Technical Reports

Precise Point Positioning 

  • NovAtel's Waypoint PPP-AR Performance Analysis - February 2020
    This paper analyzes the static and kinematic results using PPP-AR engine with TerraStar-NRT corrections.
  • Near Real Time Performance Analysis - January 2019
    This paper compares the static and kinematic PPP performance using Waypoint's Near Real Time precise satellite clock and orbit products compared to delayed common correction services. 
  • Base Station Coordinate Determination Using Precise Point Positioning
    This paper demonstrates GrafNav's static Precise Point Positioning (PPP) performance as a function of time using occupations ranging from 30 minutes to 24 hours. The results were generated from 36,000  processing runs using data from 1,000 permanently operating reference stations operating within the USA. These stations use a variety of GNSS equipment. Results show that the PPP method can be used as a base station coordinate check or even for coordinate determination
  • Tightly Coupled Processing of Precise Point Position (PPP) and INS Data
    Presented at ION GNSS 2009, NovAtel announced a major enhancement to Inertial Explorer 8.30 - a tightly coupled GPS/INS module which requires GPS data from only one receiver (i.e. no base station). Previously, tightly coupled processing was supported only in differential processing. Tightly coupled processing allows GPS data to be used even where only two satellites are tracked. This can significantly reduce IMU error growth where satellite drop outs occur due to, for example, sharp turns. Tightly coupled processing has the added advantage of being a simpler workflow in that GPS and IMU data are processed in a single step. Presented in this paper are results from seven flights processed with simulated 25 degree, 45 degree, and 70 degree turns.
  • Airborne Multi-Pass Precise Point Positioning in GrafNav 8.10
    New to GrafNav 8.10 (scheduled for release January 2008) is an improved Precise Point Positioning (PPP) method referred to as Multi-Pass. Multi-Pass PPP improves kinematic PPP results by up to 40% as compared with results from the original PPP processor introduced in GrafNav 7.80. This report presents results from five aerial surveys processed in GrafNav 7.80 and GrafNav 8.10. For each flight the PPP solutions are compared with a high quality differential solution.
  • Airborne Precise Point Positioning (PPP) in GrafNav 7.80 with Comparisons to Canadian Spatial Reference System (CSRS) Solutions
    GrafNav 7.80 is scheduled for release March 15 2007. A major new feature included in this release is a Precise Point Positioning (PPP) processor. Three airborne flights are processed with GrafNav's PPP processor and were also submitted to the CSRS for processing. Both PPP results are compared with a differential truth solution.

Inertial Explorer 

  • Aerial Photogrammetry Test Flight Results
    This paper demonstrates how NovAtel's GPS/INS technology, SPAN (Synchronized Position Attitude Navigation), can be integrated into an aerial photogrammetry application, with the Inertial Explorer® software package, providing post-processing capability
  • Tightly Coupled Processing of Precise Point Position (PPP) and INS Data
    Presented at ION GNSS 2009, NovAtel announced a major enhancement to Inertial Explorer 8.30 - a tightly coupled GPS/INS module which requires GPS data from only one receiver (i.e. no base station). Previously, tightly coupled processing was supported only in differential processing. Tightly coupled processing allows GPS data to be used even where only two satellites are tracked. This can significantly reduce IMU error growth where satellite drop outs occur due to, for example, sharp turns. Tightly coupled processing has the added advantage of being a simpler workflow in that GPS and IMU data are processed in a single step. Presented in this paper are results from seven flights processed with simulated 25 degree, 45 degree, and 70 degree turns.

GrafNav/Net 

  • GrafNav Volcano Monitoring
    NovAtel's Waypoint GrafNav software post-processes data from the crater of Mt. St. Helens.
  • Kinematic Batch Processing Accuracies of One Data Set at Varying Baseline Distances using CORS Stations in GrafNav Version 7.50 (PDF - 209KB)
    This report shows obtainable accuracies for one data set when batch processing using CORS base station data. In each run, three CORS stations are used at average baseline lengths of 130 km, 545 km and 1,270 km. Results are compared with the processing results from three locally established base stations which had an average baseline length of 14 km during the survey. Batch processing results are presented both with Waypoint's older style of batch processing as well as the new style available in version 7.50. The new method of batch processing uses all of the data simultaneously in one Kalman filter.
  • Static Baseline Accuracies as a Function of Baseline Length, Observation Time and the Effect of using the Precise Ephemeris (PDF - 112KB)

    Examined in this report are static baseline accuracies for baseline lengths of 5, 20, 40, 60, 200, 300, 400, 500, 700, 800, 900 and 1000 km. Each baseline is processed with 1, 3, 6, 12 and 24 hours of data with both the broadcast and precise ephemeris in order to establish:
    1. When the precise ephemeris begins to make a measurable difference
    2. What level of accuracy to expect for given baseline lengths and occupation times
    3. How much data should be collected before little or no improvement is seen in the convergence of the solution
  • Tracking the relative motion of four space payloads launched from a sub-orbital NASA rocket (PDF - 157KB)
    This report describes the use of moving baseline software in tracking post-mission, the relative trajectories of 4 Ashtec G12 HDMA GPS receivers attached to payloads jettisoned from a Black Brant XII rocket.

GNSS/IMU Processing 

Scanning LiDAR sensors have become a standard component in most mobile mapping systems, and they provide an impressive level of detail in 3-dimensions. To provide real-world coordinates of the LiDAR point cloud, a GNSS/IMU system is often used for the exterior orientation1 (EO), and post-processing typically delivers optimal EO estimates. For producing the final point cloud, the navigation EO parameters can either be used directly or as an initial approximation for subsequent LiDAR processing, such as SLAM. Therefore, improving the accuracies of the EO values produced by the GNSS/IMU system is highly desirable.