Sensor Fusion and Tracking Toolbox


Sensor Fusion and Tracking Toolbox

Design, simulate, and test multisensor tracking and positioning systems

Reference Applications

Simulate and track inertial navigation, autonomous, and surveillance systems.

Product Highlights

Scenario and Sensor Simulation

Define multiplatform scenarios, then assign motion profiles and attach sensor models to each platform. Simulate these scenarios and dynamically visualize the platform trajectories, sensor coverages, and object detections.

Estimation Filters

Use various estimation filters, like Kalman filters, multimodel filters, and particle filters, to estimate object states. These filters have been optimized for specific scenarios, such as linear or nonlinear motion models, or incomplete observability.

Multi-Object Tracking

Use multi-object multi-sensor trackers that integrate filters, data association, and track management. Choose from a variety of trackers that include single-hypothesis, multiple-hypothesis, joint probabilistic data association, random finite sets, or grid-based tracking.

Multi-Sensor Fusion

Explore centralized or decentralized multi-object tracking architectures and evaluate design trade-offs between track-to-track fusion, central-level tracking, or hybrid tracking architectures for various tracking applications.

Visualization, Evaluation, and Tuning

Analyze and evaluate the performance of tracking systems against ground truth using various tracking metrics. Visualize ground truth, sensor coverages, detections, and tracks on a map or in a MATLAB figure. 

Deployment and Hardware Connectivity

Deploy algorithms to hardware targets by automatically generating C/C++ code from fusion and tracking algorithms. Deploy generated code to low-cost hardware with limited memory allocation and strictly single precision processing.

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