کاهش اثر تداخل در سامانه ناوبری GPS با استفاده از فیلتر شکاف تکاملی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی برق، دانشگاه علم و صنعت ایران

2 دانشکده مهندسی برق، دانشگاه علم و صنعت ایران، نارمک، تهران 13114-16846، ایران

چکیده

با توجه به استفاده روزافزون سامانه ناوبری GPS در حوزه‌های مختلف، افزایش دقت و کارآیی این سامانه اهمیت ویژه‌ای دارد. سیگنال مخابره شده از ماهواره‌ها مسافت زیادی را تا رسیدن به گیرنده موجود در سطح زمین طی می‌کند که این امر منجر به کاهش توان سیگنال می‌گردد. این سیگنال ضعیف می‌تواند به‌راحتی تحت تأثیر سیگنال‌های تداخل عمدی (یا به اصطلاح جمینگ) و یا حتی غیرعمدی قرار گیرد. یکی از موزی‌ترین تداخل‌ها، جمینگ موج پیوسته (CW) است. محبوب‌ترین روش‌ کاهش تأثیر این تداخل بر روی سیگنال GPS فیلتر شکاف می‌باشد. بنابراین در این مقاله، برای مقابله با اثر جمینگ CW بر سیگنال GPS، استفاده از یک فیلتر شکاف تطبیقی با پاسخ ضریه نامحدود پیشنهاد گردیده است که برای تطبیق ضرایب آن متناسب با توان و فرکانس جمینگ اعمال‌شده، یکی از انواع الگوریتم تکاملی PSO به نام IPSO مورد استفاده قرار گرفته است. الگوریتم‌های تکاملی برای یافتن پاسخ مسائلی به­کار می‌روند که هیچ راه‌حل مشخصی برای آن‌ها وجود ندارد و این دقیقاً چیزی ‌است که برای رفع اشکال طراحی فیلتر دیجیتال مورد نیاز است. همچنین استفاده از الگوریتم‌ تکاملی منجر به‌سادگی روند تطبیق می‌شود چرا که از انجام عملیات ریاضی سخت و پیچیده جلوگیری می‌کند. در نهایت، کارآیی روش پیشنهادی با روش‌های مشابه مقایسه شده است. نتایج عدد نشان می‌دهد که روش پیشنهادی علاوه بر بهبود بسیار چشمگیر در شباهت سیگنال بازیابی شده به سیگنال بدون اختلال (به‌طور متوسط 99 درصد)، تعداد ماهواره‌های اکتساب شده را در تمام بازه توان‌ جمینگ، به شش ماهواره رسانده است. همچنین خطای موقعیت‌یابی کاربر را که به‌عنوان هدف اصلی گیرنده GPS می‌باشد به میزان  بسیار زیادی کاهش داده است.

کلیدواژه‌ها


عنوان مقاله [English]

Reducing Interference Effect on GPS Navigation System Using Evolutionary Notch Filter

نویسندگان [English]

  • M. Abbasi 1
  • S. M. Masoumi 2
1 Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 16846-13114, Iran
2 Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 16846-13114, Iran
چکیده [English]

As nowadays the GPS navigation system has more usage in different areas, increasing its efficiency and accuracy has gained more importance. The transmitted signal travels a long distance from the satellites to reach the receivers on the ground, so its power fades. This faded signal can easily be affected by intentional noises, the so-called jamming, or unintentional noises. One of the most destructive kinds of jamming is the continuous wave (CW) jamming. The most favored method for countering this jamming is the notch filter. Therefore, in this paper, an adaptive notch filter (ANF) with a narrow response in proposed to reduce the effects of CW jamming. A kind of PSO evolutionary algorithm called the improved particle swarm           optimization algorithm (IPSO) is used to adapt the filter’s coefficients according to the power and          frequency of the jamming signal. Evolutionary algorithms are used in problems without any straight        forward answer, and that is why we chose this method for designing the filter. It also reduces the complexity of solving such mathematical problems. Finally, the efficiency of the proposed method is compared to other similar solutions, showing a significant improvement in the similarity of recovered signal to the original signal (up to 99%), as well as an increase in the number of observed satellites up to 6, and error reduction in determining the user coordinates which is the primary goal of the GPS system.
 

کلیدواژه‌ها [English]

  • GPS
  • Jamming
  • Adaptive Notch Filter
  • Evolutionary Algorithm
   [1]      K. F. McDonald, P. J. Costa, and R. L. Fante, “Insights Into Jammer Mitigation via Space-Time Adaptive Processing,” in IEEE/ION Position, Location, And Navigation Symposium, pp. 213-217, 2006.##
   [2]      F. Ye, H. Tian, and F. Che, “CW Interference Effects on the Performance of GPS Receivers,” in IEEE Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), pp. 66-72, 2017.##
   [3]      Y. R. Chien, “Design of GPS anti-jamming Systems using Adaptive Notch Filters,” IEEE Systems Journal, vol. 9, no. 2, pp. 451-460, 2013.##
   [4]      D. Borio, C. O. Driscoll, and J. Fortuny, “Jammer Impact on Galileo and GPS Receivers,” in IEEE International Conference on Localization and GNSS (ICL-GNSS), pp. 1-6, 2013.##
   [5]      D. K. Kaur and M. S. Saini, “Design IIR Notch Filter and Comb Filter to Eliminate Unwanted Frequencies for Environment,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 3, no. 6, pp. 1-6, 2014.##
   [6]      S. Mei and K. Lin, “Adaptive Notch Filter for Single and Multiple Narrow-Band Interference,” Master Science Thesis, Edith Cowan University,  2001.##
   [7]      L. Tan and J. Jiang, “Digital Signal Processing: Fundamentals and Applications,” Academic Press, 2018.##
   [8]       Q. Lv and H. Qin, “A Novel Algorithm for Adaptive Notch Filter to Detect and Mitigate the CWI for GNSS Receivers,” in IEEE 3rd International Conference on Signal and Image Processing (ICSIP), pp. 444-451, 2018.##
   [9]      N. G. Ferrara, M. Z. H. Bhuiyan, S. Söderholm, L. Ruotsalainen, and H. Kuusniemi, “A New Implementation of Narrowband Interference Detection, Characterization, and Mitigation Technique for a Software-Defined Multi-GNSS Receiver,” GPS Solutions, vol. 22, no. 4, p. 106, 2018.##
[10]      S. W. Arif, A. Coskun, and I. Kale, “A Fully Adaptive Lattice-based Notch Filter for Mitigation of Interference in GPS,” in IEEE 15th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME), pp. 217-220, 2019.##
[11]      M. Mosavi and F. Shafiee, “Narrowband Interference Suppression for GPS Navigation using Neural Networks,” GPS Solutions, vol. 20, no. 3, pp. 341-351, 2016.##
[12]      M. R. Mosavi, M. S. Moghaddasi, and M. J. Rezaei, “A New Method for Continuous Wave Interference Mitigation in Single-Frequency GPS Receivers,” Journal of Wireless Personal Communications, vol. 90, no. 3, pp. 1563-1578, 2016.##
[13]      M. Pashaian, M. Mosavi, M. Moghaddasi, and M. Rezaei, “A Novel Interference Rejection Method for GPS Receivers,” Iranian Journal of Electrical and Electronic Engineering, vol. 12, no. 1, pp. 9-20, 2016.##
[14]      K. Borre, D. M. Akos, N. Bertelsen, P. Rinder, and S. H. Jensen, “A Software-defined GPS and Galileo Receiver: A Single-frequency Approach,” Springer Science & Business Media, 2007.##
[15]      W. L. Mao, “Novel SREKF-based Recurrent Neural Predictor for Narrowband/FM Interference Rejection in GPS,” AEU-International Journal of Electronics and Communications, vol. 62, no. 3, pp. 216-222, 2008.##
[16]      M. J. Rezaei and M. R. Mosavi, “Hybrid              anti-Jamming Approach for Kinematic Global Positioning System Receivers,” IET Signal Processing, vol. 12, no. 7, pp. 888-895, 2018.##
[17]      M. Abedi, M. J. Rezaei, and M. R. Mosavi, “Accurate Interference Mitigation in Global Positioning System Receivers based on Double-Step Short-time Fourier transform,” Journal of Circuits, Systems, and Signal Processing, vol. 37, no. 6, pp. 2450-2470, 2018.##
[18]      A. T. Balaei, A. G. Dempster, and L. L. Presti, “Characterization of the Effects of CW and Pulse CW Interference on the GPS Signal Quality,” IEEE Transactions on Aerospace and Electronic Systems, vol. 45, no. 4, pp. 1418-1431, 2009.##
[19]      M. A. Sharifi and H. Mojallali, “A Modified Imperialist Competitive Algorithm for Digital IIR Filter Design,” Optik, vol. 126, no. 21, pp.        2979-2984, 2015.##
[20]      P. A. Vikhar, “Evolutionary Algorithms: A Critical Review and its Future Prospects,” in IEEE International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pp. 261-265, 2016.##
[21]      X. S. Shen and J. Zhang, “Improvement of PSO Algorithm Based on Brown Motion and its Applications to Adaptive Filter,” in Advanced Materials Research, Trans. Tech. Publ., vol. 694, pp. 2695-2698, 2013.##
[22]      A. Gotmare, S. S. Bhattacharjee, R. Patidar, and N. V. George, “Swarm and Evolutionary Computing Algorithms for System Identification and Filter Design: A Comprehensive Review,” Swarm and Evolutionary Computation, vol. 32, pp. 68-84, 2017.##
[23]       P. Das, S. K. Naskar, S. Samanta, and S. N. Patra, “An Approach to Optimize FIR Filter Coefficients using GA, PSO, and BAT Algorithm and Their Comparative Analysis,” in IEEE International Conference on Computer, Electrical & Communication Engineering (ICCECE), pp. 1-6, 2016.##
[24]      R. Eberhart and J. Kennedy, “Particle Swarm Optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948, 1995.##
[25]      T. Ziyu and Z. Dingxue, “A Modified Particle Swarm Optimization with an Adaptive Acceleration Coefficients,” in IEEE Asia-Pacific Conference on Information Processing, vol. 2, pp. 330-332, 2009.##
 [26]      G. Bao and K. Mao, “Particle Swarm Optimization Algorithm with Asymmetric Time Varying Acceleration Coefficients,” in IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2134-2139, 2009.##
[27]      Z. Cui, J. Zeng, and Y. Yin, “An improved PSO with Time-varying Accelerator Coefficients,” in 8th IEEE International Conference on Intelligent Systems Design and Applications, vol. 2, pp. 638-643, 2008.##
[28]      N. Rahemi, M. Mosavi, A. Abedi, and S. Mirzakuchaki, “Accurate solution of Navigation Equations in GPS Receivers for very High Velocities using Pseudorange Measurements,” Journal of Advances in Aerospace Engineering, vol.2014, pp. 1-8, 2014.##
[29]      M. J. Rezaei, M. R. Mosavi, “A New Method for Cancelling CW Jamming in GPS Receivers,” Journal of Electronical & Cyber Defence, vol. 4, no. 1, pp. 69-78, 2016 (in Persian).##