Classification of GNSS SNR data for different environments and satellite orbital information
Summary (3 min read)
Introduction
- In order to guarantee an acceptable user experience, GNSS receiver’s location accuracy needs to be proven cautiously on the grounds of interference, sensitivity, and use cases in various surroundings and in different atmospheric conditions.
- GNSS laboratory measurement environments are also marketed by companies, e.g. ETS-Lindgren [4] and Keysight Technologies [5].
- As the result of measurements, characteristics of polarization based reflections, position error, coverage efficiency (i.e. mean number of tracked satellites), and the impact of satellite elevation angle on received Signalto-Noise Ratio (SNR) for a typical multipath environment are investigated in [7] to create a dynamic channel model for the laboratory environment.
- Third, the data classification approach and results are presented in section IV.
II. MEASUREMENT SETUP
- The measurement system consists of two GNSS receivers, capable of recording signals that are transmitted by various constellations of satellites (GPS, GLONASS, and Beidou).
- In addition to video recording continuous images with fish-eye lens were taken to track the changing environment.
- The camera was held head-high pointing towards zenith.
- During the test, both receivers collected GPS satellite data at a rate of 1Hz including the Coordinated Universal Time (UTC), received SNR, number of satellites tracked and used in position fix, and the orbital information of each visible satellite (i.e. satellite elevation, and azimuth).
- A detailed description of the measurement setup along with antenna orientations is presented earlier [6].
III. ENVIRONMENTS
- The mentioned GPS data were collected for three different environments; consisting of, university campus, suburban residential, and urban downtown areas.
- Starting position of all three measured routes was nearly an open-sky area to obtain complete GPS ephemeris and good position fix.
- Measurements were made in Oulu, Finland during the months, when the trees were with and without leaves, respectively.
- Measurements for university campus area represent the case in which the trees were with leaves and measurements for suburban residential, and urban downtown areas represent the case when deciduous trees were almost without leaves.
- Fig. 1 shows the aerial view of three environments.
B. Suburban Residential Area
- The measured route consist of a typical residential area occupied primarily by private residences.
- The measured route included trees surrounding the roads, two storey houses with wooden structures, narrow streets, and a three storey office building located nearly 30 meters away from the measured route.
- The test vehicle took about 10 min to finish the trajectory and about 720 data points are recorded by GNSS receivers.
C. Urban Downtown Area
- A heavily dense area with many businesses, recreations, narrow streets, underpass, and tall buildings close together.
- The subway underneath the roads and railway lines is roughly 80 meters long.
- The test vehicle took about 13 min to finish the trajectory and about 850 data points are recorded by GNSS receivers.
IV. DATA CLASSIFICATION
- Based on the measurements from various environments shown in Fig. 1, the different parts of each environment and the orbital information of satellites are used as criterion for data classification.
- Each measured environment is further divided into four different environments: (1) open area; (2) forest area; (3) single building blockage; and (4) street canyon.
- Classification of satellites is based on their elevation, however in case of single building blockage azimuth information of satellites is also taken into account.
- An elevation mask of 10◦ is used for the forest area due to insufficient data and visibility of satellites below 10◦, and the tall building structures of street canyon prevented visibility of satellites for all elevations below 30◦.
A. Notations
- Elevation and azimuth angles of satellite are represented by θS and φS , respectively and, similarly, the elevation tilt angles of directional receiver antennas are presented with θRHCP and θLHCP .
- Environments consisting of university campus, and suburban residential areas are presented with UCA, and SRA, respectively.
- Merged SNR data from different sub environments are denoted by Md.
- In single building case, Blocked side of building is represented by B, whereas, the other (open) side is presented with O. Finally, the mean and standard deviation of SNR are represented by µSNR, and σSNR.
B. Approach
- Fig. 2 represents the flow chart of the data classification approach.
- After the route selection and measurement setup, the selected route is measured with GNSS receivers in parallel with a video camera to track the continuously changing environment.
- Then, satellites are classified in nine different groups based on their elevation and the data form all the satellites that fall in same group are merged together.
- The same process is repeated for each environment and data from each sub environment are merged together.
- Finally, a table is created containing the information of mean and standard deviation of SNR for different environments and elevation ranges.
C. Results
- In order to get the full coverage for both upper and lower hemisphere, measurements were performed at different antenna orientations (i.e. θRHCP and θLHCP ).
- It should be noted that there was no area in measured environments open enough to be considered as an open area.
- Bar charts of the µSNR and σSNR from the forest area (as described under Data Classification) for RHCP antenna are shown in Figs. 4 and 5.
- Therefore, the rest of results are based on merged data from each sub environment instead of data from individual environments.
- At higher θS , RH has LOS situation and lower levels of LH pointing towards weaker reflections from ground below.
V. CONCLUSION
- In this article the authors have presented a data classification method based on the measurements from various environments.
- Different parts of each environment and the orbital information of satellites are used as criterion for data classification.
- First, the method involves taking fish eye images of measured routes and analyzing the images to divide each environment into four potential sub environments: (1) open area; (2) forest area; (3) single building blockage; and (4) street canyon.
- The results obtained with significant amount of data from three different environments showed good correlation of SNR between same sub environments.
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Frequently Asked Questions (10)
Q2. What are the future works in this paper?
The main drawback of this approach is the tedious and precise work required to classify data into sub environments if the area to be covered is extended: the process is only automated to limited extend. However, in the future more automatic pattern recognition of the still images could offer a faster way for SNR evaluation in different environments. This information will be used to regenerate a generalized set of SNR parameters in the laboratory environment for 3D GNSS channel model.
Q3. Why is an elevation mask used for the forest area?
An elevation mask of 10◦ is used for the forest area due to insufficient data and visibility of satellites below 10◦, and the tall building structures of street canyon prevented visibility of satellites for all elevations below 30◦.
Q4. How many buildings are located near the measured route?
The measured route included trees surrounding the roads, two storey houses with wooden structures, narrow streets, and a three storey office building located nearly 30 meters away from the measured route.
Q5. Why were no satellites received during the measurement campaign?
during the measurement campaign no satellites between the elevation angles of 81◦– 90◦ were received because of the northern location of the measurement routes.
Q6. What is the future of the method?
Future efforts will include taking second round of measurements with 2nd generation measurement system equipped withdual polarized GPS antennas [9], and 360 degree images of environments.
Q7. What is the metric of the GNSS data?
Elevation and azimuth angles of satellite are represented by θS and φS , respectively and, similarly, the elevation tilt angles of directional receiver antennas are presented with θRHCP and θLHCP .
Q8. What is the main drawback of this approach?
The main drawback of this approach is the tedious and precise work required to classify data into sub environments if the area to be covered is extended: the process is only automated to limited extend.
Q9. What is the purpose of this article?
This information will be used to regenerate a generalized set of SNR parameters in the laboratory environment for 3D GNSS channel model.
Q10. What are the different environments that were collected?
The mentioned GPS data were collected for three different environments; consisting of, university campus, suburban residential, and urban downtown areas.