Modelling the L-Band Air to Ground Channel for Navigation Applications (2016)

The communication, navigation and surveillance (CNS) infrastructure in the sector of civil aviation is currently undergoing a major innovation process: old legacy systems, often developed as early as the 1950s, are being replaced by new, more efficient systems. The change is necessary as the future airspace will demand higher traffic volumes and more efficient flight operations. In the field of navigation, global navigation satellite systems (GNSS) will become the primary source of aircraft navigation. Nevertheless, ground based radionavigation systems will still play a vital role as alternative positioning navigation and timing (APNT) systems in the future navigation infrastructure [1]. APNT systems are used as backup in case the primary means of navigation using GNSS becomes unavailable. An unavailability of GNSS may be due to intentional or unintentional interference or system failures. According to current plans in the US and Europe, distance measurement equipment (DME) will be the first APNT system employed using two way ranging [2],[3]. The use of DMEs limits the economic impact as most of its infrastructure already exists. However, over the long term a transition to modern systems such as the L-band digital aeronautical communication system (LDACS) or automatic dependent surveillance – broadcast (ADS-B) might be indispensable [4], [5] as only highly bandwidth efficient systems in combination with passive ranging schemes can enable higher capacity and improved ranging and positioning performance [6]. A major challenge is to guarantee that APNT systems meet the performance requirements on accuracy, integrity, capacity, and coverage to guarantee safe and efficient airspace operations in case of a GNSS outage. Legacy systems such as DME were developed to support less stringent requirements on ranging accuracy compared to today’s and tomorrow’s GNSS. Yet it is unclear how well ground based navigation systems perform compared to the more stringent performance requirements of GNSS based navigation [7]. The propagation characteristics of the air-ground (AG) radio channel, and hereby especially multipath propagation, have been identified as the main source of ranging errors for ground based radionavigation systems. The ranging performance of individual ground based radionavigation systems like DME, LDACS1, or ADS-B over the AG channel has been measured in the past by means of flight trials [8],[9],[10]. However, flight trials are very complex and cost extensive and require a long lead time: the number of flight trajectories, systems or configurations which can be covered is limited. Thus, flight trials are not practical for an extensive testing of different current or future ground based radionavigation systems. Flight trials can be augmented by computer simulations applying a sound and accurate theoretical model of the AG propagation characteristics. Hereby, the evolution of propagation characteristic over time should be modeled. This includes the exact knowledge of the propagation delays, amplitudes, and “lifetimes” of all multipath components. Currently, no accurate channel model in the relevant L-band exists for testing the ranging performance of ground based radionavigation systems. Previously developed statistical AG channel models are only relevant for a rough analysis of communication systems and not suitable for an accurate performance evaluation of ground based radionavigation systems [11]. Therefore, in this paper we present a novel type of AG channel model which is based on a comprehensive geometrical statistical channel model (GSCM) approach. Compared to a purely statistical channel model, a GSCM represents propagation effects more closely connected to their original physical cause, e.g., a multipath component (MPC) is not modeled as a purely statistical event but as a reflection originating from a specific obstacle. Thus, a GSCM is able to describe not only the statistical channel properties but also the evolution of the various propagation properties over time. This makes a GSCM very attractive in the context of range estimation. The parameters of the novel GSCM for AG propagation should be based on data collected during different propagation measurements [12],[13]. In our model, we cover the main propagation effects which are the line of sight (LoS) path, the ground or earth surface MPC, lateral MPCs, as well as diffuse MPCs. A brief description of these components follows. The line of sight (LoS) path is the direct propagation path between a ground station and aircraft. The received power of the LoS component usually follows free space loss conditions but may also be attenuated according to the Tx and Rx antenna patterns and by obstructions (often buildings or terrain). The bending of the electromagnetic waves due to the troposphere’s composition can be modelled using existing methods [14]. Ground multipath propagation originates from a reflection off the ground with (for most practical link distances) short delay relative to the LoS component; from its phase variation with path length, this usually manifests itself by the attenuation of the LoS component (the well-known 2-ray effect). Thus, ground multipath propagation causes fades of the received LoS power. However, measurements have shown that the ground MPC is not always present or exhibits only weak power levels [15]. Therefore, we propose modelling ground multipath propagation by characterization of specific reflecting areas on the ground. Once such an area is defined, the interaction of the ground MPC with the LoS component, and the resulting attenuation or amplification of the latter, follows directly from the underlying geometry. By lateral MPCs we mean signal components that originate from reflections outside the two-dimensional plane defined by the LoS and primary surface reflection. In most cases these are attributable to buildings, large structures or terrain; unlike the primary surface reflection, lateral MPCs have a relatively large delay relative to the LoS path. We propose modelling those MPCs as point reflectors. Each of these point reflectors has a specific location. Diffuse MPCs are caused by clusters of point reflectors or scattering. Thus, diffuse MPCs can usually not be modelled as a single point reflector. As their power level is generally significantly lower than the power level of the other types of MPCs, their influence on range estimation is usually minor. Diffuse MPCs, therefore, can be modelled as a statistical process. In the final paper we present the details of the new AG channel model in a comprehensive way, and show that the model components we have described follow the findings of the propagation measurement campaigns. For each of the propagation effects, we give examples from different sets of measurement data. The presented model allows to test the range estimation performance of different ground based radionavigation systems for any flight trajectories. References [1] L. Eldredge, P. Enge, M. Harrison, R. Kenagy, S. Lo, R. Loh, R. Lilley, M. Narins, and R. Niles, “Alternative Positioning, Navigation and Timing (PNT) Study,” in International Civil Aviation Organization Navigation Systems Panel (NSP), 2010. [2] M. Kayton and W. R. Fried, Avionics Navigation Systems, 2nd ed. John Wiley & Sons, Inc., 1997. [3] R. Lilley and R. Erikson, “DME / DME for Alternate Position, Navigation, and Timing (APNT),” in FAA APNT White Paper, 2012. [4] M. Schnell, U. Epple, D. Shutin, and N. Schneckenburger, “LDACS: Future Aeronautical Communications for Air-Traffic Management,” IEEE Commun. Mag., vol. 52, no. 5, pp. 104–110, 2014. [5] Y.-H. Chen, S. Lo, S.-S. Jan, G.-J. Liou, D. Akos, and P. Enge, “Design and Test of Algorithms and Real-Time Receiver to use Universal Access Transceiver ( UAT) for Alternative Positioning Navigation and Timing (APNT ),” in ION GNSS+, 2014. [6] S. Lo, Y.-H. Chen, S. Zhang, and P. Enge, “Hybrid APNT?: Terrestrial Radionavigation to Support Future Aviation Needs,” in ION GNSS+, 2014. [7] S. Lo, Y.-H. Chen, P. Enge, B. Peterson, and R. Erikson, “Distance Measuring Equipment Accuracy Performance Today and for Future Alternative Position Navigation and Timing (APNT),” in ION GNSS+, 2013. [8] W. Pelgrum and K. Li, “An Investigation on the Contributing Factors of Enhanced DME Ranging Errors,” in ION GNSS+, 2015. [9] D. Shutin, N. Schneckenburger, M. Walter, and M. Schnell, “LDACS1 Ranging Performance - An Analysis of Flight Measurement Results,” in DASC, 2013. [10] S. Lo, Y. H. Chen, P. Enge, and M. Narins, “Techniques to Provide Resilient Alternative Positioning, Navigation, and Timing ( APNT ) Using Automatic Dependent Surveillance - Broadcast (ADS B) Ground Stations,” in ION ITM, 2015. [11] D. W. Matolak, “Air-ground channels and models: Comprehensive review and considerations for unmanned aircraft systems,” in IEEE Aerospace Conference, 2012. [12] N. Schneckenburger, T. Jost, D. Shutin, M. Walter, T. Thiasiriphet, M. Schnell, and U.-C. Fiebig, “Measurement of the L-band air-to-ground channel for positioning applications,” IEEE Trans. Aerosp. Electron. Syst. (submitted 2015). [13] D. W. Matolak and R. Sun, “Air-Ground Channel Characterization for Unmanned Aircraft Systems—Part I: Methods, Measurements, and Models for Over-water Settings,” IEEE Trans. Veh. Technol. (accepted Publ.), 2016. [14] G. H. Millman, “Atmospheric effects on VHF and UHF propagation,” in IEEERE International Convention, 1958, vol. 46, no. 8, pp. 1492–1501. [15] N. Schneckenburger, T. Jost, D. Shutin, and U.-C. Fiebig, “Line of sight power variation in the air to ground channel,” in EUCAP (accepted), 2016.