Abstract
One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.
Highlights
Several Location-Based Services (LBS) for personal navigation and Intelligent Transport Systems (ITS) are subject to the availability of robust positioning, navigation and timing measurements.The Global Navigation Satellite Systems (GNSS) provides a positioning solution that is free of drift as opposed to dead-reckoning systems such as the Inertial Navigation System (INS) [1,2]
The primary observable from a GNSS signal is what is termed the pseudorange, which is essentially the difference in time between when a signal is transmitted from the satellite, and the time at which it is received at the receiver antenna, multiplied by the speed of light
The ground stations in the control segment determine and transmit clock correction parameters to the satellites for rebroadcast in the navigation message. These correction parameters are implemented by the receiver using the second-order polynomial [23]: dtp = a f 0 + a f 1 (t − toc ) + a f 2 (t − toc )2 + ∆tr where af0 is the clock bias; af1 is the clock drift; af2 is the frequency drift; toc is the clock data reference time; t is the current time epoch; ∆tr is the correction due to relativistic effects. This residual error is dependent on the Age Of Data (AOD), which is the time elapsed since the most recent control segment upload to a satellite
Summary
Several Location-Based Services (LBS) for personal navigation and Intelligent Transport Systems (ITS) are subject to the availability of robust positioning, navigation and timing measurements. Novel airspace structuring concepts are emerging in response to the drive to introduce UAM and UAS operations in urban environments. These include stacked layers [6], dynamic 4D tubes, designated zones [7], and urban air corridors. The remainder of this paper is structured as follows: Section 2 introduces the proposed system architecture followed by comprehensive modelling of GNSS performance in Section 2; Section 4 presents a simulation case study applying the developed methodology to a representative UAS operation in an urban environment, along with the relevant results
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