Position, Navigation, and Timing Technologies in the 21st Century. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

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for Space Communications Workshop, September 2012, pp. 139–146.

      77 77 J. del Peral‐Rosado, J. Lopez‐Salcedo, G. Seco‐Granados, F. Zanier, and M. Crisci, “Joint maximum likelihood time‐delay estimation for LTE positioning in multipath channels,” in Proceedings of EURASIP Journal on Advances in Signal Processing, special issue on Signal Processing Techniques for Anywhere, Anytime Positioning, September 2014, pp. 1–13.

      78 78 C. Gentner, B. Ma, M. Ulmschneider, T. Jost, and A. Dammann, “Simultaneous localization and mapping in multipath environments,” in Proceedings of IEEE/ION Position Location and Navigation Symposium, April 2016, pp. 807–815.

      79 79 C. Gentner, T. Jost, W. Wang, S. Zhang, A. Dammann, and U. Fiebig, “Multipath assisted positioning with simultaneous localization and mapping,” IEEE Transactions on Wireless Communications, vol. 15, no. 9, pp. 6104–6117, September 2016.

      80 80 3GPP2, “Recommended minimum performance standards for cdma2000 spread spectrum base stations,” 3rd Generation Partnership Project 2 (3GPP2), TS C.S0010‐E, March 2014. [Online]. Available: http://www.arib.or.jp/english/html/overview/doc/STD‐T64v7_00/Specification/ARIB_STD‐T64‐C.S0010‐Ev2.0.pdf

      81 81 L. Ljung, System identification: Theory for the User, 2nd Ed., Prentice Hall PTR, 1999.

      82 82 J. Proakis and D. Manolakis, Digital Signal Processing, Prentice Hall, Upper Saddle River, NJ, 1996.

      83 83 R. Norton, “The double exponential distribution: Using calculus to find a maximum likelihood estimator,” The American Statistician, vol. 38, no. 2, pp. 135–136, May 1984.

      84 84 D. H. Won, J. Ahn, S. Lee, J. Lee, S. Sung, H. Park, J. Park, and Y. J. Lee, “Weighted DOP with consideration on elevation‐dependent range errors of GNSS satellites,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 12, pp. 3241–3250, December 2012.

      85 85 J. Spilker, Jr., Global Positioning System: Theory and Applications. Washington, DC: American Institute of Aeronautics and Astronautics, 1996, ch. 5: “Satellite Constellation and Geometric Dilution of Precision,” pp. 177–208.

      86 86 University of California, San Diego, “Garner GPS archive,” http://garner.ucsd.edu/, accessed November 23, 2015.

      87 87 D. Gebre‐Egziabher, “What is the difference between ’loose,’ ’tight,’ ’ultra‐tight’ and ’deep’ integration strategies for INS and GNSS,” Inside GNSS, pp. 28–33, January 2007.

      88 88 J. Morales and Z. Kassas, “Distributed signals of opportunity aided inertial navigation with intermittent communication,” in Proceedings of ION GNSS Conference, September 2017, pp. 2519–2530.

      89 89 J. Morales and Z. Kassas, “A low communication rate distributed inertial navigation architecture with cellular signal aiding,” in Proceedings of IEEE Vehicular Technology Conference, 2018, pp. 1–6.

      90 90 R. Snay and M. Soler, “Continuously operating reference station (CORS): history, applications, and future enhancements,” Journal of Surveying Engineering, vol. 134, no. 4, pp. 95–104, November 2008.

       Subbu Meiyappan, Arun Raghupathy, and Ganesh Pattabiraman

       NextNav LLC, United States

      Satellite‐based systems can provide good‐quality positioning in clear‐sky outdoor environments and in some light indoor environments. A number of satellite systems have been developed for positioning and navigation such as GPS [1], Glonass [2], BeiDou [3], and Galileo [4], which all fall under the umbrella of global navigation satellite systems (GNSSs). However, all these satellite systems are limited in terms of their availability in deep‐indoor environments, due to their link budget, as well as in dense urban environments due to signal blockage. Terrestrial positioning systems can complement satellite‐based systems and work in environments where satellite‐based system performance is challenged.

      In this chapter, the transmitters of terrestrial broadcast systems are also referred to as beacons.

      Terrestrial positioning systems can be classified based on their geographic scale:

      1 Wide‐area terrestrial systems

      2 Local‐area terrestrial systems

      Wide‐area terrestrial systems have a wide coverage area extending beyond a building/venue, for example, to a metropolitan area. In contrast, local‐area terrestrial positioning systems such as WiFi and Bluetooth Low Energy (BT‐LE) are restricted in terms of their coverage. Among wide‐area terrestrial positioning systems, there are some signals such as TV, AM/FW radio, and cellular, whose primary application is different from positioning but can be used as signals of opportunity for positioning (for example, see Chapter 35 and [5]). Since these systems are not purpose‐built for positioning, they all have limitations with respect to position quality.

      Terrestrial positioning systems can use a variety of metrics and methods to estimate the 2D position of the UE. Some systems may use signal strength metrics such as Received Signal Strength Indicator (RSSI) (e.g. WiFi 802.11a/g, BT‐LE, Polaris RFPM), whereas others may use pseudoranges or direct range measurements (e.g. UTDOA, OTDOA, MBS) to estimate position using some type of trilateration algorithm.

      Among terrestrial systems that use ranging, some use transmissions that are by design synchronized, whereas some others may not be synchronized and need additional timing observations for synchronization. One example is a system that uses DTV signals for positioning, where additional timing monitoring units are required to be deployed to estimate the timing errors and provide them to the UE.

Schematic illustration of broadcast, uplink, and bidirectional systems.

      This