An open-source parallelized direct position estimation-based GPS receiver
Stanford Navigation and Autonomous Vehicles Laboratory
Matthew Peretic and Grace X. Gao, Design of a Parallelized Direct Position Estimation-Based GNSS Receiver, Navigation: Journal of the Institute of Navigation, vol. 68, no. 1, pp. 21-39, Dec 2020. doi: 10.1002/navi.402. [paper]
- Ubuntu 16
- CUDA 9.0.252 (obtained from Jetpack 3.3.1 -- developer.nvidia.com)
- Eigen 3.3.5 !!! with modification to work with CUDA !!!
- Run get_Eigen_Jetson.sh to download Eigen and apply the patch to work with CUDA.
- This script will make one edit to Eigen/Core -- view the script to see what is changed.
- UHD >= 3.10.2 (obtained from Ettus Research)
- Requires Boost -- can be installed with "sudo apt-get install libboost-all-dev"
- Requires Mako
- Requires Boost -- can be installed with "sudo apt-get install libboost-all-dev"
- Nsight Eclipse IDE
- Ubuntu 16
- CUDA 9.0.252 (obtained from Jetpack 3.3.1 -- developer.nvidia.com)
- Eigen 3.3.5 !!! with modification to work with CUDA !!!
- Run get_Eigen_Jetson.sh to download Eigen and apply the patch to work with CUDA.
- This script will make one edit to Eigen/Core -- view the script to see what is changed.
- UHD >= 3.10.2 (obtained from Ettus Research)
- Requires Boost -- can be installed with "sudo apt-get install libboost-all-dev"
- Requires Mako
- Requires Boost -- can be installed with "sudo apt-get install libboost-all-dev"
The NVIDIA Jetson TX2 uses the AARCH64 architecture -- different than a conventional personal computer
- If compiling on the host, Nsight must cross-compile for AARCH64
- Consider configuring Nsight to compile on the device instead
- Run nsight_config.sh
- In Nsight,
- New > Makefile Project with Existing Code
- Existing Code Location: the outermost cudarecv folder
- Toolchain: Cross GCC
- Once the project is created, right-click the project in the Project Explorer > Properties
- Build > Builder Settings > Build location > ${workspace_loc:/cudarecv}/build
- Build > Builder Settings > Makefile generation > "Generate Makefiles Automatically" SHOULD NOT BE CHECKED
- Build > Target Systems > Select remote connection... > Choose and configure your ethernet-connected Jetson TX2
- New > Makefile Project with Existing Code
Three files are required to run the DPE receiver:
- Dataset
- Configured for Ettus Research USRP sampling at 2.5MHz (digitally downconverted from the GPS L1 band to 0Hz) with I and Q samples of 16 bits each. 20ms of samples are read-in and processed together.
- The sampling frequency and window size are specified in dpeflow and dpinit.
- The data size of samples is hardcoded into sampleblock.
- Initialization file
- Recommend obtaining by running scalar tracking in PyGNSS for >10s.
- If you can't get a lock on the data in PyGNSS scalar tracking (eg, particularly bad signal environment), you can conceivably make your own following the template handoff csv provided.
- Provide the non-clock corrected receiver time estimate: rxTime
- Provide the initial state estimate: X_ECEF
- Provide the location in the dataset where you want to begin reading the dataset: bytes_read
- Provide the list of satellites you want to track: prn_list
- Provide the channel parameters by differencing the true receiver state and the satellites' states (see discussion at end of README): rc, ri, fc, fi,
- Provide the TOW value, the code periods elapsed since the start of the run when the TOW arrived, and the code periods elapsed when beginning reading the dataset: TOW, cp_timestamp, cp
- File settings can be found in dpeflow.
- RINEX file
- Designed for Version 2.10 NAVIGATION DATA
- Recommend downloading nistDDD0.YYn filetype from CDDIS
- For year 20YY and day of the year DDD, go to folder 20YY > DDD > YYn > ctrl+F "nist"
- Unzip the download
- File settings can be found in dpeflow.
- (Optional) DPE grid
- A position grid can be read-in (instead of generated within the code) through a csv file of the ENU-delta-t offsets (see rngrid3.csv for example).
- File settings can be found in dpeflow.
- Run the project. A prompt 'gnss$' will appear in the console.
- Type 'newflow dpe' and hit enter
- Type 'loadflow 0' and hit enter
- Type 'startflow 0' and hit enter
Move the demofiles folder to your Ubuntu Desktop and run CUDARecv following the "Processing a Dataset" section above.
- static_opensky is a 45-second GPS dataset simulated for July 4, 2018 at 19:00 UTC Time. The satellite PRNs simulated are 2, 3, 6, 12, 17, 19, 24, and 28. The simulated receiver is stationary and located in Urbana-Champaign, IL, USA.
- handoff_params provides the channel initialization data from scalar tracking in PyGNSS.
- nist1860 is the CDDIS navigation data for the simulated dataset, providing the GPS ephemeris.
- rngrid3 is a custom position-time-domain manifold grid for the BCM module. If there are errors with opening these files, provide their absolute paths in dpeflow.cpp.
- Specify the details of your dataset in PyGNSS's setting.py.
- Run PyGNSS's 1_Data_reduct_scalar.py.
- Use the "handoff_params.csv" file generated.
- The DPE flow is intended to run on minutes of data at most -- things like TOW and ephemeris do not update, and no tracking is performed to update the channel parameters.
- The DataLogger and SampleBlock modules have timeouts of 1.5s which will crash the flow when exceeded. If your hardware does not complete one iteration in this time, extend the timeouts in those two files.
- Error catching has not been fully implemented.
- Configuration for the DPE flow is done in main.cu and dpeflow.cpp.
- The "Flow-Module" paradigm is detailed in Matthew Peretic's MS Thesis.
- When creating a new Flow:
- Use the functions (or create new ones) defined in flow.cu.
- Add the file name to the CMakeLists in dsp.
- All Inputs, Outputs, and Parameters to constituent modules should be connected in your new flow file.
- Add your flow to flowmgr.h:
- Add a #include to your new flow's header file.
- Add your flow using the RegFlow function.
- "push back" your flow in "_regis" in flowmgr.cpp.
- Create a class declaration in dsp.h.
- Include your flow's header in main.cu.
- When creating a new Module:
- Use the functions (or create new ones) defined in module.cpp.
- Add the file name to the CMakeLists in modules.
- The syntax for console commands can be found in cmdFlow.h.
- Module parameters can be changed through the console after 'loadflow' and before 'startflow' by the command 'setparam flow module param value'.
- flow: flow number, ex 0
- module: module name, ex SampleBlock
- param: parameter name, ex SamplingFrequency
- value: parameter value, ex 2500000
- The guhd folder contains some starter code that may be useful for USRP streaming.
- rc, ri, fc, fi can be back-calculated by differencing the receiver's true state and the satellites' true states.
- These parameters are the receiver's track of the transmissions sent by each satellite
- The better these estimates, the better the replica created by the DPE receiver, and the more accurate the correlation scores
- Consider for your work whether you want to study solutions to the manifold (poor initial state, good initial channel parameters) or study how to generate the replica (poor initial channel parameters).
- The authors hold that the latter question is more challenging, but is the bigger roadblock to broader use of DPE.
- Acquiring good estimates of channel parameters is a tracking problem, and may be addressed with tracking loops like conventional GNSS receivers use. However, this mitigates some of the benefits of using DPE.
- For a primer and the authors' views on this problem, a survey is presented in this paper by Peretic and Gao
An open-source sequential GPS receiver with scalar tracking (conventional two-steps) and DPE (one-step) localization algorithms.
Stanford Navigation and Autonomous Vehicles Laboratory
E. Wycoff, Y. Ng, and G. X. Gao, "Python GNSS Receiver: An Object-Oriented Software Platform Suitable for Multiple Receivers," GPS World Magazine, February 2015. Online
- Python 2.7
- Numpy 1.15.1
- Run one of the numerically-indexed .py files in pygnss
- 0_Data_reduction.py performs scalar tracking initialization then hands off and runs DPE.
- 1_Data_reduct_scalar.py performs scalar tracking only and can print DPE handoff parameters to a file.
- 2_Generate_ephemerides.py acquires and stores ephemerides from the dataset being processed.
- 3_Data_reduct_dp.py performs DPE only, loading in external channel parameters for initialization.
- Results are saved in folders 'post-simulator' and 'pre-simulator', which are created on-run.
- Set parameters such as sampling frequency, etc. accordingly in setting.py
- Change the flow of the run itself in the .py files.
Grace Gao Research Group
- Lead PI: Dr. Grace X. Gao
- Ramya Bhamidipati
- Shubhendra Chauhan
- Arthur Hsi-Ping Chu
- Enyu Luo
- Yuting Ng
- Matthew Peretic
- ...and contributions through many helpful discussions with other members of the research group!
Copyright (c) 2020 Stanford Navigation and Autonomous Systems Laboratory
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