libgnss++ — Modern C++ GNSS/RTK/PPP/CLAS Toolkit

Native non-GUI GNSS stack in modern C++17 with built-in SPP, RTK, PPP, CLAS/MADOCA, RTCM, UBX, and direct QZSS L6 handling.

The point of this repo is simple: ship a usable GNSS toolchain without depending on an external RTKLIB runtime.

If this repo is useful, star it.

Contribution and PR workflow: CONTRIBUTING.md Architecture notes: docs/architecture.md Documentation index: docs/index.md

Performance at a glance

How libgnss++ stacks up against the established open reference implementations, on the same public observations:

Reference stack	Benchmark	Result
RTKLIB demo5	Public moving-body RTK — PPC Tokyo/Nagoya, 6 runs	Matches or beats demo5: Hmed 42–56× tighter and +17 pp positioning rate on the public runs
CLASLIB	QZSS CLAS PPP from raw L6, 1-hour static	Beats CLASLIB: RMS 3D 3.57 mm vs 7.29 mm (−51 %), same fix rate, no CLASLIB runtime needed
MADOCALIB / MALIB	MADOCA-PPP (QZSS L6E orbit/clock/bias + L6D ionosphere)	Natively supported end-to-end (raw L6 decode → PPP-AR); a linked-MADOCALIB oracle bridge ships for parity work. Moving-body accuracy parity is still being validated

The demo5 and CLASLIB numbers are detailed and reproducible below. MADOCALIB and MALIB are open MADOCA-PPP reference stacks (RTKLIB-based); libgnss++ implements the same MADOCA-PPP pipeline natively, and the --madocalib-bridge oracle is provided to benchmark against them directly.

CLAS Performance vs CLASLIB

QZSS CLAS (Centimeter-Level Augmentation Service) PPP from raw L6 binary, 2019-08-27 static dataset (TRM59800.80 antenna), 1 hour (3599 epochs):

Metric	gnssplusplus --claslib-parity	CLASLIB
Matched fixed epochs	3594 / 3599 (99.86%)	3594 / 3599 (99.86%)
RMS 3D (fixed-only)	3.57 mm	7.29 mm
3D bias (mean offset)	1.66 mm	4.84 mm
RMS East	1.15 mm	1.52 mm
RMS North	1.21 mm	0.92 mm
RMS Up	3.15 mm	7.07 mm
Mean E / N / U	-0.72 / +0.93 / +1.17 mm	+0.65 / -0.59 / +4.76 mm
First fix epoch	epoch 6	epoch 6
CLASLIB runtime link	not required	required
Parity depth	17 helpers at 1e-6 m vs CLASLIB oracle	reference

CLASLIB 2D	gnssplusplus 2D

gnssplusplus achieves 51% lower RMS 3D and ~3x tighter 3D bias than upstream CLASLIB on the same 1-hour window while keeping the same fix rate, with no CLASLIB runtime dependency on the default path. The ClasnatParity GoogleTest suite pins 17 core helpers (windupcorr, antmodel, ionmapf, prectrop, corrmeas, satpos_ssr, tidedisp, eph2clk, eph2pos, geodist, satantoff, compensatedisp, trop_grid_data, filter, lambda, tropmodel, stec_grid_data) to 1e-6 m parity against the CLASLIB C source.

Opt-ins:

• -DCLASLIB_PARITY_LINK=ON + --claslib-bridge: delegate to upstream CLASLIB postpos() linked as a static library (oracle mode)
• --legacy-strict-parity: iter13-era non-native strict OSR path (regression reference)

See docs/clas_port_architecture.md for the port design and docs/clas_validated_datasets.md for the validated dataset set.

RTK Performance vs RTKLIB (demo5)

The primary public RTK benchmark is taroz/PPC-Dataset: urban Tokyo/Nagoya vehicle runs with survey-grade receiver observations, reference-station observations, broadcast navigation data, and trajectory truth. The comparison below solves the same public rover/base/nav observations with gnssplusplus and RTKLIB demo5. It is not a proprietary receiver-engine comparison. UrbanNav Tokyo Odaiba is dominated by the explicit --preset odaiba opt-in profile across Fix count, Fix rate, Hmed, Hp95, and Vp95.

On PPC Tokyo and Nagoya, the current gnssplusplus develop branch dominates RTKLIB demo5 on positioned-epoch precision and Fix rate with no Phase 2 opt-in flags. Positioning rate is tracked as a separate first-class metric: the PPC coverage profile keeps valid SPP/float fallback epochs and now exceeds RTKLIB demo5 on Positioning rate for all six public Tokyo/Nagoya runs. UrbanNav Tokyo Odaiba is kept as an independent public urban stress smoke: gnssplusplus wins Fix count, Hp95, and Vp95 there, while the Hmed gap closes to 9 cm when wide-lane AR is explicitly enabled.

All runs below use --mode kinematic --preset low-cost --match-tolerance-s 0.25. The coverage profile additionally uses --no-arfilter plus the default low-speed non-FIX drift guard and SPP height-step guard, the default FLOAT bridge-tail guard, and --ratio 2.4. The kinematic post-filter cascade was removed in PR #36 (single-epoch height-step drop only), so --no-kinematic-post-filter is no longer required for coverage parity with the default profile.

Benchmark Scope

Dataset	Role	Receiver/input basis	Comparison target
PPC Tokyo/Nagoya	Primary public moving-RTK sign-off	Septentrio mosaic-X5 rover RINEX plus Trimble Alloy/NetR9 base RINEX/nav and reference.csv truth	gnssplusplus vs RTKLIB demo5 on the same observations
UrbanNav Tokyo Odaiba	External urban stress smoke	Public Odaiba rover/base/nav and Applanix reference	gnssplusplus vs RTKLIB demo5; not a receiver-engine benchmark

ppc-demo summaries record this under receiver_observation_provenance, including the rover/base receiver and antenna model. receiver_engine_solution_available is intentionally false for PPC because the benchmark target is the open observation solve against reference truth.

The checked-in scorecard is generated from gnss ppc-coverage-matrix output, so it shows Positioning-rate wins first and keeps Fix-rate, PPC official distance-ratio score, and P95 horizontal-error deltas visible in the same view.

PPC Tokyo precision profile (kinematic, low-cost preset, no Phase 2 flags)

This fixed-output table is the precision-oriented view. The coverage table below is the sign-off view for no-solution gaps and fallback-positioned epochs.

Run	gnssplusplus Fix / rate	RTKLIB Fix / rate	Hmed (m)	Vp95 (m)
run1	3572 / 81.26%	2418 / 30.52%	0.037 vs 1.567 (42×)	1.259 vs 36.703 (29×)
run2	4674 / 80.12%	2127 / 27.58%	0.016 vs 0.835 (52×)	0.313 vs 42.624 (136×)
run3	7516 / 86.84%	5778 / 40.55%	0.012 vs 0.666 (56×)	0.137 vs 24.521 (179×)

PPC coverage profile (GNSS-only fallback epochs retained)

Run	gnssplusplus Positioning	RTKLIB Positioning	Delta	gnssplusplus Fix	RTKLIB Fix	PPC official score	RTKLIB official score	Official delta	P95 H delta
Tokyo run1	90.0%	66.3%	+23.7 pp	54.4%	30.5%	34.9%	0.0%	+34.9 pp	+3.39 m
Tokyo run2	95.3%	84.3%	+11.0 pp	64.1%	27.6%	69.0%	16.9%	+52.1 pp	-18.51 m
Tokyo run3	95.7%	93.1%	+2.5 pp	63.0%	40.5%	60.6%	35.6%	+25.0 pp	-0.24 m
Nagoya run1	88.8%	65.8%	+23.0 pp	64.5%	33.8%	49.5%	22.4%	+27.1 pp	-23.78 m
Nagoya run2	85.6%	69.8%	+15.8 pp	51.4%	18.8%	20.9%	11.0%	+9.9 pp	-27.24 m
Nagoya run3	93.8%	67.7%	+26.1 pp	27.1%	13.9%	27.4%	7.6%	+19.7 pp	-5.37 m

Across these six public runs, the coverage profile averages +17.0 pp Positioning-rate lead, +28.1 pp PPC official-score lead, and -11.96 m P95 horizontal-error delta versus RTKLIB demo5.

PPC realtime status profile (deployable Wrong/FIX sign-off)

The current PPC status-correctness profile is --realtime-profile sigma-demote. It keeps the benchmark on the same public Tokyo/Nagoya rover/base/nav observations and uses PPC reference.csv only after the run for scoring and Wrong-FIX labeling. The runtime selector itself is deployable-only: it uses solver diagnostics such as NIS per observation, ratio, residual/jump guards, and emitted status, not reference truth.

On the six public PPC runs, the recommended nis2-ratio4 profile reaches 64.686% weighted PPC official score with 6.188x minimum realtime factor. Its purpose is not to maximize the number of emitted FIX epochs; it minimizes the measured Wrong/FIX rate while staying realtime:

Profile	Weighted official	FIX epochs	Wrong FIX	Wrong/FIX	Min realtime
sigma profile, no status demotion	64.750%	41608	3078	7.398%	6.883x
sigma-demote (--demote-fixed-status-nis-per-obs 2)	64.707%	29705	563	1.895%	7.046x
sigma-demote + ratio status demotion (--demote-fixed-status-max-ratio 4)	64.686%	27395	470	1.716%	6.188x

This is a public-run replay result, not a hidden/private PPC leaderboard submission. The RTKLIB demo5 comparison remains the open-observation baseline shown above; do not describe this as a proprietary receiver-engine comparison. Full rejected candidates and the Wrong-FIX audit are recorded in docs/ppc_realtime_gate_existing_outputs.md.

PPC tuning history and diagnostics

The README keeps the sign-off view above. The long exploratory trail is split into focused notes:

• Benchmarks: PPC coverage matrix, historical selector experiments, IMU/dropout bridge diagnostics, scorecards, and reproduction commands.
• PPC realtime gate outputs: accepted sigma-demote nis2-ratio4 result plus rejected real-time gate candidates.
• PPC smoother sanity report: smoother and oracle/reference-selected checks kept as diagnostics only.

PPC2024's official score is a distance ratio with 3D error <= 50 cm. The published first-place result was 78.7% Public / 85.6% Private in PPC2024 results. The tables above use the same score definition on public open runs, but they are local replays, not official Kaggle submissions or hidden Private split results.

PPC Nagoya (same preset)

Run	Fix delta	rate delta	Hmed delta
run1	+1743	+58.03 pp	9× better
run2	+1735	+64.00 pp	10× better
run3	+154	+50.16 pp	44× better

UrbanNav Tokyo Odaiba (kinematic, low-cost preset)

Config	Fix	Rate	Hmed (m)	Hp95 (m)	Vp95 (m)
RTKLIB demo5	595	7.22%	0.707	27.878	45.212
gnssplusplus default	1268 (+673)	36.98%	1.707	19.585	25.495
gnssplusplus --preset odaiba	735 (+140)	32.81%	0.698	19.976	26.440

PPC Tokyo + Nagoya need no Phase 2 flags. On Odaiba, --preset odaiba is the explicit all-metric demo5-beating profile.

RTKLIB 2D	libgnss++ 2D

Phase 2 opt-in tuning gates

The default RTK pipeline is the benchmark path. Additional gates are default-off unless explicitly enabled for diagnostics or dataset-specific profiles such as Odaiba.

Common entry points:

• Use --realtime-profile sigma-demote for the current PPC deployable Wrong/FIX sign-off.
• Use --preset odaiba for the Odaiba all-metric demo5-beating tradeoff.
• See RTK tuning gates for the full flag table and historical sweep context.

Docs

• Public site: https://rsasaki0109.github.io/gnssplusplus-library/
• Documentation index
• Architecture notes
• Reference analyses
• Contribution workflow

Local docs site:

python3 -m pip install -r requirements-docs.txt
python3 -m mkdocs serve

Docker

Build the runtime image:

docker build -t libgnsspp:latest .

Pull the published image:

docker pull ghcr.io/rsasaki0109/gnssplusplus-library:develop

Run the CLI against a mounted workspace or dataset directory:

docker run --rm -it \
  -v "$PWD:/workspace" \
  libgnsspp:latest \
  solve --rover /workspace/data/rover_kinematic.obs \
  --base /workspace/data/base_kinematic.obs \
  --nav /workspace/data/navigation_kinematic.nav \
  --out /workspace/output/docker_rtk.pos

Serve the local web UI from inside the container:

docker run --rm -it \
  -p 8085:8085 \
  -v "$PWD:/workspace" \
  libgnsspp:latest \
  web --host 0.0.0.0 --port 8085 --root /workspace

The image installs the gnss dispatcher, Python helpers, and libgnsspp Python package, but it does not embed the repo sample datasets. Mount your source tree or your own dataset directory.

Run the web UI with Compose:

docker compose up gnss-web

Override the image if you want a local or tagged build:

LIBGNSSPP_IMAGE=ghcr.io/rsasaki0109/gnssplusplus-library:v0.1.0 docker compose up gnss-web

What You Get

• Native solvers: SPP, RTK, PPP, CLAS-style PPP
• Native protocols: RINEX, RTCM, UBX, direct QZSS L6
• Raw/log tooling: NMEA, NovAtel, SBP, SBF, Trimble, SkyTraq, BINEX
• Product tooling: fetch-products, ionex-info, dcb-info
• Analysis tooling: visibility, visibility-plot, and moving-base-plot for az/el/SNR exports plus moving-base/visibility PNG quick-looks
• Moving-base tooling: moving-base-prepare plus moving-base-signoff for real bag/replay/live validation, including optional commercial receiver side-by-side summaries
• One CLI entrypoint: gnss spp, solve, ppp, visibility, stream, convert, live, rcv
• Local web UI: gnss web for benchmark snapshots, live/moving-base/PPP-product sign-offs, 2D trajectories, visibility views, artifact bundles, receiver status, and artifact links
• Built-in sign-off scripts and checked-in benchmark artifacts
• CMake install/export, Python bindings, and ROS2 playback node

Quick Start

Build

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j

First solutions

python3 apps/gnss.py spp \
  --obs data/rover_static.obs \
  --nav data/navigation_static.nav \
  --out output/spp_solution.pos

python3 apps/gnss.py solve \
  --rover data/short_baseline/TSK200JPN_R_20240010000_01D_30S_MO.rnx \
  --base data/short_baseline/TSKB00JPN_R_20240010000_01D_30S_MO.rnx \
  --nav data/short_baseline/BRDC00IGS_R_20240010000_01D_MN.rnx \
  --mode static \
  --out output/rtk_solution.pos

python3 apps/gnss.py ppp \
  --static \
  --obs data/rover_static.obs \
  --nav data/navigation_static.nav \
  --out output/ppp_solution.pos

python3 apps/gnss.py visibility \
  --obs data/rover_static.obs \
  --nav data/navigation_static.nav \
  --csv output/visibility.csv \
  --summary-json output/visibility_summary.json \
  --max-epochs 60

python3 apps/gnss.py replay \
  --rover-rinex data/rover_kinematic.obs \
  --base-rinex data/base_kinematic.obs \
  --nav-rinex data/navigation_kinematic.nav \
  --mode moving-base \
  --out output/moving_base_replay.pos \
  --max-epochs 20

Inspect receiver logs

python3 apps/gnss.py ubx-info \
  --input logs/session.ubx \
  --decode-observations

python3 apps/gnss.py sbf-info \
  --input logs/session.sbf \
  --decode-pvt \
  --decode-lband \
  --decode-p2pp

Useful commands

Command	Purpose
gnss spp	Batch SPP from rover/nav RINEX
gnss solve	Batch RTK from rover/base/nav RINEX
gnss ppp	Batch PPP from rover RINEX plus nav or precise products
gnss fetch-products	Fetch and cache SP3/CLK/IONEX/DCB products
gnss visibility	Export azimuth/elevation/SNR visibility rows and summary JSON
gnss stream	Inspect and relay RTCM over file, NTRIP, TCP, or serial
gnss ubx-info / gnss sbf-info	Inspect receiver logs
gnss ppc-rtk-signoff	PPC Tokyo/Nagoya RTK sign-off profiles
gnss ppc-coverage-matrix	Full six-run PPC matrix with JSON/Markdown summaries
gnss web	Local browser UI for summary JSON, live/moving-base/PPP-product sign-offs, .pos trajectories, moving-base/visibility plots and histories, receiver status, and artifact/provenance links

See all commands and options:

python3 apps/gnss.py --help

More CLI details are in Interfaces.

Local web UI

python3 apps/gnss.py web \
  --port 8085 \
  --rcv-status output/receiver.status.json

Then open http://127.0.0.1:8085 to inspect Odaiba metrics, live/moving-base/PPP-product sign-offs, 2D trajectories, moving-base and visibility plots, moving-base history, PPC summaries, receiver status, and linked artifact bundles in a browser. The PPP products table links directly to fetched products, MALIB .pos, comparison CSV/PNG artifacts, and dataset provenance.

Long-running dashboard commands can also read TOML config files. See configs/web.example.toml, configs/live_signoff.example.toml, configs/moving_base_signoff.example.toml, configs/ppc_rtk_signoff.example.toml, and configs/ppp_products_ppc.example.toml, then pass --config-toml <file>.

Container form:

docker run --rm -it -p 8085:8085 -v "$PWD:/workspace" \
  libgnsspp:latest web --host 0.0.0.0 --port 8085 --root /workspace

Real moving-base sign-off

gnss solve, gnss replay, and gnss live accept --mode moving-base. For real moving-base datasets, use gnss moving-base-signoff with a reference CSV carrying per-epoch base/rover ECEF coordinates. Example TOML files live under configs/; detailed commands are in Quick Start.

Product-driven PPP

Use gnss fetch-products, gnss ppp-static-signoff, gnss ppp-kinematic-signoff, and gnss ppp-products-signoff for SP3/CLK/IONEX / DCB driven runs. See Quick Start and configs/ppp_products_ppc.example.toml for full examples.

Reproduce Benchmarks

PPC-Dataset can be verified directly from an extracted dataset tree. The current sign-off command is:

python3 apps/gnss.py ppc-rtk-signoff \
  --dataset-root /datasets/PPC-Dataset \
  --city tokyo \
  --realtime-profile sigma-demote \
  --rtklib-bin /path/to/rnx2rtkp \
  --summary-json output/ppc_tokyo_run1_rtk_signoff.json

Full replay, RTKLIB comparison, and historical diagnostic commands are in PPC reproduction commands. Dataset source: taroz/PPC-Dataset.

Other benchmark artifacts and checked sign-offs are documented in Benchmarks and Validation.

Install And Package

cmake --install build --prefix /opt/libgnsspp

Installed layout includes:

• bin/gnss, native binaries, Python wrappers, sign-off scripts, asset generators, CMake/pkg-config exports, and the libgnsspp Python package.

Examples:

# pkg-config
pkg-config --cflags --libs libgnsspp

# source the installed dispatcher
/opt/libgnsspp/bin/gnss --help

Python And ROS2

Python bindings expose:

• RINEX/header inspection, .pos statistics, coordinate helpers, and file-based SPP, PPP, and RTK solve helpers.

ROS2 support includes a playback node that publishes .pos files as:

• sensor_msgs/NavSatFix, geometry_msgs/PoseStamped, nav_msgs/Path, and solution status / satellite-count telemetry.

Tests And Dogfooding

Run the full non-GUI regression set:

ctest --test-dir build --output-on-failure

Important checks already covered in-tree:

• solver/unit tests, live realtime/error-handling regression, benchmark/image generation tests, installed-prefix packaging smoke tests, Python bindings, and ROS2 node smoke tests.

Data

Bundled samples live under data/; generated benchmark outputs live under output/ and docs/.

Scope

This repo is intentionally focused on a strong non-GUI GNSS stack.

It already covers native RTK, PPP, CLAS, RTCM, UBX, QZSS L6, installed CLI tooling, benchmarks, sign-off scripts, and README asset generation.

It is still not marketed as a perfect RTKLIB drop-in replacement. The remaining gaps are about scope breadth, not the core non-GUI workflow shipped here.

License

MIT License. See LICENSE.