Issue link: https://insidegnss.epubxp.com/i/960969

60 Inside GNSS M A R C H / A P R I L 2 0 1 8 www.insidegnss.com the PIPE soware GNSS signal generator, or samples recorded from an RFCS's output. PIPE accommodates INLU's diverse requirements by a mod- ular approach of signal chain simulations: Certain soware elements of the chain can be replaced by hardware compo- nents. Additionally, interfaces to front-end sampling and replay devices are available. e following subsections describe the creation of the user's trajectory, the simulation of a satellite constellation as well as the processing of the response stemming from a propagation channel model. Trajectory Generation e first step in the scenario generation process is the creation of the user's trajectory. is trajectory serves as input for the generation of GNSS observations, as well as further sensor data like odometers, inertial sensors, baro-altimeters, and magnetometers. e challenge hereby is to produce consistent dynamics data. When the accelerations and angular rates pro- vided by the trajectory generator are used for the generation of inertial sensor data assuming an ideal inertial measurement unit, the output of an ideally initialized strapdown algorithm must match the original trajectory exactly, even aer longer simulation times. is is achieved in the trajectory generator first by producing desired positions and attitudes over time. en, a strapdown algorithm together with a flight control-like algorithm is applied. From the small offsets between desired positions and attitudes and the strapdown state, accelerations and angular rates are generated, which are provided to the strapdown algorithm in the next epoch and drive these offsets to zero. Constellation Simulation From the trajectory generation, the GNSS antenna positions and velocities also can be calculated at equidistant points in time. For each of these points, the constellation simulation needs to calculate the corresponding satellite positions and velocities based on RINEX files. The trajectory time scale defines the times of reception of the satellite signals. In order to calculate the satellite positions and velocities, the times of transmission at the satellite need to be determined. This is achieved by approximating the satellite orbit in a time interval of T=0.1 seconds by a straight line, which is accurate to the sub-millimeter level. Denoting the time of reception with t 0 , the satellite position p S at the time of transmission t 0 –t can be expressed as S S It must now be considered that the ECEF frame is rotat- ing. e ephemeris describes the satellite position in the ECEF frame at the point in time for which a satellite position is cal- culated. In order to express the satellite position at t 0 –T in the ECEF frame that is valid at t 0 , the rotation of the ECEF frame in the time interval T needs to be considered: WORKING PAPERS In the following, p S (t 0 –T) always refers to the satellite posi- tion at t 0 –T, expressed in the ECEF frame at t 0 . In order to deter- mine the time of flight of the satellite signal t, the equation relating the range between satellite position at time of trans- mission, p S (t 0 –t), and antenna position at time of reception, p A (t 0 ), is used: Here, c denotes the speed of light. Inserting the linear approximation of the satellite orbit and squaring the equation leads to is is a quadratic equation that can be solved for the time of flight, t. Consequently, the satellite position at time of trans- mission, p S (t 0 –T), in coordinates of the ECEF frame at time of reception, t 0 , is obtained. e carrier phase, code phase, and Doppler measurements obtained at t 0 can then be generated using appropriate error models. Propagation Channel Models e reception conditions for land-based users are impacted to a large extent by multipath propagation caused by objects and structures in the receiver's vicinity. Moreover, in urban areas, signals are oen blocked and diffracted by buildings and trees. State-of-the-art multipath propagation models reproduce these effects and generate channel impulse responses (CIR) at a given rate dependent on the receiver's dynamics. CIRs contain components that reflect the complex amplitude and delay of line-of-sight as well as multipath components. PIPE's interface to a channel response generated by a multipath propagation model is given by the Channel Data Exchange format (CDX) (see CDX - Channel Data Exchange Library in Additional Resources). e PIPE GNSS signal gen- erator can read the channel impulse response for every time step from a CDX file and generate the corresponding multipath components with their respective delays and complex ampli- tudes in the output signal. is allows for the usage of various channel models for INLU. Within the project, the model rec- ommended by ITU-R P.681 for urban environments was used. Additionally, this model was extended for railway applications during a research activity (I. Gulie et alia). Figure 2 shows the visualization of a common railway scenery. e INLU project also requires the generation of RF signals by hardware constellation simulators using the mentioned channel models. As these models produce more multipath components than a common hardware constellation simulator can re-pro- duce, a component count reduction step is required. In INLU, a method based on F. M. Schubert et alia (2014b) is applied.

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