本論文提出智慧型(smart)全球定位系統(GPS)接收機設計在窄頻干擾、高速動態與多路徑環境下之效能評估。在本論文的第一個部分，利用遞迴式類神經網路(recurrent neural network, RNN)預估器在GPS抗干擾系統之應用。考慮五種窄頻干擾源: (1)自動遞迴式干擾，(2)連續波單頻干擾，(3) 連續波多頻干擾，(4) 掃頻干擾與(5)脈波干擾。因為接收訊號之雜訊並非為高斯分怖，我們使用非線性結構RNN來即時預估所遭遇之窄頻帶干擾。在學習演算過程，採用NDEKF (node decoupled extended Kalman filter)法來達成較好的收斂速率、較少的計算時間與記憶體需求。同時我們也針對提出的NDEKF演算法與GEKF (global extended Kalman filter)法，進行計算複雜度與記憶體需求之分析與比較。模擬結果顯示我們提出的方法比傳統線性與非線性預估器，有較好的信號雜訊改善率與較低的均方預估誤差(mean squared prediction error)，並可抵抗較廣型態的干擾訊息環境。
在本研究的第二個部分，我們探討動態GPS載波相位量測之應用。在GPS接收機設計，窄迴路雜訊頻寬可減少熱雜訊所造成的相位顫抖(phase jitter)。然而，它同時會降低鎖相迴路之追蹤能力並造成週期滑脫(cycle slip)。根據可適頻寬原理，我們提出兩種智慧型載波迴路並在動態環境下改善其追蹤效能。在我們使用模糊控制中，選擇載波相位誤差與載波頻率誤差為輸入變數，來達成暫態與穩態情況下快速正確的控制數位式鎖相迴路。當載波相位誤差或頻率誤差增加時，智慧型迴路增加其迴路頻寬來達成快速鎖定。當兩者誤差同時減少時，迴路頻寬減小來改善定位準確度。藉由最大的動態行為參數資訊，我們設計的兩種載波迴路可提估以下優於傳統迴路之優點: (1)加寬鎖定範圍與拉入(pull-in)範圍，(2)加快拉入速度，(3)加大頻率斜坡範圍。模擬結果顯示，我們提出之智慧型接收機可提供較短的安定時間與較寬的搜尋時間，同時改善週期滑脫的發生。
一般而言，在GPS參考站與遠端接收機上所發生的多路徑效應是不一樣的。然而它已成為差分式GPS中最主要的定位誤差來源。在本研究的第三個部分，我們提出一個靜態GPS應用中具有多路徑抑制的接收機系統架構。它包括四個部分: (1)可適性路徑估測器，(2)多路徑干擾重建器，(3) 耙式(Rake-based)延遲鎖定迴路，(4) 耙式延遲相位鎖定迴路。在此僅考慮短路徑延遲所造成之效應(延遲時間在1.5 chip內)。為了在相關領域(correlation domain)估測反射路徑參數，我們採用快速傅利葉進行循環相關運算(circular correlation)來減少計算複雜度。同時利用可適性路徑估測器來估測多路徑效應中延遲路徑的各項參數。根據前項的預估參數，相對的多路徑成分啟動來完成延遲信號重建的功能。再將複製的延遲信號與具有多路徑效應的信號分別在載波鑑別器與碼鑑別器內部作相減的運算，如此一來便可將已消除多路徑干擾的信號送入耙式延遲鎖定迴路與耙式延遲相位鎖定迴路中來完成信號同步的功能。

The main goal of this thesis is design and analysis of smart GPS receivers in narrowband interference, high dynamic and multipath propagation environments. At the first part of this thesis, we propose a recurrent neural network (RNN) predictor for application in GPS anti-jamming systems. Five types of narrowband jammers, i.e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.
The second part of this study focuses on carrier phase measurement in dynamic GPS applications. For GPS receiver design, a narrow loop noise bandwidth is desirable to reduce the phase jitter due to thermal noise. However, this will deteriorate the capability of tracking loops and may result in cycle slips. Based on adaptive bandwidth criterion, two new designs for intelligent carrier loops are presented to improve carrier phase tracking in the presence of kinematic environments. A fuzzy logic controller (FLC), which uses the carrier phase and frequency errors as input data, is employed to provide rapid and accurate control of digital phase-locked loops (DPLL) in the transient and steady states. When the phase error or frequency error is large, the intelligent carrier loop increases the loop bandwidth adaptively and performs fast locking. Once the tracking errors are reduced, this tracking loop decreases the loop bandwidth and improves ranging accuracy. By utilizing the highest dynamic stress information, our type-I and type-II carrier loops are developed to offer several advantages over traditional methods in acquisition limitations, those being (1) wider lock range and pull-in range, (2) faster pull-in speed, and (3) larger frequency ramp range. Simulation results demonstrate that our fuzzy-based receiver does achieve a shorter settling time and broader acquisition range than conventional tracking loops while preventing the occurrence of cycle slipping.
Multipath errors are not identical to the GPS reference station and remote receivers. Thus, it becomes the dominant error source in differential GPS. In the third part of this study, a multipath mitigation tracking system is presented for static GPS applications. It is comprised of four function blocks, those being (1) adaptive path estimator (APE), (2) multipath interference reproducer (MPIR), (3) Rake-based delay locked loop (RB-DLL), and (4) Rake-based phase locked loop (RB-PLL). Only The short delay condition with delay less than 1.5 PN chip is considered here, because GPS pseudorange error envelope decreases to zero for delay time greater than 1.5 PN chip. In order to estimate reflection profile in the correlation domain, the FFT-based circular correlation and block average method (BAM) are utilized to offer significant savings in computational complexity. The APE estimates the delayed profiles and coefficients of the reflection signals. With the path parameters from APE, the corresponding multipath arms are activated to accomplish the multipath reproduction. These replica profiles are used for subtracting the reflection components from carrier and code discriminators before sending it into the Rake-based carrier/code tracking loops.

Abstract…………………………………………………………………..i
Contents………………………………………………………………….iv
List of Tables………………………………………………………….vii
List of Figures………………………………………………………..viii
1Introduction………………………………………………………………1
1.1Motivation……………………………………………………………1
1.2Scope of Research……………………………………………………3
1.2.1 Narrowband Interference Cancellation…………………………3
1.2.2 Kinematic Environments…………………………….…………4
1.2.3 Multi-path Environments………………………….……………6
1.3Thesis Organization…………………………………….…………….7
2Global Positioning System (GPS) Receiver……………….……….8
2.1L1 Signal Specification…………………………….………………8
2.2.1 C/A Code And Its Properties………………………………….10
2.2GPS Receiver Architecture………………………………………..12
2.3Signal Acquisition…………………………………………………14
2.4Carrier Tracking Loop……………………………………………..17
2.5Code Tracking Loop……………………………………………….18
3Narrowband Interference Suppression ………………………………20
3.1System Description…………………………………………………20
3.2Jamming Signal Models……………………………………………22
3.3Frequency Domain Excision……………………………………….23
3.4Linear Tapped Delay Line Predictor…………………………….26
3.5Proposed Recurrent Neural Network Predictor………………….27
3.5.1GDEKF-based Algorithm……………………………………..30
3.5.2NDEKF-based Algorithm……………………………………..32
3.5.3Complexity Analysis ……………………………………….34
3.6Simulation Results…………………………………………..35
4Intelligent Carrier Phase Tracking Loops………………………..47
4.1Mobile Carrier Phase Model…………………………….………..47
4.2Cycle-Slip Problem………………………………………………….49
4.3Type-I Intelligent Carrier Tracking Loop……….…………..50
4.3.1Phase/ Frequency Discriminator………………….……………51
4.3.2Fuzzy Logic Controller……………………………………….52
4.3.3Digital Carrier Loop Design…………………………………..55
4.3.4Simulation Results…………………………………………….60
4.4Type-II Carrier Tracking Loop ………………………………….69
4.4.1Fuzzy Bandwidth Controller (FBC) ………………………….69
4.4.2Filter Coefficient Transformation…………………………..70
4.4.3Simulation Results…………………………………………….72
5Multipath Mitigation Tracking System…………….……………….74
5.1Multipath System Descriptions ……………….…………………74
5.2FFT-Based Circular Correlation………………….……………….77
5.3Adaptive Path Algorithm………………………….…………………80
5.4Rake-based Delay Locked Loop………………….………………….82
5.5Rake-based Phase Locked Loop……………….…………………….86
5.6Simulation Results…………………………….…………………..88
6Conclusions……………………………………….………………………92
6.1Concluding Remarks…………………………………………………92
6.2Future Works…………………………………………………………94
Bibliography………………………………………….………………….96
Publication List…………………………………………………………102

[1] E. D. Kaplan: ‘Understanding GPS: principles and Application’ (Artech House, London, 1996.)
[2] J. B. Y. Tsui: ‘Fundamentals of global positioning system receivers, a software approach’ (John Wiley & Sons, Canada, 2000.)
[3] B. W. Parkinson, and Jr. J. J. Spilker: ‘Global positioning system: theory and applications’ (AAAI, USA, 1996.)
[4] S. M. Kay, ‘Fundamentals of statistical signal processing: estimation theory’, PTR Prentice Hall, New Jersey, 1993.
[5] S. Haykin, Neural networks, Prentice Hall, New Jersey, 1999.
[6] J. G. Proakis, M. Salehi, Contemporary communication systems using MATLAB, Book/Cole, Pacific Grove, 2000.
[7] S. Haykin, Adaptive filter theory, Prentice Hall international Inc., New Jersey, 1996.
[8] B. Kosko, Neural networks and fuzzy systems: a dynamical system approach to machine intelligence, Prentice Hall, USA, 1992
[9] R. L. Peterson, R. E. Ziemer and D. E. Borth, Introduction to spread spectrum communications, New York: Prentice Hall International, 1985.
[10] R. E. Best: ‘Phase-locked loops, theory, design and applications’ (Mc-Graw-Hill, Inc., 1993.)
[11] D. R. Stephens: ‘Phase-locked loops for wireless communications: digital and analog implementation’ (Prentice Hall, Kluwer Academic, 1998.)
[12] P. Misra and P. Enge, “Global positioning system: signals, measurements and performances”, Ganga-Jamuna Press.
[13] M. S. Grewal, L. R. Weill and A. P. Andrews, “Global positioning systems, inertial navigation and integration”, John Wiely & Sons Inc., 2001.
[14] J. K. Holmes: ‘Coherent spread spectrum systems’ (John Wiley & Sons, Inc., 1982.)
[15] M. S. Grewal, and A. P. Andrews, “Kalman filtering theory and practice”, Prentice-Hall Inc., 1993.
[16] “Navstar GPS L5 signal specification”, RTCA Inc., 2000.
[17] C. T. Lin, and C. S. George Lee,: ‘Neural fuzzy systems, a neuro-fuzzy synergism to intelligent systems’ (Prentice Hall, Upper Saddle River, 1996.)
[18] L. M. Li, and L. B. Milstein, “Rejection of pulsed CW interference in PN spread spectrum systems using complex adaptive filter”, IEEE Trans. Commun., vol.31, no.1, pp.11-20, Jan 1983.
[19] Y. C. Wang, and L. B. Milstein, “Rejection of multiple narrow-band interference in both BPSK and QPSK DS spread spectrum systems”, IEEE Trans. Commun., vol.36, no.2, pp. 195-204, Feb 1988.
[20] R. Vijayan and H. V. Poor, “Nonlinear techniques for interference suppression in spread spectrum systems” IEEE Trans. Commun., vol.38, no.7, pp. 1060-1065, July 1990.
[21] L. A. Rusch, and H. V. Poor, “Narrowband interference suppression in CDMA spread spectrum communications”, IEEE Trans. Commun., vol.42, no.2, pp. 1969-1979, Feb 1994.
[22] S. G. Glisic, A. Mammela, V. P. Kaasila, and M. D. Pajkovic, “Rejection of frequency sweeping signal in DS spread spectrum systems using complex adaptive filters”, IEEE Trans. Commun., vol.43, no. 1, pp. 136-145, Jan. 1995.
[23] R. Rifkin and J. J. Vaccaro, “Comparison of narrowband adaptive filter technologies for GPS”, IEEE Position Location and Navigation Symposium, pp. 125-131, March 2000.
[24] G. Dimos, T. Upadhyay and T. Jenkins, “Low-cost solution to narrowband GPS interference problem”, Aerospace and Electronics Conference, 1995. NAECON 1995, Proceedings of the IEEE, Vol. 1, pp. 145-153, May 1995.
[25] W. W. Jones and K. R. Jones, “Narrowband interference suppression using filter-bank analysis/synthesis technologies”, Military Communications Conference, MILCOM '92, Conference Record. 'Communications - Fusing Command, Control and Intelligence', pp. 898-902, 1992.
[26] P. R. Chang, and J. T. Hu, “Narrow-band interference suppression in spread spectrum CDMA communications using pipelined recurrent neural network”, IEEE Trans. Vehicular Technology., vol. 48, no. 2, pp. 467-477, March 1999.
[27] W. R. Wu, and F. F. Yu, “New nonlinear algorithm for estimating and suppressing narrowband interference in DS spread spectrum systems”, IEEE Trans. Commun., vol.44, no.4, pp. 508-515, April 1996.
[28] R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks”, Neural Compu., vol.1, pp 270-280, 1989.
[29] S. Haykin, and L. LI, ”Nonlinear adaptive prediction of nonstationary signal”, IEEE Trans. Signal Processing, vol.43, no.2, pp. 526-535, Feb. 1995.
[30] J. Baltersee, and J. A. Chambers, “Nonlinear adaptive prediction of speech with a pipelined recurrent neural network”, IEEE Trans. Signal Processing., vol.46, no.6, pp. 2207-2216 , Aug. 1998.
[31] M. S. Braasch, and A. J. Van Dierendonck, ”GPS receiver architecture and measurement”, Proc. of the IEEE., vol.87, no.1, pp. 48-64, Jan. 1999.
[32] R. Bijjani and P. K. Das, “Neural networks as exciser for spread spectrum communication systems” in Neural networks in telecommunications, Eds. B. Yuhas and N. Ansari, Kluwer Academic Publisher, 1994.
[33] J. T. Connor, R. D. Martin and L. E. Atlas, “Recurrent neural networks and robust time series prediction”, IEEE Trans. Nerual Network, vol. 1, pp. 240-254, March 1994.
[34] E. Varoglu and K. Hacioglu, “Speech prediction using recurrent networks”, IEE Electronic Letters, vol. 35, no. 16, pp. 1353-1355, Aug., 1999.
[35] L. A. Feldkamp, and G. V. Puskorius, “Neurocontrol of nonlinear dynamical system with Kalman filter trained recurrent networks”, IEEE Trans. Neural Networks, vol.5, no.2, pp. 279-297, March 1994.
[36] R. J. Landry, V. Calmettes, and M. Bousquet, “Impact of interference on a generic GPS receiver and assessment of mitigation techniques”, Proc. 5th International Symposium on Spread Spectrum Techniques and Applications, vol.1, pp.87—91, 1998.
[37] A. Perez-Neria, C. Anton-Haro, and M. A. Lagunas, ”An adaptive fuzzy logic enhancer for rejection of narrowband interference in DS-spread spectrum”, Proc. 48th Conf. on Vehicular Technology, vol.2, pp. 1598 —1602, 1998.
[38] G. Kechriotis, and E. Manolakos, “Using neural networks for nonlinear and chaotic signal processing”, ICASSP int. Conf. on Acoustics, Speech and Signal Processing, vol.1, pp.465-469, April 1993.
[39] C. Altmayer, “Enhancing the integrity of GPS/INS systems by cycle slip diction and correction”, Intelligent Vehicles Symposium Proceeding of IEEE, pp. 164-170, 2000.
[40] D. Simon, and H. El-Sherief: ‘Fuzzy logic digital phase-locked loop filter design’, IEEE Trans. Fuzzy Systems, 3, pp. 211-218, 1995.
[41] R. A. G. Ungiaub, and C. S. Tsang: ‘Phase tracking by fuzzy control loop’, IEEE Proceeding on Aerospace conference, 5, pp. 57-66, 1999.
[42] D. J. Jwo: ‘Optimisation and sensitivity analysis of GPS receiver tracking loops in dynamic environments’, IEE Proc. Rader, Sonar, Navig., 148, pp. 241-250, 2001.
[43] S. A. Stephen, and J. B. Thomas: ‘Controlled-root formulation for digital phase-locked loops’, IEEE Trans. Aerospace and Electronic Systems, 31, pp. 78-95, 1995.
[44] J. Lee and B. Kim, “A low-noise fast-lock phase-locked loop with adaptive bandwidth control”, IEEE Journal of Solid-State Circuits, vol. 35, no. 8, pp. 1137-1145, Aug., 2000.
[45] Y. Tang, Y. Zhou, S. Bibykl, and M. Ismail, “A low-noise fast-settling PLL with extended loop bandwidth enhancement by new adaptation technique”, Proceedings 14th Annual IEEE International ASIC/SOC Conference, pp. 93-97, 2001.
[46] W. Zhuang, “Performance analysis of GPS carrier phase observable”, IEEE Trans. Aerospace and Electronics Systems, vol. 30, no. 2, pp. 754-767, Apr., 1996.
[47] W. H. Sheen and C. H. Tai, ”A noncoherent tracking loop with diversity and multipath interference cancellation for direct-sequence spread-spectrum systems”, IEEE Trans. Commun., vol.46, no.11, pp. 1516-1524, Nov. 1998.
[48] M. C. Laxton and S. L. DeVilbiss, “GPS multipath mitigation during code tracking”, American Control Conference, vol. 3, pp. 1429 —1433, 1997.
[49] M. Rlinami, H. Morikawa, and T. Aoyama, “An adaptive multipath mitigation technique for GPS signal reception”, Vehicular Technology Conference Proceedings, vol. 2, pp. 1625—1629, 2000.
[50] T. Felhauer and L. Botchkovski, “Impact of multipath signal reception on signal tracking in spread spectrum satellite navigation receivers”, Spread Spectrum Techniques and Applications Proceedings, IEEE 4th International Symposium, Vol. 2, pp. 862-866, Sept. 1996.
[51] R. D. J. Van Nee, “Spread-spectrum code and carrier synchronization errors caused by multipath and interference”, IEEE Trans. Aerospace and Electronics System, vol. 29, no. 4, pp. 1359-1365, 1993.
[52] W. Zhuang and J. M. Tranquilla, “Effect of multipath and antenna on GPS observables”, IEEE. Proc.-Radar, Sonar and Navig., Vol. 142, no. 5, pp. 267-275, Oct. 1995.
[53] A. J. Van Dierendonck, P. Fenton, and T. Ford, “Theory and performance of narrow correlator spacing in a GPS receiver”, Proceeding of the National Technical Meeting, ION, San Diego, CA, pp. 115-124, 1992.
[54] L. Weil and B. Fisher, “Method of mitigating multipath effects in radio ranging system”, U.S. Patent, No. 6,031,881, Feb. 29th, 2000.
[55] M. .S. Braasch, “Performance comparison of multipath mitigating receiver architecture“, Aerospace Conference, IEEE Proceedings, Vol. 3, pp. 1309-1315, March 2001, 2001.
[56] M. S. Braasch, “GPS multipath model validation”, Position Location and Navigation Symposium, pp. 672-678, April 1996.
[57] B. Townsend, J. Wiebe, A. Jakab, M. Clayton, and T. Murfin, “Analysis of the multipath meter performance in environments with multiple interferers”, ION GPS 2000, Salt Lake, UT, pp. 480-488, 2000.
[58] C. R. Cahn, and M. M. Chansarkar, “Multipath corrections for a GPS receiver”, ION GPS, pp. 551-557, 2000.