Sensor Fusion and Object Tracking with MATLAB (New)

Sensor Fusion and Object Tracking with MATLAB

Learn how to make an estimation of where the object will be at the next time step based on some assumed physical motion model of the object using MATLAB

Course
Highlights

An autonomous system primarily consists of four main components: perception, localization, planning and control. Sensor Fusion and Tracking toolboxTM focuses on perception, which starts with a fused position estimation of object from previous time step, then make an estimation of where the object will be at the next time step based on some assumed physical motion model of the object. Some of the common problems that are tackled in sensor fusion and tracking systems such as “How can I design trackers and localization algorithm?”.

Attend our 1-day course that provides hands-on experience with developing and testing localization and tracking algorithms. Examples and exercises demonstrate the use of appropriate MATLAB® and Sensor Fusion and Tracking Toolbox™ functionality. Topics include:

Localization for orientation and position
Scene generation and sensor detection import

Filters and motion models
Data association
Multi-object trackers

Who Should
Attend

The course is intended for engineers, scientists, and researchers who wants to create multi-object trackers to fuse information from multiple sensors using Sensor Fusion and Tracking Toolbox.

Course
Prerequisites

MATLAB Fundamentals or equivalent experience using MATLAB; basic knowledge of tracking concepts

Course
Benefits

Upon the completion of the course, the participants will be able to:

Develop and test algorithms for sensor fusion related to estimating pose, orientation, and position
Generate scenarios and detections that can fee multi-object trackers

Select and tune filters and motion models based on tracking scenarios
Integrate data association algorithms that map to scenarios
Create multi-object trackers to fuse information from multiple sensors

Partners

TechSource Systems is MathWorks Authorised Reseller and Training Partner

Upcoming Program

Please keep me posted on the next schedule
Please contact me to arrange customized/ in-house training

Course Outline

Day 1 of 1

Localization for Orientation and Position

Objective: Fuse IMU and GPS sensor data to estimate position and orientation.

Model measurements from accelerometers, gyroscopes, magnetometers, and GPS
Fuse sensor data to estimate the pose in terms of position, velocity, and orientation

Visualize the pose estimation and plot platforms and trajectories

Scene Generation and Detection Import

Objective: Import and process detections or generate scenarios used in multi-object trackers.

Preprocess and package collected sensor detections
Create a tracking scenario with multiple sensors and platforms

Define waypoint or kinematic trajectories
Customize sensor parameters
Generate detections used in sensor fusion algorithms

Filters and Motion Models

Objective: Select and tune filters and motion models based on tracking requirements.

Evaluate filters against scenario requirements
Compare and contrast different motion models

Configure an Interacting Multiple Model (IMM) filter to track different maneuvers

Data Association

Objective: Determine the appropriate data association method for different tracking situations.

Select from among Global Nearest Neighbor (GNN), Joint Probabilistic Data Association (JPDA), Track-Oriented Multiple Hypothesis (TOMHT), and other data association methods

Determine how multiple detections are assigned to different tracks

Multi-Object Tracker

Objective: Create multi-object trackers to fuse information from multiple sensors such as vision, radar, and lidar.

Configure trackers and parameters
Perform track association and management

Visualize the tracked objects

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