-19.
[22]Glisic. S.Cz etc, New PN Code Acquisition Schemes for CDMA Networks withLow Signal-To-Noise Ratios[J]. IEEE Trans. on Comm.,1999,47(2):300-309.
[23]G lisic. S.Cz, Poutanen. T., Wu. W.W, New PN Code Acquisition Schemes forLow Earth Orbit Satellites[C]//IEEE Third International Symposium on SpreadSpectrum Techniques and Applications,1994,2:608-613.
[24]李小民,张其善,一种基于DSP技术的CA码高动态GPS接收机设计[J].电子技术应用,1998,11:37-42.
[25]王忠,刘晓玲,任苏萍,高动态GPS接收机的设计动态性能分析与改善途径[J].航天电子对抗,1999,4:53-62.
[26]田明坤等,高动态GPS接收机的一种设计方案[J].遥测遥控,2002,23(3):15-20.
[27]S. Hinedi and J. I. Stateman, High Dynamic GPS Tracking[M], Final Rep., JPLPublication, JPL, Pasadena, CA,1988.
[28]W.J. Hurd, High dynamic GPS Receiver Using Maximum LikelihoodEstimation and Frequency Tracking[J]. IEEE Trans. on AES.,1987, AES-23(4):425-436.
[29]V A. Vilnrotter, Frequency Estimation Techniques for High DynamicTrajectories[J]. IEEE Trans. on AES.,1989, AES-25(4):559-575.
[30]V A. Vilnrotter, S. Hinedi.&R. Kumar, A Comparison of Frequency EstimationTechniques for High Dynamic Trajectories[J]. JPL Publication,1988,21:30-42.
[31]S. Hinedi, An Extended Kalman Filter Based Automatic Frequency ControlLoop[M], DTA progress Report,1988.
[32]S. Aguirre, S. Hinedi, Two Novel Automatic Frequency Control Loops[J]. IEEETrans. on AES,1989,25(5):425-436.
[33]S. Aguirre, An Automatic Frequency Control Loop Using OverlappingDFTs[C]//DTA progress Report42-94,1988:222-231.
[34]R. Kumar, Fast Frequency Acquisition via Adaptive Least-SquaresAlgorithm[C]//IEE Proceedings,1989,136(4):155-160.
[35]R. Kumar, Efficient Detection and Signal Parameter Estimation withApplications to High Dynamic GPS Receivers[C]//ICC '90Conference onCommunications, Including Supercomm Technical Sessions,1990:358-364.
[36]R. Kumar, Efficient Sampling for Fast Frequency Acquisition via AdaptiveLeast-Squares Algorithm[C]//Proceedings of the International TelemeteringConference,1987:191-199.
[37]R. Kumar, A Novel Multistage Estimation of Signal Parameters[J]. IEEE Trans.on AES., AES-26(4),1990:181-194.
[38]Elliott D. Kalplan, Understanding GPS Principle and Application[M],1996.
[39]S. J.ulier, J. Uhlmann, and H. F. Dun-ant-Whyte, A New Method for TheNonlinear Transformation of Means and Covariances in Filters andEstimators[J]. IEEE Transactions on Automatic Control,2000,4(3):477-482.
[40]S. J. Julier and J. K. Uhlmann, A New Extension of the Kalman Filter toNonlinear Systems[C]//Signal Processing, Sensor Fusion, and TargetRecognition VI,3068, Proceedings of the society of Photo-OpticalInstrumentation Engineers(SPIE),1997:182-193.
[41]R. v. d. Merwe, E. A. Wan, and S. Julier, Sigma-Point Kalman Filters forNonlinear Estimation and Sensor-Fusion: Applications to IntegratedNavigation[C]//ALAA Guidance, Navigation and Control Conference,Providence, RI,2004:1-30.
[42]李涛,非线性滤波方法在导航系统中的应用研究[D].长沙:国防科学技术大学,2003.
[43]Y C. Ho and R. C. K. Lee, A Bayesian Approach to Problems in StochasticEstimation and Control[J]. IEEE Transactions on Automatic Control,1964,AC-9:333-339.
[44]F.M. Smith and A. E. Gelfand, Bayesian Statistics without Tears: aSampling-Resamping Perspective[J]. The American Statistician,1992,46(2):84-88.
[45]N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel Approach toNonlinear/Non-Gaussian Bayesian State Estimation[C]//IEE Proceedings-F,1993,140(2):107-113.
[46]N. Bergman, Recursive Bayesian Estimation-Navigation and TrackingApplications[D]. Sweden: Linkoping University,1999.
[47]R. Karlsson, Particle Filtering for Positioning and Tracking Applications[D].Sweden: Linkoping University,2005.
[48]C. Kwok, D. Fox, and M. Meila, Real-Time Particle Filters[C]//Proceedings ofthe IEEE,2004,92(3):469-484.
[49]G Casella and R. C. P., Rao-Blackwellisation of Sampling Schemes[J].Biometrika,1996,83(1):81-94.
[50]J. H. Koteclla and P. A. Djuric, Gaussian Particle Filtering[J]. IEEETransactions on Signal Processing,2003,51(10):2592-2601.
[51]Y Wu, X. Hu, D. Hu, and M. Wu, Comments on Gaussian Particle Filtering[J].IEEE Transactions on Signal Processing,2005,53(8):3350-3351.
[52]Mcaulay, R.J., Denlinger, E., A Decision-Directed Adaptive Tracker[J].Aerospace and Electronic Systems, IEEE Transactions on,1973, AES-9(2):229–236.
[53]Moose, R.L., Gholson, N.H., Adaptive Tracking of Abruptly ManeuveringTargets[C]//Decision and Control including the15th Symposium on AdaptiveProcesses, IEEE Conference on,1976,15:804-808.
[54]Y. T. Chan, A. Hu, A Kalman Filter Based Tracking Scheme with InputEstimation[J]. IEEE Transactions on Aerospace and Electronic Systems,1979,AES-15(2):237-244.
[55]P. L. Bogler, Tracking a Maneuvering Target Using Input Estimation[J]. IEEETransactions on Aerospace and Electronic Systems,1987, AES-23(3):298-310.
[56]Henk Blom, Yaakov Bar-Shalom, The Interacting Multiple Model Algorithm forSystems with Markovian Switching Coefficients[J]. IEEE Transactions onAutomatic Control,1988,33(8):780-783.
[57]M. Busch, S. Blackman, Evaluation of IMM Filtering for an Air Defense SystemApplication[C]//SPIE Proceedings,1995,2561:435-447.
[58]Zhang Jinsong, Yang weiqin, Hu shiqiang, Target Tracking Using the InteractiveMultiple Model Method[J]. Journal of Beijing institute of technology,1998,7(3):299-304.
[59]Best R. E. Phase-Locked Loops, Desigins Simulation, and Applications[M].New York: Mcgraw-Hill,1999:45–49.
[60]罗大成,王仕成,王明福,等.载波跟踪算法综述[J].系统仿真学报,2008,20(22):6023–6028.
[61]Egan W F. Phase–Lock Basics[M]. New York: John Wiley&Sons,2007.
[62]Salychev O S. Inertial Systems in Navigation and Geophysics[M]. Moscow,Russia: Bauman MSTU Press,1998.
[63]Mazor E, Averbuch A, Bar-Shalom Y, et al. Interacting Multiple Model Methodsin Target Tracking: A Survey[J]. Aerospace and Electronic Systems, IEEETransactions on,1998,34(1):103–123.
[64]Chiou T Y. GPS Receiver Performance Using Inertial-Aided Carrier TrackingLoop[C]//Proceedings of ION GNSS. Long Beach: ION,2005:13–16.
[65]左启耀,袁洪,林宝军.高动态环境下GPS信号跟踪环路优化算法研究[J].宇航学报,2008,29(2):550–555.
[66]Willett P. The workshop On Estimation, Tracking and Fusion: a Tribute toYaakov Bar-Shalom[J]. Aerospace and Electronic Systems Magazine, IEEE,2002,17(3):28–33.
[67]Yang C. Tracking of GPS Code Phase and Carrier Frequency in the FrequencyDomain[C]//Proceedings of the16th International Technical Meeting of theSatellite Division of The Institute of Navigation. Portland: ION,2003:628–637.
[68]Chen Z. Bayesian Filtering: from Kalman Filters to Particle Filters, andBeyond[J]. Statistics,2003,182(1):1–69.
[69]Gebre-Egziabher D, Razavi A, Enge P, et al. Doppler Aided Tracking Loops forSRGPS Integrity Monitoring[C]//Proceedings of the16th InternationalTechnical Meeting of the Satellite Division of The Institute of Navigation (IONGPS/GNSS2003). Portland: ION,2001:2562–2571.
[70]Macabiau C, Ries L, Bastide F, et al. GPS L5Receiver ImplementationIssues[C]//Proceedings of the16th International Technical Meeting of theSatellite Division of The Institute of Navigation (ION GPS/GNSS2003).Portland: ION,2001:153–164.
[71]Hamm C R, Flenniken W, Bevly D M, et al. Comparative Performance Analysisof Aided Carrier Tracking Loop Algorithms in High Noise/High DynamicEnvironments[C]//Proceedings of the2004ION GNSS Conference. Long Beach:ION,2004.
[72]Belanger P R. Estimation of Noise Covariance Matrices for a LinearTime-Varying Stochastic Process[J]. Automatica,1974,10(3):267–275.
[73]Myers K, Tapley B. Adaptive Sequential Estimation with Unknown NoiseStatistics[J]. Automatic Control, IEEE Transactions on,1976,21(4):520–523.
[74]Hurd W, Statman J, Vilnrotter V. High Dynamic GPS Receiver Using MaximumLikelihood Estimationand Frequency Tracking[J]. Aerospace and ElectronicSystems, IEEE Transactions on,1987(4):425–437.
[75]Vilnrotter V A, Hinedi S, Kumar R. Frequency Estimation Techniques for HighDynamic Trajectories[J]. Aerospace and Electronic Systems, IEEE Transactionson,1989,25(4):559–577.
[76]Ziedan N, Garrison J. Bit Synchronization and Doppler Frequency Removal atVery Low Carrier to Noise Ratio Using a Combination of the Viterbi Algorithmwith an Extended Kalman Filter[C]//Proceedings of the16th InternationalTechnical Meeting of the Satellite Division of The Institute of Navigation.Portland: ION,2001:616–627.
[77]Julier S, Uhlmann J, Durrant-Whyte H F. A New Method for the NonlinearTransformation of Means And Covariances in Filters and Estimators[J].Automatic Control, IEEE Transactions on,2000,45(3):477–482.
[78]宋迎春.动态定位中的卡尔曼滤波研究[D].长沙:中南大学,2006.
[79]La Scala B F, Bitmead R R, James M R. Conditions for Stability of the ExtendedKalman Filter and Their Application to the Frequency Tracking Problem[J].Mathematics of Control, Signals and Systems,1995,8(1):1–26.
[80]Jung H, Psiaki M L, Powell S P. Kalman-Filter-Based Semi-Codeless Trackingof Weak Dual-Frequency GPS Signals[C]//Proceeding of ION GPS. Portland:ION,2003:2515–2523.
[81]Psiaki M L, Jung H.Extended Kalman Filter Methods for Tracking Weak GPSSignals[C]//Proceedings of the15th International Technical Meeting of theSatellite Division of The Institute of Navigation. Portland: ION,2001:2539–2553.
[82]I to K. Approximation of the Zakai Equation for Nonlinear Filtering[J]. SIAMJournal on Control and Optimization,1996,34(2):620–634.
[83]Peral-Rosado D, López-Salcedo J A, Seco-Granados G, et al. Kalman FilterBased Architecture for Robust and High-Sensitivity Tracking in GNSSReceivers[C]//Satellite Navigation Technologies and European Workshop onGNSS Signals and Signal Processing (NAVITEC),20105th ESA Workshop on.Noordwijk: IEEE,2010:1–8.
[84]王文静,陈曦,孟维晓.基于UKF的GNSS高动态载波跟踪环路设计与性能分析[J].高技术通讯,2011,21(9):898–902.
[85]Ziedan N I, Garrison J L. Extended Kalman Filter-Based Tracking Of WeakGPS Signals under High Dynamic Conditions[C]//Proceedings of ION GNSS.Long Beach: ION,2004,2004:20–31.
[86]Julier S J, Uhlmann J K. Unscented Filtering and Nonlinear Estimation[J].Proceedings of the IEEE,2004,92(3):401–422.
[87]Jiang Z, Song Q, He Y, et al. A Novel Adaptive Unscented Kalman Filter forNonlinear Estimation[C]//Decision and Control,200746th IEEE Conference on.New Orleans: IEEE,2007:4293–4298.
[88]Tanizaki H, Mariano R S. Nonlinear Filters Based on Taylor SeriesExpansions[J]. Communications in Statistics-Theory and Methods,1996,25(6):1261–1282.
[89]Lototsky S, Mikulevicius R, Rozovskii B L. Nonlinear Filtering Revisited: aSpectral Approach[J]. SIAM Journal on Control and Optimization,1997,35(2):435–461.
[90]Reif K, Günther S, Yaz E, et al. Stochastic Stability of the Continuous-TimeExtended Kalman Filter[J]. IEE Proceedings-Control Theory and Applications,2000,147(1):45–52.
[91]Nrgaard M, Poulsen N K, Ravn O. New Developments in State Estimation forNonlinear Systems[J]. Automatica,2000,36(11):1627–1638.
[92]I to K, Xiong K. Gaussian Filters for Nonlinear Filtering Problems[J]. AutomaticControl, IEEE Transactions on,2000,45(5):910–927.
[93]Crassidis J L. Sigma-Point Kalman filtering for Integrated GPS and InertialNavigation[J]. Aerospace and Electronic Systems, IEEE Transactions on,2006,42(2):750–756.
[94]Crassidis J L, Markley F L. Unscented Filtering for Spacecraft AttitudeEstimation[J]. Journal of Guidance, Control, and Dynamics,2003,26(4):536–542.
[95]Farina A, Ristic B, Benvenuti D. Tracking a Ballistic Target: Comparison ofSeveral Nonlinear Filters[J]. Aerospace and Electronic Systems, IEEETransactions on,2002,38(3):854–867.
[96]Li W, Leung H, Zhou Y. Space-Time Registration of Radar and ESM UsingUnscented Kalman Filter[J]. Aerospace and Electronic Systems, IEEETransactions on,2004,40(3):824–836.
[97]Haykin S S, et al. Kalman Filtering and Neural Networks[M]. New York: WileyOnline Library,2001.
[98]Doucet A, De Freitas N, Gordon N, et al. Sequential Monte Carlo Methods inPractice[M]. Vol.1. New York: Springer,2001.
[99]Karlsson R. Particle Filtering for Positioning and Tracking Applications[D].Trondheim: Norwegian University of Science and Technology,2005.
[100] Arulampalam M S, Maskell S, Gordon N, et al. A Tutorial on Particle Filtersfor Online Nonlinear/Non-Gaussian Bayesian Tracking[J]. Signal Processing,IEEE Transactions on,2002,50(2):174–188.
[101] Doucet A, Godsill S, Andrieu C. On Sequential Monte Carlo SamplingMethods for Bayesian Filtering[J]. Statistics and computing,2000,10(3):197–208.
[102] Berzuini C, Best N G, Gilks W R, et al. Dynamic Conditional IndependenceModels and Markov Chain Monte Carlo Methods[J]. Journal of the AmericanStatistical Association,1997,92(440):1403–1412.
[103] Kotecha J H, Djuric P M. Gaussian Particle Filtering[J]. Signal Processing,IEEE Transactions on,2003,51(10):2592–2601.
[104] Wu Y, Hu X, Hu D, et al. Comments on” Gaussian Particle Filtering”.[J].IEEE Transactions on Signal Processing,2005,53(8-2):3350–3351.
[105] Skaloud J, Bruton A, Schwarz K. Detection and Filtering of Short-Term(1/FΓ)Noise in Inertial Sensors[J]. Navigation,1999,46(2):97–107.
[106] Carvalho H, Del Moral P, Monin A, et al. Optimal Nonlinear Filtering inGPS/INS Integration[J]. Aerospace and Electronic Systems, IEEE Transactionson,1997,33(3):835–850.
[107] Nordlund P J, Gustafsson F.Sequential Monte Carlo Filtering TechniquesApplied to Integrated Navigation Systems[C]//American Control Conference,2001. Proceedings of the2001. Arlington: IEEE,2001,6:4375–4380.
[108] Sasiadek J, Wang Q, Zeremba M. Fuzzy Adaptive Kalman Filtering forINS/GPS Data Fusion[C]//Intelligent Control,2000. Proceedings of the2000IEEE International Symposium on. Rio Patras: IEEE,2000:181–186.
[109] Carew B, Bélanger P. Identification of Optimum Filter Steady-State Gainfor Systems with Unknown Noise Covariances[J]. Automatic Control, IEEETransactionson,1973,18(6):582–587.
[110] Patapoutian A. Application of Kalman Filters with a Loop Delay inSynchronization[J]. Communications, IEEE Transactions on,2002,50(5):703–706.
[111] Mehra R. On the Identification of Variances and Adaptive KalmanFiltering[J]. Automatic Control, IEEE Transactions on,1970,15(2):175–184.
[112] Zhang L, Morton Y, Miller M M. A Variable Gain Adaptive KalmanFilter-Based GPS Carrier Tracking Algorithm for Ionosphere ScintillationSignals[C]//Proc. ION GNSS,. Portland, OR: ION,2010.
[113] Won J H, D tterb ck D, Eissfeller B. Performance Comparison of DifferentForms of Kalman Filter Approaches for a Vector-Based GNSS Signal TrackingLoop[J]. Navigation,2010,57(3):185–199.
[114] Jwo D J, Lai C N. Unscented Kalman Filter with Nonlinear DynamicProcess Modeling for GPS Navigation[J]. GPS Solutions,2008,12(4):249–260.
[115] Ganguly S. Real-Time Dual Frequency Software Receiver[C]//PositionLocation and Navigation Symposium,2004. PLANS2004. Portland: IEEE,2004:366–374.
[116] Psiaki M L. Smoother-based GPS Signal Tracking in a SoftwareReceiver[C]//Proceedings of the14th International Technical Meeting of theSatellite Division of The Institute of Navigation. Salt Lake City: ION,2001:2900–2913.
[117] Ma C, Lachapelle G, Cannon M.Implementation of a Software GPSReceiver[C]//Proceedings of ION GNSS. Long Beach: ION,2004:21–24.
[118] Krumvieda K, Madhani P, Cloman C, et al. A Complete IF Software GPSReceiver: A Tutorial About the Details[C]//Proceedings of the14th InternationalTechnical Meeting of the Satellite Division of The Institute of Navigation (IONGPS2001). Salt Lake City: ION,2001:789–829.
[119] Humphreys T E, Psiaki M L, Ledvina B M, et al. GPS Carrier TrackingLoop Performance in the Presence of Ionospheric Scintillations[C]//Proceedingsof ION GNSS2005. Long Beach: ION,2005:13–16.
[120] Simon D, El-Sherief H. Fuzzy Phase-Locked Loops[C]//Position Locationand Navigation Symposium,1994., IEEE. Las Vegas: IEEE,1994:252–259.
[121] Tseng S M, Zheng Y. Fuzzy Two-Stage Carrier Synchronization[J]. Fuzzysets and systems,1999,102(2):211–219.
[122] Brown R G, Hwang P Y, et al. Introduction to Random Signals and AppliedKalman Filtering[M]. Vol.1. New York: John Wiley&Sons,1992.
[123] Higuchi T. Monte Carlo Filter Using the Genetic Algorithm Operators[J].Journal of Statistical Computation and Simulation,1997,59(1):1–23.
[124] Kwok C, Fox D, Meila M. Real-Time Particle Filters[J]. Proceedings of theIEEE,2004,92(3):469–484.
[125] Chen R, Liu J S. Mixture Kalman Filters[J]. Journal of the Royal StatisticalSociety: Series B (Statistical Methodology),2000,62(3):493–508.
[126] Andrieu C, Doucet A. Particle Filtering for Partially Observed GaussianState Space Models[J]. Journal of the Royal Statistical Society: Series B(Statistical Methodology),2002,64(4):827–836.
[127] Doucet A, Logothetis A, Krishnamurthy V. Stochastic Sampling Algorithmsfor State Estimation of Jump Markov Linear Systems[J]. Automatic Control,IEEE Transactions on,2000,45(2):188–202.
[128] Wan E A, Van Der Merwe R. The Unscented Kalman Filter for NonlinearEstimation[C]//Adaptive Systems for Signal Processing, Communications, andControl Symposium2000. AS-SPCC. The IEEE2000. Lake Louise: IEEE,2000:153–158.
[129] Mallat S. A Wavelet Tour of Signal Processing[M]. San Diego: AccessOnline via Elsevier,1999.
[130] Mertins A. Signal Analysis: Wavelets, Filter Banks, Time-FrequencyTransforms and Applications[M]. Ultrasound in Biomedicine Research Series.New York:Wiley,1999. http://books.google.ca/books?id=hctqQgAACAAJ.
[131] Taswell C. The What, How, and Why of Wavelet Shrinkage Denoising[J].Computing in Science&Engineering,2000,2(3):12–19.
[132] Zhang Y, Wang Y, Wang W, et al. Doppler Ultrasound Signal DenoisingBased on Wavelet Frames[J]. Ultrasonics, Ferroelectrics and Frequency Control,IEEE Transactions on,2001,48(3):709–716.
[133] Ray P, Basuray A, Maitra A K. Optimum Wavelet Bases Selection forWavelet Based De-Noising in Partial Discharge Measurement[C]//Information&Communication Technologies (ICT),2013IEEE Conference on. Thuckalay:IEEE,2013:1110–1113.
[134] Coifman R R, Donoho D L. Translation-Invariant De-Noising[M]. NewYork: Springer,1995:125–150.
[135] Rioul O, Duhamel P. Fast Algorithms for Discrete and Continuous WaveletTransforms[J]. Information Theory, IEEE Transactions on,1992,38(2):569–586.
[136] Mallat S G. A Theory for Multiresolution Signal Decomposition: theWavelet Representation[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1989,11(7):674–693.
[137] DonohoDL. De-noising by Soft-thresholding[J]. IEEE Transactions onInformation Theory,1995,41(3):613–627.
[138] Ray P, Maitra A, Basuray A. A New Threshold Function for De-NoisingPartial Discharge Signal Based on Wavelet Transform[C]//Signal ProcessingImage Processing&Pattern Recognition (ICSIPR),2013InternationalConference on. Coimbatore: IEEE,2013:185–189.
[139] Gelb A. Applied Optimal Estimation[M]. USA: The MIT press,1974.
[140] Legrand F, Macabiau C. Real-time minimization of the total tracking errorin phase and delay lock loops-a second approach of the Fast AdaptiveBandwidth algorithm[C]//Proceedings of the57th Annual Meeting of TheInstitute of Navigation. Albuquerque: ION,2001:408–413.
[141] Legrand F, Macabiau C, Issler J L, et al. Improvement of PseudorangeMeasure-ments Accuracy by Using Fast Adaptive Bandwidth LockLoops[C]//Proceedings of the13th International Technical Meeting of theSatellite Division of The Institute of Navigation (ION GPS2000). Salt LakeCity: ION,2000:2346–2356.
[2] Parkinson B W, Spilker J J. Global Positioning System: Theory andApplications[M]. Vol.1. Danvers: Aiaa,1996.
[3] Corbell P M, Miller M M. Design and Analysis of a Matlab Based Digitized IFGPS Signal Simulator and a Simulink Based Configurable GPSReceiver[C]//Proceedings of the13th International Technical Meeting of theSatellite Division of The Institute of Navigation. Salt Lake City: ION,2000:1906–1915.
[4] Shen N, Zhang X. The Design for High Dynamic GPS Receiver in aCombinated Method of FLL and PLL[M]//Emerging Technologies forInformation Systems, Computing, and Management. New York: Springer,2013:3–11.
[5] Su Y T, Wu R C. Frequency Acquisition and Tracking in High DynamicEnvironments[J]. Vehicular Technology, IEEE Transactions on,2000,49(6):2419–2429.
[6]吕艳梅,井荣华,吴国庆,等.基于UKF的高动态GPS信号参数估计研究[J].系统仿真学报,2008,20(1):169–172.
[7]胡广书.数字信号处理导论[M].北京:清华大学出版社,2005.
[8] Liu J S, Chen R. Sequential Monte Carlo Methods for Dynamic Systems[J].Journal of the American statistical association,1998,93(443):1032–1044.
[9] Stirling-Gallacher R, Hulbert A, Povey G J. A Fast Acquisition Technique for aDirect Sequence Spread Spectrum Signal in the Presence of a Large DopplerShift[C]//Spread Spectrum Techniques and Applications Proceedings, IEEE4thInternational Symposium on. Mainz: IEEE,1996,1:156–160.
[10]Povey G J, Talvitie J. Doppler Compensation and Code Acquisition Techniquesfor LEO Satellite Mobile Radio Communications[C]//Satellite Systems forMobile Communications and Navigation, Fifth International Conference on.1996:16-19.
[11]唐学术.高动态GPS接收机的捕获与跟踪算法研究[D].北京:北京理工大学,2008.
[12]王伟,张廷新.高动态环境下GPS信号跟踪算法综述[J].空间电子技术,2000(1):1–9.
[13]Glisic S G, Poutanen T J, Wu W W, et al. New PN Code Acquisition Scheme forCDMA Networks with Low Signal-To-Noise Ratios[J]. Communications, IEEETransactions on,1999,47(2):300–310.
[14] Gold K, Brown A. Architectureand Performance Testing of a Software GPS Receiver for Space-BasedApplications[C]//Aerospace Conference,2004. Proceedings.2004IEEE. USA:IEEE,2004,4:2404–2416.
[15]R.F. Ormondroyd and V E. Comley, Limits on the Search Rate of a SlidingCorrelator Synchroniser Due to the Effects of Self-Noise andDecorrelation[C]//IEE Proceedings, Part F,1984,131(7):742-750.
[16]Alfred W. Fuxjaeger and Ronald A. Iltis, Acquisition of Timing andDoppler-Shift in a Direct-Sequence Spread-Spectrum System[J]. IEEE Trans onCommun,1994,42(10),:2870-2880.
[17]K.V Ravi and R. F. Ormondroyd, Simulation Performance of a QuantizedLog-Likelihood Sequential Detector for PN Code Acquisition in the Presence ofData Modulation and Doppler Shift[C]//IEEE MILCOM'91,1991:798-803.
[18]Dr. Ing. Petr Bezucha, Mean Acquisition Times of Serial Spread Spectrum PNAcquisition System in the Presence of Code Doppler[C]//IEEE7th Int. Sym. OnSpread-Spectrum Tech.&App., Prague,2002:750-755.
[19]Unjeng Cheng, William J. Hurd and J.I. Statman, Spread-Spectrum CodeAcquisition in the Presence of Doppler Shift and Data Modulation[J]. IEEETrans. on Commun,1990,38(2):241-250.
[20]Szu-Lin Su, Nan-Yang Yen and Sheng-Cheng Hsieh, Code Acquisition ofDirect-Sequence Spread Spectrum Communication with Doppler Shift[C]//IEEEInternational Conference on Communications,1995,3:1663-1667.
[21]Povey CzJ.R., Talvitie J., Doppler Compensation and Code AcquisitionTechniques for LEO Satellite Mobile Radio Communications[C]//5thInternational Conference on Satellite Systems for Mobile Communications andNavigation,1996:16-19.
[22]Glisic. S.Cz etc, New PN Code Acquisition Schemes for CDMA Networks withLow Signal-To-Noise Ratios[J]. IEEE Trans. on Comm.,1999,47(2):300-309.
[23]G lisic. S.Cz, Poutanen. T., Wu. W.W, New PN Code Acquisition Schemes forLow Earth Orbit Satellites[C]//IEEE Third International Symposium on SpreadSpectrum Techniques and Applications,1994,2:608-613.
[24]李小民,张其善,一种基于DSP技术的CA码高动态GPS接收机设计[J].电子技术应用,1998,11:37-42.
[25]王忠,刘晓玲,任苏萍,高动态GPS接收机的设计动态性能分析与改善途径[J].航天电子对抗,1999,4:53-62.
[26]田明坤等,高动态GPS接收机的一种设计方案[J].遥测遥控,2002,23(3):15-20.
[27]S. Hinedi and J. I. Stateman, High Dynamic GPS Tracking[M], Final Rep., JPLPublication, JPL, Pasadena, CA,1988.
[28]W.J. Hurd, High dynamic GPS Receiver Using Maximum LikelihoodEstimation and Frequency Tracking[J]. IEEE Trans. on AES.,1987, AES-23(4):425-436.
[29]V A. Vilnrotter, Frequency Estimation Techniques for High DynamicTrajectories[J]. IEEE Trans. on AES.,1989, AES-25(4):559-575.
[30]V A. Vilnrotter, S. Hinedi.&R. Kumar, A Comparison of Frequency EstimationTechniques for High Dynamic Trajectories[J]. JPL Publication,1988,21:30-42.
[31]S. Hinedi, An Extended Kalman Filter Based Automatic Frequency ControlLoop[M], DTA progress Report,1988.
[32]S. Aguirre, S. Hinedi, Two Novel Automatic Frequency Control Loops[J]. IEEETrans. on AES,1989,25(5):425-436.
[33]S. Aguirre, An Automatic Frequency Control Loop Using OverlappingDFTs[C]//DTA progress Report42-94,1988:222-231.
[34]R. Kumar, Fast Frequency Acquisition via Adaptive Least-SquaresAlgorithm[C]//IEE Proceedings,1989,136(4):155-160.
[35]R. Kumar, Efficient Detection and Signal Parameter Estimation withApplications to High Dynamic GPS Receivers[C]//ICC '90Conference onCommunications, Including Supercomm Technical Sessions,1990:358-364.
[36]R. Kumar, Efficient Sampling for Fast Frequency Acquisition via AdaptiveLeast-Squares Algorithm[C]//Proceedings of the International TelemeteringConference,1987:191-199.
[37]R. Kumar, A Novel Multistage Estimation of Signal Parameters[J]. IEEE Trans.on AES., AES-26(4),1990:181-194.
[38]Elliott D. Kalplan, Understanding GPS Principle and Application[M],1996.
[39]S. J.ulier, J. Uhlmann, and H. F. Dun-ant-Whyte, A New Method for TheNonlinear Transformation of Means and Covariances in Filters andEstimators[J]. IEEE Transactions on Automatic Control,2000,4(3):477-482.
[40]S. J. Julier and J. K. Uhlmann, A New Extension of the Kalman Filter toNonlinear Systems[C]//Signal Processing, Sensor Fusion, and TargetRecognition VI,3068, Proceedings of the society of Photo-OpticalInstrumentation Engineers(SPIE),1997:182-193.
[41]R. v. d. Merwe, E. A. Wan, and S. Julier, Sigma-Point Kalman Filters forNonlinear Estimation and Sensor-Fusion: Applications to IntegratedNavigation[C]//ALAA Guidance, Navigation and Control Conference,Providence, RI,2004:1-30.
[42]李涛,非线性滤波方法在导航系统中的应用研究[D].长沙:国防科学技术大学,2003.
[43]Y C. Ho and R. C. K. Lee, A Bayesian Approach to Problems in StochasticEstimation and Control[J]. IEEE Transactions on Automatic Control,1964,AC-9:333-339.
[44]F.M. Smith and A. E. Gelfand, Bayesian Statistics without Tears: aSampling-Resamping Perspective[J]. The American Statistician,1992,46(2):84-88.
[45]N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel Approach toNonlinear/Non-Gaussian Bayesian State Estimation[C]//IEE Proceedings-F,1993,140(2):107-113.
[46]N. Bergman, Recursive Bayesian Estimation-Navigation and TrackingApplications[D]. Sweden: Linkoping University,1999.
[47]R. Karlsson, Particle Filtering for Positioning and Tracking Applications[D].Sweden: Linkoping University,2005.
[48]C. Kwok, D. Fox, and M. Meila, Real-Time Particle Filters[C]//Proceedings ofthe IEEE,2004,92(3):469-484.
[49]G Casella and R. C. P., Rao-Blackwellisation of Sampling Schemes[J].Biometrika,1996,83(1):81-94.
[50]J. H. Koteclla and P. A. Djuric, Gaussian Particle Filtering[J]. IEEETransactions on Signal Processing,2003,51(10):2592-2601.
[51]Y Wu, X. Hu, D. Hu, and M. Wu, Comments on Gaussian Particle Filtering[J].IEEE Transactions on Signal Processing,2005,53(8):3350-3351.
[52]Mcaulay, R.J., Denlinger, E., A Decision-Directed Adaptive Tracker[J].Aerospace and Electronic Systems, IEEE Transactions on,1973, AES-9(2):229–236.
[53]Moose, R.L., Gholson, N.H., Adaptive Tracking of Abruptly ManeuveringTargets[C]//Decision and Control including the15th Symposium on AdaptiveProcesses, IEEE Conference on,1976,15:804-808.
[54]Y. T. Chan, A. Hu, A Kalman Filter Based Tracking Scheme with InputEstimation[J]. IEEE Transactions on Aerospace and Electronic Systems,1979,AES-15(2):237-244.
[55]P. L. Bogler, Tracking a Maneuvering Target Using Input Estimation[J]. IEEETransactions on Aerospace and Electronic Systems,1987, AES-23(3):298-310.
[56]Henk Blom, Yaakov Bar-Shalom, The Interacting Multiple Model Algorithm forSystems with Markovian Switching Coefficients[J]. IEEE Transactions onAutomatic Control,1988,33(8):780-783.
[57]M. Busch, S. Blackman, Evaluation of IMM Filtering for an Air Defense SystemApplication[C]//SPIE Proceedings,1995,2561:435-447.
[58]Zhang Jinsong, Yang weiqin, Hu shiqiang, Target Tracking Using the InteractiveMultiple Model Method[J]. Journal of Beijing institute of technology,1998,7(3):299-304.
[59]Best R. E. Phase-Locked Loops, Desigins Simulation, and Applications[M].New York: Mcgraw-Hill,1999:45–49.
[60]罗大成,王仕成,王明福,等.载波跟踪算法综述[J].系统仿真学报,2008,20(22):6023–6028.
[61]Egan W F. Phase–Lock Basics[M]. New York: John Wiley&Sons,2007.
[62]Salychev O S. Inertial Systems in Navigation and Geophysics[M]. Moscow,Russia: Bauman MSTU Press,1998.
[63]Mazor E, Averbuch A, Bar-Shalom Y, et al. Interacting Multiple Model Methodsin Target Tracking: A Survey[J]. Aerospace and Electronic Systems, IEEETransactions on,1998,34(1):103–123.
[64]Chiou T Y. GPS Receiver Performance Using Inertial-Aided Carrier TrackingLoop[C]//Proceedings of ION GNSS. Long Beach: ION,2005:13–16.
[65]左启耀,袁洪,林宝军.高动态环境下GPS信号跟踪环路优化算法研究[J].宇航学报,2008,29(2):550–555.
[66]Willett P. The workshop On Estimation, Tracking and Fusion: a Tribute toYaakov Bar-Shalom[J]. Aerospace and Electronic Systems Magazine, IEEE,2002,17(3):28–33.
[67]Yang C. Tracking of GPS Code Phase and Carrier Frequency in the FrequencyDomain[C]//Proceedings of the16th International Technical Meeting of theSatellite Division of The Institute of Navigation. Portland: ION,2003:628–637.
[68]Chen Z. Bayesian Filtering: from Kalman Filters to Particle Filters, andBeyond[J]. Statistics,2003,182(1):1–69.
[69]Gebre-Egziabher D, Razavi A, Enge P, et al. Doppler Aided Tracking Loops forSRGPS Integrity Monitoring[C]//Proceedings of the16th InternationalTechnical Meeting of the Satellite Division of The Institute of Navigation (IONGPS/GNSS2003). Portland: ION,2001:2562–2571.
[70]Macabiau C, Ries L, Bastide F, et al. GPS L5Receiver ImplementationIssues[C]//Proceedings of the16th International Technical Meeting of theSatellite Division of The Institute of Navigation (ION GPS/GNSS2003).Portland: ION,2001:153–164.
[71]Hamm C R, Flenniken W, Bevly D M, et al. Comparative Performance Analysisof Aided Carrier Tracking Loop Algorithms in High Noise/High DynamicEnvironments[C]//Proceedings of the2004ION GNSS Conference. Long Beach:ION,2004.
[72]Belanger P R. Estimation of Noise Covariance Matrices for a LinearTime-Varying Stochastic Process[J]. Automatica,1974,10(3):267–275.
[73]Myers K, Tapley B. Adaptive Sequential Estimation with Unknown NoiseStatistics[J]. Automatic Control, IEEE Transactions on,1976,21(4):520–523.
[74]Hurd W, Statman J, Vilnrotter V. High Dynamic GPS Receiver Using MaximumLikelihood Estimationand Frequency Tracking[J]. Aerospace and ElectronicSystems, IEEE Transactions on,1987(4):425–437.
[75]Vilnrotter V A, Hinedi S, Kumar R. Frequency Estimation Techniques for HighDynamic Trajectories[J]. Aerospace and Electronic Systems, IEEE Transactionson,1989,25(4):559–577.
[76]Ziedan N, Garrison J. Bit Synchronization and Doppler Frequency Removal atVery Low Carrier to Noise Ratio Using a Combination of the Viterbi Algorithmwith an Extended Kalman Filter[C]//Proceedings of the16th InternationalTechnical Meeting of the Satellite Division of The Institute of Navigation.Portland: ION,2001:616–627.
[77]Julier S, Uhlmann J, Durrant-Whyte H F. A New Method for the NonlinearTransformation of Means And Covariances in Filters and Estimators[J].Automatic Control, IEEE Transactions on,2000,45(3):477–482.
[78]宋迎春.动态定位中的卡尔曼滤波研究[D].长沙:中南大学,2006.
[79]La Scala B F, Bitmead R R, James M R. Conditions for Stability of the ExtendedKalman Filter and Their Application to the Frequency Tracking Problem[J].Mathematics of Control, Signals and Systems,1995,8(1):1–26.
[80]Jung H, Psiaki M L, Powell S P. Kalman-Filter-Based Semi-Codeless Trackingof Weak Dual-Frequency GPS Signals[C]//Proceeding of ION GPS. Portland:ION,2003:2515–2523.
[81]Psiaki M L, Jung H.Extended Kalman Filter Methods for Tracking Weak GPSSignals[C]//Proceedings of the15th International Technical Meeting of theSatellite Division of The Institute of Navigation. Portland: ION,2001:2539–2553.
[82]I to K. Approximation of the Zakai Equation for Nonlinear Filtering[J]. SIAMJournal on Control and Optimization,1996,34(2):620–634.
[83]Peral-Rosado D, López-Salcedo J A, Seco-Granados G, et al. Kalman FilterBased Architecture for Robust and High-Sensitivity Tracking in GNSSReceivers[C]//Satellite Navigation Technologies and European Workshop onGNSS Signals and Signal Processing (NAVITEC),20105th ESA Workshop on.Noordwijk: IEEE,2010:1–8.
[84]王文静,陈曦,孟维晓.基于UKF的GNSS高动态载波跟踪环路设计与性能分析[J].高技术通讯,2011,21(9):898–902.
[85]Ziedan N I, Garrison J L. Extended Kalman Filter-Based Tracking Of WeakGPS Signals under High Dynamic Conditions[C]//Proceedings of ION GNSS.Long Beach: ION,2004,2004:20–31.
[86]Julier S J, Uhlmann J K. Unscented Filtering and Nonlinear Estimation[J].Proceedings of the IEEE,2004,92(3):401–422.
[87]Jiang Z, Song Q, He Y, et al. A Novel Adaptive Unscented Kalman Filter forNonlinear Estimation[C]//Decision and Control,200746th IEEE Conference on.New Orleans: IEEE,2007:4293–4298.
[88]Tanizaki H, Mariano R S. Nonlinear Filters Based on Taylor SeriesExpansions[J]. Communications in Statistics-Theory and Methods,1996,25(6):1261–1282.
[89]Lototsky S, Mikulevicius R, Rozovskii B L. Nonlinear Filtering Revisited: aSpectral Approach[J]. SIAM Journal on Control and Optimization,1997,35(2):435–461.
[90]Reif K, Günther S, Yaz E, et al. Stochastic Stability of the Continuous-TimeExtended Kalman Filter[J]. IEE Proceedings-Control Theory and Applications,2000,147(1):45–52.
[91]Nrgaard M, Poulsen N K, Ravn O. New Developments in State Estimation forNonlinear Systems[J]. Automatica,2000,36(11):1627–1638.
[92]I to K, Xiong K. Gaussian Filters for Nonlinear Filtering Problems[J]. AutomaticControl, IEEE Transactions on,2000,45(5):910–927.
[93]Crassidis J L. Sigma-Point Kalman filtering for Integrated GPS and InertialNavigation[J]. Aerospace and Electronic Systems, IEEE Transactions on,2006,42(2):750–756.
[94]Crassidis J L, Markley F L. Unscented Filtering for Spacecraft AttitudeEstimation[J]. Journal of Guidance, Control, and Dynamics,2003,26(4):536–542.
[95]Farina A, Ristic B, Benvenuti D. Tracking a Ballistic Target: Comparison ofSeveral Nonlinear Filters[J]. Aerospace and Electronic Systems, IEEETransactions on,2002,38(3):854–867.
[96]Li W, Leung H, Zhou Y. Space-Time Registration of Radar and ESM UsingUnscented Kalman Filter[J]. Aerospace and Electronic Systems, IEEETransactions on,2004,40(3):824–836.
[97]Haykin S S, et al. Kalman Filtering and Neural Networks[M]. New York: WileyOnline Library,2001.
[98]Doucet A, De Freitas N, Gordon N, et al. Sequential Monte Carlo Methods inPractice[M]. Vol.1. New York: Springer,2001.
[99]Karlsson R. Particle Filtering for Positioning and Tracking Applications[D].Trondheim: Norwegian University of Science and Technology,2005.
[100] Arulampalam M S, Maskell S, Gordon N, et al. A Tutorial on Particle Filtersfor Online Nonlinear/Non-Gaussian Bayesian Tracking[J]. Signal Processing,IEEE Transactions on,2002,50(2):174–188.
[101] Doucet A, Godsill S, Andrieu C. On Sequential Monte Carlo SamplingMethods for Bayesian Filtering[J]. Statistics and computing,2000,10(3):197–208.
[102] Berzuini C, Best N G, Gilks W R, et al. Dynamic Conditional IndependenceModels and Markov Chain Monte Carlo Methods[J]. Journal of the AmericanStatistical Association,1997,92(440):1403–1412.
[103] Kotecha J H, Djuric P M. Gaussian Particle Filtering[J]. Signal Processing,IEEE Transactions on,2003,51(10):2592–2601.
[104] Wu Y, Hu X, Hu D, et al. Comments on” Gaussian Particle Filtering”.[J].IEEE Transactions on Signal Processing,2005,53(8-2):3350–3351.
[105] Skaloud J, Bruton A, Schwarz K. Detection and Filtering of Short-Term(1/FΓ)Noise in Inertial Sensors[J]. Navigation,1999,46(2):97–107.
[106] Carvalho H, Del Moral P, Monin A, et al. Optimal Nonlinear Filtering inGPS/INS Integration[J]. Aerospace and Electronic Systems, IEEE Transactionson,1997,33(3):835–850.
[107] Nordlund P J, Gustafsson F.Sequential Monte Carlo Filtering TechniquesApplied to Integrated Navigation Systems[C]//American Control Conference,2001. Proceedings of the2001. Arlington: IEEE,2001,6:4375–4380.
[108] Sasiadek J, Wang Q, Zeremba M. Fuzzy Adaptive Kalman Filtering forINS/GPS Data Fusion[C]//Intelligent Control,2000. Proceedings of the2000IEEE International Symposium on. Rio Patras: IEEE,2000:181–186.
[109] Carew B, Bélanger P. Identification of Optimum Filter Steady-State Gainfor Systems with Unknown Noise Covariances[J]. Automatic Control, IEEETransactionson,1973,18(6):582–587.
[110] Patapoutian A. Application of Kalman Filters with a Loop Delay inSynchronization[J]. Communications, IEEE Transactions on,2002,50(5):703–706.
[111] Mehra R. On the Identification of Variances and Adaptive KalmanFiltering[J]. Automatic Control, IEEE Transactions on,1970,15(2):175–184.
[112] Zhang L, Morton Y, Miller M M. A Variable Gain Adaptive KalmanFilter-Based GPS Carrier Tracking Algorithm for Ionosphere ScintillationSignals[C]//Proc. ION GNSS,. Portland, OR: ION,2010.
[113] Won J H, D tterb ck D, Eissfeller B. Performance Comparison of DifferentForms of Kalman Filter Approaches for a Vector-Based GNSS Signal TrackingLoop[J]. Navigation,2010,57(3):185–199.
[114] Jwo D J, Lai C N. Unscented Kalman Filter with Nonlinear DynamicProcess Modeling for GPS Navigation[J]. GPS Solutions,2008,12(4):249–260.
[115] Ganguly S. Real-Time Dual Frequency Software Receiver[C]//PositionLocation and Navigation Symposium,2004. PLANS2004. Portland: IEEE,2004:366–374.
[116] Psiaki M L. Smoother-based GPS Signal Tracking in a SoftwareReceiver[C]//Proceedings of the14th International Technical Meeting of theSatellite Division of The Institute of Navigation. Salt Lake City: ION,2001:2900–2913.
[117] Ma C, Lachapelle G, Cannon M.Implementation of a Software GPSReceiver[C]//Proceedings of ION GNSS. Long Beach: ION,2004:21–24.
[118] Krumvieda K, Madhani P, Cloman C, et al. A Complete IF Software GPSReceiver: A Tutorial About the Details[C]//Proceedings of the14th InternationalTechnical Meeting of the Satellite Division of The Institute of Navigation (IONGPS2001). Salt Lake City: ION,2001:789–829.
[119] Humphreys T E, Psiaki M L, Ledvina B M, et al. GPS Carrier TrackingLoop Performance in the Presence of Ionospheric Scintillations[C]//Proceedingsof ION GNSS2005. Long Beach: ION,2005:13–16.
[120] Simon D, El-Sherief H. Fuzzy Phase-Locked Loops[C]//Position Locationand Navigation Symposium,1994., IEEE. Las Vegas: IEEE,1994:252–259.
[121] Tseng S M, Zheng Y. Fuzzy Two-Stage Carrier Synchronization[J]. Fuzzysets and systems,1999,102(2):211–219.
[122] Brown R G, Hwang P Y, et al. Introduction to Random Signals and AppliedKalman Filtering[M]. Vol.1. New York: John Wiley&Sons,1992.
[123] Higuchi T. Monte Carlo Filter Using the Genetic Algorithm Operators[J].Journal of Statistical Computation and Simulation,1997,59(1):1–23.
[124] Kwok C, Fox D, Meila M. Real-Time Particle Filters[J]. Proceedings of theIEEE,2004,92(3):469–484.
[125] Chen R, Liu J S. Mixture Kalman Filters[J]. Journal of the Royal StatisticalSociety: Series B (Statistical Methodology),2000,62(3):493–508.
[126] Andrieu C, Doucet A. Particle Filtering for Partially Observed GaussianState Space Models[J]. Journal of the Royal Statistical Society: Series B(Statistical Methodology),2002,64(4):827–836.
[127] Doucet A, Logothetis A, Krishnamurthy V. Stochastic Sampling Algorithmsfor State Estimation of Jump Markov Linear Systems[J]. Automatic Control,IEEE Transactions on,2000,45(2):188–202.
[128] Wan E A, Van Der Merwe R. The Unscented Kalman Filter for NonlinearEstimation[C]//Adaptive Systems for Signal Processing, Communications, andControl Symposium2000. AS-SPCC. The IEEE2000. Lake Louise: IEEE,2000:153–158.
[129] Mallat S. A Wavelet Tour of Signal Processing[M]. San Diego: AccessOnline via Elsevier,1999.
[130] Mertins A. Signal Analysis: Wavelets, Filter Banks, Time-FrequencyTransforms and Applications[M]. Ultrasound in Biomedicine Research Series.New York:Wiley,1999. http://books.google.ca/books?id=hctqQgAACAAJ.
[131] Taswell C. The What, How, and Why of Wavelet Shrinkage Denoising[J].Computing in Science&Engineering,2000,2(3):12–19.
[132] Zhang Y, Wang Y, Wang W, et al. Doppler Ultrasound Signal DenoisingBased on Wavelet Frames[J]. Ultrasonics, Ferroelectrics and Frequency Control,IEEE Transactions on,2001,48(3):709–716.
[133] Ray P, Basuray A, Maitra A K. Optimum Wavelet Bases Selection forWavelet Based De-Noising in Partial Discharge Measurement[C]//Information&Communication Technologies (ICT),2013IEEE Conference on. Thuckalay:IEEE,2013:1110–1113.
[134] Coifman R R, Donoho D L. Translation-Invariant De-Noising[M]. NewYork: Springer,1995:125–150.
[135] Rioul O, Duhamel P. Fast Algorithms for Discrete and Continuous WaveletTransforms[J]. Information Theory, IEEE Transactions on,1992,38(2):569–586.
[136] Mallat S G. A Theory for Multiresolution Signal Decomposition: theWavelet Representation[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1989,11(7):674–693.
[137] DonohoDL. De-noising by Soft-thresholding[J]. IEEE Transactions onInformation Theory,1995,41(3):613–627.
[138] Ray P, Maitra A, Basuray A. A New Threshold Function for De-NoisingPartial Discharge Signal Based on Wavelet Transform[C]//Signal ProcessingImage Processing&Pattern Recognition (ICSIPR),2013InternationalConference on. Coimbatore: IEEE,2013:185–189.
[139] Gelb A. Applied Optimal Estimation[M]. USA: The MIT press,1974.
[140] Legrand F, Macabiau C. Real-time minimization of the total tracking errorin phase and delay lock loops-a second approach of the Fast AdaptiveBandwidth algorithm[C]//Proceedings of the57th Annual Meeting of TheInstitute of Navigation. Albuquerque: ION,2001:408–413.
[141] Legrand F, Macabiau C, Issler J L, et al. Improvement of PseudorangeMeasure-ments Accuracy by Using Fast Adaptive Bandwidth LockLoops[C]//Proceedings of the13th International Technical Meeting of theSatellite Division of The Institute of Navigation (ION GPS2000). Salt LakeCity: ION,2000:2346–2356.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |