UPSI Digital Repository (UDRep)
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Abstract : |
Global positioning system (GPS) has undergone intensive development, starting as an advanced specialized tool to a general-purpose gadget used in our daily lives. GPS exists in new technologies, applications, and consumer products, especially in smartphones and tablets. In a GPS receiver design, power consumption and localization accuracy are critical factors that affect the outcome of a GPS receiver system. Theoretically, increasing the number of required tracking channels in a GPS baseband receiver increases the design complexity and size of this system. Thus, power consumption can significantly increase. The receiver should acquire and track numerous satellites to improve the location accuracy of a position, thereby indicating that the receiver requires a high number of tracking channels. Thus, optimizing these tracking channels to balance the conflict among performance parameters is a difficult and challenging task.This paper presents a technique for order performance by similarity to ideal solution (TOPSIS) for solving complex situations for multi-criteria optimization of the tracking channels of GPS baseband telecommunication receiver. Nine operation modes of GPS receiver were evaluated by each design parameter, such as power consumption, localization accuracy, and time with no position available for static and dynamic positioning. Then, the TOPSIS was utilized and implemented to measure and rank the overall performance of tracking channel selection. Results of this study indicate that (1) multi-objective optimization is a reliable strategy for visualizing the trade-off among the GPS design parameters and providing a dynamic power consumption planning. (2)The best aggregated performance of the GPS receiver occurs when the number of tracking channels equals five and six for static and dynamic positioning, respectively. (3) The most frequent number of available satellites is eight, whereas the other number of satellites is a rare case to acquire.However, GPS standards require that available GPS satellites are constantly 12 at any time and place. |
References |
1. Aeroflex GPSG-1000 Portable Satellite Simulator. (n.d.). Retrieved from http://ats.aeroflex.com/gps-simulators-products/gps-simulators-products-2. 2. Ahsan, M. R., Islam, M. T., Habib Ullah, M., Mansor, M. F., & Misran, N. (2016). Dual band printed patch antenna on ceramicpolytetrafluoroethylene composite material substrate for GPS and WLAN applications. Telecommunication Systems, 62, 747–756. 3. Alkan, R. M., ˙Ilçi, V., Ozulu, I. M., & Saka, M. H. (2015). A comparative study for accuracy assessment of PPP technique using GPS and GLONASS in urban areas. Measurement, 69, 1–8. 4. Aydin, O., Yıldız, H. A., Ozoguz, S., & Toker, A. (2017). MOS-only complex filter design for dual-band GNSS receivers. AEU-International Journal of Electronics and Communications.Retrieved from http://www.sciencedirect.com/science/article/pii/ S1434841117310932. 5. Azzedine, B., & Sheetal, V. (2003). A performance evaluation of a dynamic source routing discovery optimization protocol using GPS system. Telecommunication Systems, 22(1–4), 337–354. 6. Belal, N. A., Nur, F. E., Hazura, M., Zaidan, A. A., & Zaidan,B. B. (2016). An evaluation and selection problems of OSS-LMS packages. SpringerPlus, 5, 1–35. 7. Bingyuan, W., Mei, G., Jing, T., Lei, L., & Liwen, W. (2011). Speed measurement for friction test vehicle based on GPS/hall sensor information fusion. Procedia Engineering, 17, 39–45. 8. Borre, K., Akos, D. M., Bertelsen, N., Rinder, P., & Jensen, S. H.(2007). A software-defined GPS and Galileo receiver: A singlefrequency approach. Springer. Retrieved from http://books.google. com/books?hl=en&lr=&id=x2g6XTEkb8oC&oi=fnd&pg=PR9& dq=software-defined+gps+and+galileo+receiver+a+singlefrequency+approach&ots=cTuL_xHcRV&sig=Y_-HTTUThXJStF-UhbzJzddEeA. 9. Cheng, K.-W., Natarajan, K., & Allstot, D. (2009). A 7.2 mW quadrature GPS receiver in 0.13µm CMOS. In Solid-State Circuits Conference-Digest of Technical Papers, 2009. ISSCC 2009. IEEE International, IEEE (pp. 422–423). Retrieved from http://ieeexplore.ieee.org/xpls/ abs_all.jsp?arnumber=4977488. 10. Chou, S.-Y., Chang, Y.-H., & Shen, C.-Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132–145. 11. Colleen, H. (2002). Operation and application of global positioning system ‘p. 13’. Crosslink, 3(2), 56. 12. Cui, Y., & Ge, S. S. (2003). Autonomous vehicle positioning with GPS in urban canyon environments. IEEE Transactions on Robotics and Automation, 19(1), 15–25. 13. Dana, P. H. (1997). Global Positioning System (GPS) time dissemination for real-time applications. Real-Time Systems, 12(1),9–40. 14. Daniel, C., & Antonio, A. F. L. (2001). GPS/ant-like routing in ad oc networks. Telecommunication Systems, 18(1–3), 85–100. 15. Das, R. C., & Alam, T. (2014). Location based emergency medical assistance system using openstreetmap. In International Conference on Informatics, Electronics & Vision (ICIEV), 2014 (pp. 1–5).IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp? arnumber=6850695. 16. Das, R. C., Purohit, P. P., Alam, T., & Chowdhury, M. (2014).Location based ATM locator system using OpenStreetMap. In 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) 2014 (pp. 1–6).IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp? arnumber=7083518. 17. De Angelis, G., De Angelis, A., Pasku, V., Moschitta, A., & Carbone, P. (2016). An experimental system for tightly coupled integration of GPS and AC magnetic positioning. IEEE Transactions on Instrumentation and Measurement, 65(5), 1232–1241. 18. DoD, U. S. (2001). Global positioning system standard positioning service performance standard. Assistant Secretary of Defense for Command, Control, Communications, and Intelligence. 19. Dogan, U., Uludag, M., & Demir, D. O. (2014). Investigation of GPS positioning accuracy during the seasonal variation. Amsterdam: Elsevier. 20. Drawil, N. M., Amar, H. M., & Basir, O. A. (2013). GPS localization accuracy classification: A context-based approach. IEEE Transactions on Intelligent Transportation Systems, 14(1), 262–273. 21. Fang, S., & Zimmermann, R. (2011). EnAcq: energy-efficient GPS trajectory data acquisition based on improved map matching. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp.221–230). ACM. Retrieved from http://dl.acm.org/citation.cfm? id=2094004. 22. Farmer, D. G., Lau, C. Y., Martin, K. A., & Rodal, E. B. (1997).GPS receiver having a low power standby mode. Google Patents.Retrieved from http://www.google.com/patents/US5592173. 23. Ferrara, V., Pietrelli, A., Chicarella, S., & Pajewski, L. (2017). GPR/GPS/IMU system as buried objects locator. Measurement Retrieved from http://www.sciencedirect.com/science/article/pii/ S0263224117302920. 24. Hafidhi, M. M., Boutillon, E., & Dion, A. (2016). Localisation in a faulty digital GPS receiver. In Conference on Design and Architectures for Signal and Image Processing (DASIP), 2016 (pp. 223–224). IEEE. Retrieved from http://ieeexplore.ieee.org/ abstract/document/7853824/. 25. Jo, K., Chu, K., & Sunwoo, M. (2012). Interacting multiple model filter-based sensor fusion of GPS with in-vehicle sensors for real-time vehicle positioning. IEEE Transactions on Intelligent Transportation Systems, 13(1), 329–343. 26. Jumaah, F. M., Hashim, S. J., Sidek, R., & Rokhani, F. (2013). Low power GPS baseband receiver design. In 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC), 2013 (pp. 65–68). 27. Kahraman, C., & Çebı, S. (2009). A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Expert Systems with Applications, 36(3), 4848–4861. 28. Kao, W.-W. (1991). Integration of GPS and dead-reckoning navigation systems. In Vehicle Navigation and Information Systems Conference, 1991 (Vol. 2, pp. 635–643). IEEE. Retrieved from http://ieeexplore.ieee.org/ xpls/abs_all.jsp?arnumber=1623672. 29. Kaplan, E. D., & Hegarty, C. J. (2005). Understanding GPS: principles and applications. Artech house. Retrieved from http://books.google.com.my/books?hl=en&lr=&id=- sPXPuOW7ggC&oi=fnd&pg=PR7&dq=Understanding+GPS, +principles+and+applications&ots=2s-CyyOLoB&sig= jW8lOSMORsnK1-WXvOUnh_hbOpY. 30. Kos, T., Markezic, I., & Pokrajcic, J. (2010). Effects of multipath reception on GPS positioning performance. In ELMAR, 2010 Proceedings (pp. 399–402). IEEE. Retrieved from http://ieeexplore. ieee.org/xpls/abs_all.jsp?arnumber=5606130. 31. Kudithipudi, D., Petko, S., & John, E. B. (2008). Caches for multimedia workloads: power and energy tradeoffs. IEEE Transactions on Multimedia, 10(6), 1013–1021. 32. Lehtinen, M., Happonen, A., & Ikonen, J. (2008). Accuracy and time to first fix using consumer-grade GPS receivers. In 16th International Conference on Software, Telecommunications and Computer Networks, 2008. SoftCOM 2008 (pp. 334–340).IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp? arnumber=4669506. 33. Lin, K., Kansal, A., Lymberopoulos, D., & Zhao, F. (2010).Energy-accuracy trade-off for continuous mobile device location.In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (pp. 285–298). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1814462. 34. Liu, J., Priyantha, B., Hart, T., Ramos, H. S., Loureiro, A. A., & Wang, Q. (2012). Energy efficient GPS sensing with cloud offloading. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (pp. 85–98). ACM. Retrieved from http:// dl.acm.org/citation.cfm?id=2426666. 35. Liu, K., Lim, H. B., Frazzoli, E., Ji, H., & Lee, V. (2014). Improving positioning accuracy using GPS pseudorange measurements for cooperative vehicular localization.IEEE Transactions on Vehicular Technology, 63(6), 2544–2556. 36. Meng, T. H. (1998). Low-power GPS receiver design. In IEEE Workshop on Signal Processing Systems, 1998. SIPS 98. 1998 (pp.1–10). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=715763. 37. Misra, P. K., Hu, W., Jin, Y., Liu, J., Souza de Paula, A., Wirstrom,N., & Voigt, T. (2014). Energy efficient GPS acquisition with sparse-gps. In Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (pp. 155–166). IEEE Press. Retrieved from http://dl.acm.org/citation.cfm?id=2602357. 38. Montenbruck, O., Steigenberger, P., Prange, L., Deng, Z., Zhao, Q.,Perosanz, F., et al. (2017). The multi-GNSS experiment (MGEX)of the international GNSS service (IGS)-achievements, prospects and challenges. Advances in Space Research, 59(7), 1671–1697. 39. Mumford, P. J., Parkinson, K., & Dempster, A. G. (2006). The namuru open GNSS research receiver. In Proceedings of 19th International Technical Meeting of the Satellite Division of the US, Inst. of Navigation, Fort Worth, Texas (pp. 26–29). Citeseer. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.63.2981&rep=rep1&type=pdf. 40. Namgoong, W., Reader, S., & Meng, T. H. (2000). An all-digital low-power IF GPS synchronizer. IEEE Journal of Solid-State Circuits, 35(6), 856–864. 41. Nordin, N. A. M., Zaharudin, Z. A., Maasar, M. A., & Nordin, N. A.(2012). Finding shortest path of the ambulance routing: Interface of A* algorithm using C# programming. In IEEE Symposium on Humanities, Science and Engineering Research (SHUSER), 2012 (pp. 1569–1573). IEEE. Retrieved from http://ieeexplore.ieee.org/ xpls/abs_all.jsp?arnumber=6268841. 42. NovAtel. (2003, December 3). GPS Position Accuracy Measures. Retrieved from http://support.novatel.com/attachments/ token/ivzihuupjvuiwya/?name=apn029.pdf. 43. Ogle, J., Guensler, R., Bachman, W., Koutsak, M., & Wolf, J.(2002). Accuracy of global positioning system for determining driver performance parameters. Transportation Research Record:Journal of the Transportation Research Board, 1818(1), 12–24. 44. Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. 45. Paul, S., Chatterjee, S., Mukhopadhyay, S., & Bhunia, S.(2011). Energy-efficient reconfigurable computing using a circuitarchitecture-software co-design approach. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 1(3), 369–380. 46. Qader, M. A., Zaidan, B. B., Zaidan, A. A., Ali, S. K., Kamaluddin,M. A., & Radzi, W. B. (2017). A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement, 111, 38–50. 47. Qahtan, M. Y., Zadain, A. A., Zaidan, B. B., Lakulu, M. B., & Rahmatullah, B. (2017). Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques. International Journal of Pattern Recognition and Artificial Intelligence, 31(3), 1–24. 48. Qi, H., & Moore, J. B. (2002). Direct Kalman filtering approach for GPS/INS integration. IEEE Transactions on Aerospace and Electronic Systems, 38(2), 687–693. 49. Rao, K. D., & Narayana, J. L. (1995). An approach for a faster GPS tracking extended Kalman filter. Navigation, 42(4), 619–630. 50. Raskovic, D., & Giessel, D. (2007). Battery-Aware embedded GPS receiver node. In Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services,2007. MobiQuitous 2007 (pp. 1–6). IEEE. Retrieved from http:// ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4450986. 51. Ravindran, A. R. (2008). Operations research methodologies. CRC Press. Retrieved from https://books.google.com/books?hl=en&lr=&id=iYNlruLcUVIC&oi=fnd&pg=PP1&dq= Operations+research+methodologies&ots=gA4ty8Q2f4&sig= pXDCmS-7Bh1pugb21GboDWYYgvw. 52. Salman, O. H., Zaidan, A. A., Zaidan, B. B., Naser Kalid, M., & Hashim, M. (2017). Novel methodology for triage and prioritizing using “big data” patients with chronic heart diseases through telemedicine environmental. International Journal of Information Technology & Decision Making, 16(4), 1–35. 53. Sato, G., Asai, T., Sakamoto, T., & Hase, T. (2000). Improvement of the positioning accuracy of a software-based GPS receiver using a 32-bit embedded microprocessor. IEEE Transactions on Consumer Electronics, 46(3), 521–530. 54. Schrader, D. K., Min, B.-C., Matson, E. T., & Dietz, J. E. (2016).Real-time averaging of position data from multiple GPS receivers.Measurement, 90, 329–337. 55. Serpelloni, E., Casula, G., Galvani, A., Anzidei, M., & Baldi,P. (2006). Data analysis of Permanent GPS networks in Italy and surrounding region: application of a distributed processing approach. Annals of Geophysics, 49(4–5). Retrieved from http://www.annalsofgeophysics.eu/ index.php/annals/article/viewArticle/4410. 56. Shih, H.-S., Shyur, H.-J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7), 801–813. 57. Shivaramaiah, N. C., & Dempster, A. G. (2012). Baseband Hardware Designs in Modernised GNSS Receivers. INTECH Open Access Publisher. Retrieved from http://www.intechopen.com/source/pdfs/27709/InTech-Baseband_hardware_designs_in_modernised_gnss_receivers.pdf. 58. Shun, T. W., & Jean, L. C. W. (2004). SABAGAR: A simple attribute-based addressing and GPS-aided routing protocol for applications in wireless sensor networks. Telecommunication Systems, 26(2–4), 197–212. 59. Stojanovic, V., Markovic, D., Nikolic, B., Horowitz, M. A., & Brodersen, R. W. (2002). Energy-delay tradeoffs in combinational logic using gate sizing and supply voltage optimization. In Proceedings of the 28th European Solid-State Circuits Conference, 2002. ESSCIRC 2002 (pp. 211–214). IEEE. Retrieved from http:// ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1471503. 60. Sun, X., Han, C., & Chen, P. (2017). Precise real-time navigation of LEO satellites using a single-frequency GPS receiver and ultrarapid ephemerides. Aerospace Science and Technology, 67, 228–236. 61. Synopsys Design Compiler. (n.d.). Retrieved from http://www.synopsys.com/tools/implementation/rtlsynthesis/dcgraphical/Pages/default.aspx. 62. Tang, B. Z., Longfield, S., Bhave, S. A., & Manohar, R. (2012).A low power asynchronous GPS baseband processor. In 18th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), 2012 (pp. 33–40). IEEE. Retrieved from http:// ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6243879. 63. Teague, W. R. (1999). GPS/Map Position Coordinate Issues: GPS Position Accuracy. Cooperative Extension Service, University of Arkansas, US Department of Agriculture, and county governments cooperating. 64. Tsui, J. B.-Y. (2005). Fundamentals of Global Positioning System Receivers: A Software Approach. Wiley Online Library. Retrieved from http://onlinelibrary.wiley.com/doi/10. 1002/0471200549.fmatter_indsub/summary. 65. Williams, A. C., Brown, A. D., & Zwolinski, M. (2000). Simultaneous optimisation of dynamic power, area and delay in behavioural synthesis. In IEE Proceedings-Computers and Digital Techniques (Vol. 147, pp. 383–390). IET. Retrieved fromhttp://ieeexplore.ieee. org/xpls/abs_all.jsp?arnumber=903233. 66. Zaidan, B. B., Zaidan, A. A., Karim, H. A., Ahmad, N. N. (2016). A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’. Software: Practice and Experience (pp.1–14). 67. Zaidan, B. B., & Zaidan, A. A. (2017a). Software and hardware FPGA-based digital watermarking and steganography approaches:Toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques”. Journal of Circuits, Systems and Computers, 26(7), 1750116. 68. Zaidan, B. B., Zaidan, A. A., Abdul Karim, H., & Ahmad, N. N. (2017b). A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology & Decision Making, 16, 1–41. 69. Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015b). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390–404. 70. Zaidan, A. A., Zaidan, B. B., Hussain, Muzammil, Ahmed Haiqi,M. L., Kiah, Mat, & Abdulnabi, Mohamed. (2015a). Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decision Support Systems, 78, 15–27. 71. Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Tamošaitien˙e, J. (2009). Multi-attribute decision-making model by applying grey numbers. Informatica, 20(2), 305–320. 72. Zhang, L., Liu, J., Jiang, H., & Guan, Y. (2013). Senstrack: Energyefficient location tracking with smartphone sensors. IEEE Sensors Journal, 13(10), 3775–3784. |
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