Tracker Component Library (Python)
A Python port of the U.S. Naval Research Laboratory's Tracker Component Library, a comprehensive collection of algorithms for target tracking, estimation, coordinate systems, and related mathematical functions.
1,450+ functions | 217 modules | 8,500+ tests | 90% coverage
Overview
The Tracker Component Library provides building blocks for developing target tracking algorithms, including:
- Coordinate Systems: Conversions between Cartesian, spherical, geodetic, and other coordinate systems
- Dynamic Models: State transition matrices for constant velocity, coordinated turn, and other motion models
- Estimation Algorithms: Kalman filters (KF, EKF, UKF, CKF, CEKF, H-infinity), particle filters (bootstrap, RBPF), smoothers, and batch estimation
- Assignment Algorithms: Hungarian algorithm, auction algorithms, 3D/ND assignment, k-best assignments
- Data Association: Global Nearest Neighbor, JPDA, MHT for multi-target tracking
- Mathematical Functions: Special functions, statistics, numerical integration (Gaussian quadrature, cubature rules including the full 10-variant seventh-order algorithm surface, Genz-Keister and Smolyak sparse grids), and more
- Astronomical Code: SGP4/SDP4 propagation, TLE parsing, special orbits (parabolic/hyperbolic), ephemerides, relativistic corrections
- Reference Frames: GCRF, ITRF, TEME, TOD, MOD with full transformation chains
- Navigation: Geodetic calculations, INS mechanization, GNSS utilities, INS/GNSS integration
- Geophysical Models: Gravity (WGS84, EGM96/2008), magnetism (WMM, IGRF, EMM, WMMHR2025), atmosphere (US Standard 1976/ISA, simplified thermosphere, ionosphere, humidity/dew point, astronomical and radar refraction with ray tracing, speed of sound), tides, terrain (GEBCO 2025, Earth2014)
- Signal Processing: Digital filters, matched filtering, CFAR detection, transforms (FFT, STFT, wavelets)
- GPU Acceleration: CuPy (NVIDIA CUDA) and MLX (Apple Silicon) backends for batch Kalman filtering and particle filters
- Results I/O: CSV/Parquet measurement readers, polars DataFrame accessors for track histories, msgspec (MessagePack/JSON) serialization, ASDF archival export, HDF5 compression (measured 4.73x), and AIS/NMEA transponder decoding
- Typed Configs & Sessions:
msgspec.Structconfigs (IMMConfig,GaussianSumConfig,RBPFConfig,SingleTargetConfig,MultiTargetConfig) accepted via a keyword-onlyconfig=on the matching constructor, plus full state snapshot/resume (pytcl.io.save_session/load_session) for six tracker and filter classes, bit-exact resume for the four deterministic ones
Installation
Basic Installation
pip install nrl-tracker
With Optional Dependencies
# For astronomy features (ephemerides, celestial mechanics)
pip install nrl-tracker[astronomy]
# For geodesy features (coordinate transforms, map projections)
pip install nrl-tracker[geodesy]
# For terrain data (GEBCO, Earth2014 via NetCDF)
pip install nrl-tracker[terrain]
# For visualization
pip install nrl-tracker[visualization]
# For signal processing (wavelets)
pip install nrl-tracker[signal]
# For polars DataFrame accessors on track histories and metrics
pip install nrl-tracker[dataframe]
# For AIS/NMEA transponder decoding
pip install nrl-tracker[ais]
# For ASDF archival export/import of tracks and states
pip install nrl-tracker[asdf]
# For GPU acceleration (NVIDIA CUDA)
pip install nrl-tracker[gpu]
# For GPU acceleration (Apple Silicon M1/M2/M3)
pip install nrl-tracker[gpu-apple]
# Install every user-facing extra except gpu (dev tooling is no longer a
# published extra — contributors use `uv sync`)
pip install nrl-tracker[all]
From Source
git clone https://github.com/nedonatelli/TCL.git
cd TCL
pip install -e .
Quick Start
Coordinate Conversions
import numpy as np
from pytcl.coordinate_systems import cart2sphere, sphere2cart
# Convert Cartesian to spherical coordinates
cart_point = np.array([1.0, 1.0, 1.0])
r, az, el = cart2sphere(cart_point, system_type="az-el") # tracking convention
print(
f"Range: {r:.3f}, Azimuth: {np.degrees(az):.1f}°, Elevation: {np.degrees(el):.1f}°"
)
# Convert back
cart_recovered = sphere2cart(r, az, el, system_type="az-el")
Kalman Filter
import numpy as np
from pytcl.dynamic_estimation.kalman import kf_predict, kf_update
from pytcl.dynamic_models import f_constant_velocity, q_constant_velocity
# Constant-velocity model, 2D state [x, vx, y, vy]
dt = 0.1
F = f_constant_velocity(dt, num_dims=2)
Q = q_constant_velocity(dt, sigma_a=1.0, num_dims=2)
H = np.array([[1.0, 0, 0, 0], [0, 0, 1.0, 0]]) # measure position only
R = np.eye(2) * 10.0
x = np.zeros(4)
P = np.eye(4) * 100.0
measurement = np.array([1.2, -0.7])
pred = kf_predict(x, P, F, Q)
upd = kf_update(pred.x, pred.P, measurement, H, R)
print(upd.x) # updated state; upd.P, upd.y, upd.S, upd.K, upd.likelihood
Assignment Problem
import numpy as np
from pytcl.assignment_algorithms import hungarian
cost_matrix = np.array(
[
[10.0, 5.0, 13.0],
[3.0, 15.0, 8.0],
[7.0, 9.0, 12.0],
]
)
row_ind, col_ind, total_cost = hungarian(cost_matrix)
print(f"rows {row_ind} -> columns {col_ind}, total cost {total_cost}") # cost 20.0
GPU Acceleration
The library supports GPU acceleration for batch processing of multiple tracks:
import numpy as np
from pytcl.gpu import is_gpu_available, get_backend, to_gpu, to_cpu
# 100 tracks with a 4-dimensional state each
states = np.random.randn(100, 4)
covariances = np.tile(np.eye(4), (100, 1, 1))
F = np.eye(4)
Q = np.eye(4) * 0.01
# Check GPU availability (auto-detects CUDA or Apple Silicon)
if is_gpu_available():
print(f"GPU available, using {get_backend()} backend")
# Transfer data to GPU
x_gpu = to_gpu(states) # (n_tracks, state_dim)
P_gpu = to_gpu(covariances) # (n_tracks, state_dim, state_dim)
# Use batch Kalman filter operations
from pytcl.gpu import batch_kf_predict
x_pred, P_pred = batch_kf_predict(x_gpu, P_gpu, F, Q)
# Transfer results back to CPU
x_pred_cpu = to_cpu(x_pred)
Supported backends:
- NVIDIA CUDA: Via CuPy (
pip install nrl-tracker[gpu]) — float64 - Apple Silicon: Via MLX (
pip install nrl-tracker[gpu-apple]) — float32
The backend is automatically selected based on your platform. Batch Kalman, EKF, UKF, particle-filter, and matrix operations all run on either backend.
Measured on Apple Silicon (MLX), batch linear Kalman predict+update versus a per-track CPU loop, end-to-end including host-device transfers and result materialization, after warm-up: 1.6x at 100 tracks, 13x at 1,000, 40x at 20,000 (August 2026).
Precision note: MLX computes in float32 (it raises on float64 GPU operations), so results match the CPU implementations to ~1e-7 relative rather than machine epsilon. The unscented filter is especially sensitive: its default
alpha=1e-3yields sigma-point weights of order 1e6, which float32 cannot resolve — usealpha >= 0.1on MLX (the library warns).
Module Structure
pytcl/
├── core/ # Foundation utilities and constants
├── mathematical_functions/ # Basic math, statistics, special functions
├── coordinate_systems/ # Coordinate conversions and transforms
├── dynamic_models/ # State transition and process noise models
├── dynamic_estimation/ # Kalman filters, particle filters
├── static_estimation/ # ML, least squares estimation
├── assignment_algorithms/ # 2D and multi-dimensional assignment
├── clustering/ # Mixture reduction, clustering
├── performance_evaluation/ # OSPA, track metrics
├── astronomical/ # Ephemerides, time systems
├── navigation/ # Geodetic, INS, GNSS
├── atmosphere/ # Standard atmosphere, thermosphere, ionosphere, humidity, refraction
├── gravity/ # Gravity models
├── magnetism/ # Magnetic field models
├── terrain/ # Terrain elevation models
├── containers/ # Spatial indexes, track/measurement containers
├── trackers/ # Multi-target trackers (GNN, JPDA, MHT)
├── io/ # SQL/HDF5 storage, CSV/Parquet/DataFrame/ASDF/msgspec I/O
├── diagnostics/ # Opt-in loguru logging, ASCII-safe rich progress/tables
├── transponders/ # AIS/NMEA decoding (maritime)
├── gpu/ # GPU acceleration (CuPy/MLX)
└── plotting/ # Covariance ellipses, tracks, metrics plots
Examples & Tutorials
The library includes 42 runnable code examples demonstrating all major features:
Examples (32 files in /examples/)
Comprehensive demonstrations of library functionality:
- Tracking & Estimation: Kalman filters, particle filters, smoothers
- Assignment: Hungarian algorithm, k-best assignments, 3D assignment
- Coordinates: Frame conversions, transformations, geodetic calculations
- Dynamics: State models, motion models, dynamic systems
- Filtering: Uncertainty visualization, multi-target tracking
- Astronomy: Ephemerides, orbital mechanics, relativistic corrections
- Navigation: INS/GNSS integration, geophysical modeling
- Signal Processing: Detection, filtering, transforms
- Terrain & Atmosphere: Elevation models, atmospheric properties
Status: ✅ All 32 examples run in CI on every push
Tutorials (10 modules in /docs/tutorials/)
Interactive learning modules with visualizations:
- Assignment algorithms and 3D assignment problems
- Atmospheric and geophysical models
- Dynamical systems and reference frames
- Filtering and smoothing techniques
- Sensor fusion and advanced filtering
- Special functions and mathematical tools
Status: ✅ All 10 tutorials validated and passing (100% execution success)
Documentation
- API Reference
- User Guides
- Examples - 32 validated example scripts
- Tutorials - 10 interactive tutorial modules
Comparison with Original MATLAB Library
The core tracking workflow is fully ported and validated against independent references; the complete function-level accounting is in docs/matlab_parity_inventory.rst, and the explicit name mappings plus calling-convention differences are in docs/matlab_migration_map.rst. A taste:
| MATLAB | Python |
|---|---|
Cart2Sphere(cartPoints) |
cart2sphere(cart_points) |
discKalPred(x, P, F, Q) |
kf_predict(x, P, F, Q) |
KalmanUpdate(x, P, z, R, H) |
kf_update(x, P, z, H, R) — note H/R order |
FPolyKal(T, xDim, 1) |
f_constant_velocity(dt, dim) |
Key differences:
- Function names use
snake_case; multiple return values become NamedTuples - States are 1-D arrays and batches are
(N, dim)row-major (coordinate conversions also accept MATLAB-style column layouts) - 0-based indexing, explicit
unassigned_rowsinstead of 0-sentinels
Testing
# Run all tests
uv run pytest
# Run with coverage
uv run pytest --cov=pytcl
# Run only fast tests
uv run pytest -m "not slow"
# Run tests validated against MATLAB
uv run pytest -m matlab_validated
A bare local pytest run picks up tests/property/'s Hypothesis-generated
property tests too, at the 500-example dev profile (CI runs a
derandomized 100-example profile instead); see
tests/property/README.md for what's covered
and the profile policy.
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
Development Setup
git clone https://github.com/nedonatelli/TCL.git
cd TCL
uv sync
uv run prek install
See CONTRIBUTING.md for the full setup (installing uv,
uv run vs. activating .venv, the gpu-apple extra for Apple Silicon).
Running Quality Checks
# Format code
uv run ruff format .
# Lint (includes import sorting)
uv run ruff check .
# Type check (gate)
uv run ty check pytcl
# Run all checks
uv run prek run --all-files
Citation
If you use this library in your research, please cite the original MATLAB library:
@article{crouse2017tracker,
title={The Tracker Component Library: Free Routines for Rapid Prototyping},
author={Crouse, David F.},
journal={IEEE Aerospace and Electronic Systems Magazine},
volume={32},
number={5},
pages={18--27},
year={2017},
publisher={IEEE}
}
License
This project is in the public domain, following the original MATLAB library's license. See LICENSE for details.
Acknowledgments
- Original MATLAB library by David F. Crouse at the U.S. Naval Research Laboratory
- This port follows the Federal Source Code Policy (OMB M-16-21)
Related Projects
- FilterPy - Kalman filtering library
- Stone Soup - Framework for tracking algorithms
- Astropy - Astronomy library for Python
1 maintainer
Metadata
1 maintainer
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