An open-source GNSS Direct Position Estimation Plug-in Module for Two-Step Positioning SDRs, integrated into SoftGNSS v3.0 by Borre et al. (2007)
Intelligent Positioning and Navigation Laboratory (IPNL) / PNT Signal Processing Group
The Hong Kong Polytechnic University
More information can be found in the file Sergio - ION GNSS+2024 PPT.pdf as well as the following manuscript
Multipath Mitigation Technology-integrated GNSS Direct Position Estimation Plug-in Module
DPE_module is a Direct Position Estimation (DPE) plug-in module that can be integrated into existing two-step positioning (2SP) MATLAB SDRs. 2SP information, namely tracking code phase, signal transmission time, receiver local time, satellite position from Least Squares, satellite clock bias, and Least Squares position solution, are used as input for the plug-in module.
Programmed in a user-friendly language, MATLAB, DPE_module is aimed for better understanding and familiarity of a practical implementation of DPE. In this repository, DPE_module is integrated into the SoftGNSS MATLAB GPS L1 C/A 2SP SDR from Borre et al. (2007), which is based on Scalar Tracking Loop (STL). But integration of DPE_module is not restricted to an STL-based receiver and can be integrated with other 2SP architectures, such as Vector Tracking Loop (VTL). Additionally, the GNSS constellation is not restricted to GPS L1 C/A, but is also integrable with SDRs working with other BPSK-modulated GNSS signals.
DPE_module would use the grid-based method, by which of establishing a set of candidate position, velocity, and time (PVT) and obtaining the correlations from each satellite before finally non-coherently summing them up to obtain the candidate position with the highest signal correlation, which would be the PVT estimate of DPE. Position as well as receiver clock bias estimate from 2SP are also used as initialization for the grid of candidate position. An illustration is provided below (Vicenzo et al. 2024)
Instead of iteratively computing the signal correlations per every candidate position, our DPE plug-in module pre-calculates the correlations per every pre-determined chip spacing or code phase. This implementation was previously used by previous research on collective detection, which is another name for DPE, to save computational time (Axelrad et al., 2009; Cheong et al., 2011).
Parts of SoftGNSS v3.0 have been adapted to allow the DPE_module to be integrated into it.
Though DPE has been proven to be robust against MP, previous research has proved that its superior performance against two-step positioning (2SP) typically falters in deep urban environments where multipath (MP) and non-line-of-sight (NLOS) are the majority of signals received (Vicenzo et al. 2023). This does not necessarily mean that the performance of DPE is worse than 2SP, but rather that its performance is depreciated to a large degree with increasing errors from MP and NLOS the same way 2SP does.
To solve this issue for DPE, we also introduce a Multipath Mitigation Technology (MMT)-integrated DPE, nicknamed MMT-DPE. MMT was introduced as an efficient maximum likelihood estimator for accurate estimation of the code delays and carrier phase of the LOS and reflected signal (Weill 2002). An MMT-DPE would incorporate the cost function of MMT into DPE. In essence, both DPE and MMT are the same maximum likelihood estimators that share similar cost functions, with the difference that in MMT, a reflected path is assumed to be received in addition to the LOS path. Replacing DPE cost function with that of MMT would virtually mean that the MP component is incorporated into DPE signal model.
Parts of SoftGNSS v3.0 have been adapted to employ the MMT-DPE algorithm.
The software presented in this repository is a SoftGNSS v3.0 that has been integrated with DPE_Module, and using it follows the same steps as running a regular SoftGNSS v3.0. Modifications were made to SoftGNSS v3.0 to also run on 16-bit samples (int16 or short data types).
When running with 16-bit data samples, use int16 in settings.dataType (instead of short). On the other hand, use schar in settings.dataType (instead of int8) when running with 8-bit data samples.
Further information on using the software can be found in the original SoftGNSS v3.0 readme readme - SoftGNSS.txt and ppt GPS_L1_CA_SDR.pdf.
All DPE_module parameters can be edited from initSettings.m, which are listed below.
-
settings.candPVT_spacing= Grid spacing for the latitude-longitude-height estimation (Default = 1 meter) -
settings.DPE_latlong_span= Span of latitude and longitude search space (Default = ±30 meters) -
settings.DPE_height_span= Span of height search space (Default = ±50 meters) -
settings.DPE_clkBias_span= Span of clock bias search space (Default = ±20 meters) -
settings.DPE_nonCohInt= DPE non-coherent integration time (Default = 1) to improve the performance of DPE as the satellite correlations would be more filtered -
settings.DPE_CohInt= DPE coherent integration time (Default = 20 ms) to improve the performance of DPE against MP -
settings.DPE_plotCorrelogram= Output the correlograms, plotted at the estimated DPE height -
settings.gt_llh= Ground truth coordinates in geodetic coordinates to output the positioning errors from both DPE and 2SP -
settings.chipspacing_dpe_precalc= Chip spacings between the pre-calculated correlations (Default = chips/sample) -
settings.MMT= 1 (Activate MMT-DPE and MMT-2SP) or 0 (Perform conventional DPE and 2SP) -
settings.MMT_const= Relative amplitude constraint for MMT (Default = 0.8)
MMT_Sergio.m and DPE_module were developed with MATLAB2022a and it is recommended to run the program with the same version or later. Running the program with other MATLAB versions has yet to be tested. No additional MATLAB toolbox is required i.e., it can run with just the basic MATLAB package and no additional toolboxes.
Dependencies of the underlying SoftGNSS v3.0 can be found in its readme file readme - SoftGNSS.txt
Sample GPS L1 C/A intermediate frequency (IF) datasets can be accessed through the following link
All the datasets were collected in a static urban scenario at The Hong Kong Polytechnic University main campus. The collection site taken with Google Earth is shown below (marked H).
The datasets have the following configuration.
FRONT-END EQUIPMENT : LABSAT 3 Wideband
SAMPLING FREQUENCY : 58 MHz (settings.samplingFreq = 58e6)
INTERMEDIATE FREQUENCY : 1580e6-1575.42e6 Hz (settings.IF = 1580e6-1575.42e6)
DATA TYPE : 'schar' or 'int8' (settings.dataType = 'schar')
FILE TYPE : IQ (settings.fileType = 2)
FRONT-END BANDWIDTH : 56 MHz
GROUND TRUTH (in llh) : [22.30397720 114.17889380 10.770] (settings.gt_llh = [22.30397720 114.17889380 10.770])
FILE LENGTH : 240,000 ms (settings.msToProcess = 240000)
For datasets used in the manuscript Multipath Mitigation Technology-integrated GNSS Direct Position Estimation Plug-in Module, please refer to the folder MMT Manuscript Dataset at Sample IF datasets.
MMT_Sergio and DPE_module were written by Sergio Vicenzo, inspired by the multicorrelator-based DPE (Corr-DPE) written by HaoSheng Xu and Dr. Bing Xu, which was previously introduced in (Vicenzo et al. 2023).
Sergio Vicenzo is currently a PhD candidate at the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University. He received first-class honours in Bachelor of Engineering in Aviation Engineering from the same university in 2022. He is supervised by Assistant Professor Bing Xu, and co-supervised by Associate Professor Li-Ta Hsu. His research interests include GNSS urban navigation and positioning with direct position estimation.
Email: seergio.vicenzo@connect.polyu.hk
I, Sergio Vicenzo, hereby do not claim ownership nor any responsibility for the SoftGNSS v3.0 software by Prof. Kai Borre and Prof. Dennis M. Akos and the llh2xyz.m script by Prof. Todd Walter. All changes made to the SoftGNSS v3.0 software have been labelled appropriately and its GNU license has been kept intact in each code file, in accordance to GNU General Public License. No changes were made to the llh2xyz.m script and its copyright statement is kept intact.
No copyright infringement is intended from any of the MATLAB scripts provided in this GitHub repository.
Axelrad P, Donna J, Mitchell M (2009) Enhancing GNSS acquisition by combining signals from multiple channels and satellites. In: Proc. ION GNSS 2009, Institute of Navigation, Savannah, Georgia, USA, September 22 – 25, 3117-3128
Borre K, Akos DM, Bertelsen N, Rinder P, Jensen SH (2007) A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach. Birkhäuser, Boston, Massachusetts.
Cheong JW, Wu J, Dempster AG, Rizos C (2011) Efficient Implementation of Collective Detection. In: Proc. IGNSS Symposium 2011, International Global Navigation Satellite Systems Society, Sydney, New South Wales, Australia, November 15-17.
Vicenzo S, Xu B, Dey A, Hsu L-T (2023) Experimental Investigation of GNSS Direct Position Estimation in Densely Urban Area. In: In: Proc. ION GNSS+ 2023, Institute of Navigation, Denver, Colorado, USA, September 19 – 23, 2906-2919.
Vicenzo S, Xu B, Xu H, Hsu L-T (2024) GNSS direct position estimation-inspired positioning with pseudorange correlogram for urban navigation. GPS Solutions 28(2). https://doi.org/10.1007/s10291-024-01627-5
Weill LR (2002) Multipath Mitigation using Modernized GPS Signals: How Good Can it Get?. In: Proc. ION GPS 2002, Institute of Navigation, Portland, Oregon, USA, September 24 - 27, 493 - 505.
This repository is still under development. I apologise if some information is incomplete or if you encounter any problems...
If you have any questions, drop an email at seergio.vicenzo@connect.polyu.hk!
Last Updated: 13 January 2025