Multi-Target Tracking and Multi-Sensor Data Fusion Training Workshop

Digital Signal Processing System Design Training is intended for engineers and scientists concerned with the design and performance analysis of signal processing applications. The Digital Signal Processing System Design Training course will provide the fundamentals required to develop optimum signal processing flows based on processor throughput resource requirements analysis. Emphasis will be placed on practical approaches based on lessons learned, thoroughly developed using procedures and computer tools that show each step required in the design and analysis. MATLAB code will be used to demonstrate concepts and show actual tools available for performing the design and analysis.

WHAT’S INCLUDED?

• 3 days of Multi-Target Tracking and Multi-Sensor Data Fusion Training Workshop with an expert instructor
• Multi-Target Tracking and Multi-Sensor Data Fusion Training Electronic Course Guide
• Certificate of Completion
• 100% Satisfaction Guarantee

RESOURCES

• Multi-Target Tracking and Multi-Sensor Data Fusion Training – https://www.wiley.com/
• Multi-Target Tracking and Multi-Sensor Data Fusion – https://www.packtpub.com/
• Multi-Target Tracking and Multi-Sensor Data Fusion Training – https://store.logicaloperations.com/
• Multi-Target Tracking and Multi-Sensor Data Fusion Training – https://us.artechhouse.com/
• Multi-Target Tracking and Multi-Sensor Data Fusion Training – https://www.amazon.com/

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• Radar Systems Design & Engineering Training
• Radar Systems Fundamentals Training
• Radar Technology Training Fundamentals

AUDIENCE/TARGET GROUP:

The target audience for this Multi-Target Tracking and Multi-Sensor Data Fusion course:

• All

Course Prerequisite:

The knowledge and skills that a learner must have before attending this Multi-Target Tracking and Multi-Sensor Data Fusion course are:

• Basic technical knowledge

Multi-Target Tracking and Multi-Sensor Data Fusion Training Workshop

Course Objectives:

Upon completing this Multi-Target Tracking and Multi-Sensor Data Fusion course, learners will be able to meet these objectives:

• State Estimation Techniques – Kalman Filter, constant-gain filters.
• Non-linear filtering – When is it needed? Extended Kalman Filter.
• Techniques for angle-only tracking.
• Tracking algorithms, their advantages, and limitations, including:
• Nearest Neighbor
• Probabilistic Data Association
• Multiple Hypothesis Tracking
• Interactive Multiple Model (IMM)
• How to handle maneuvering targets.
• Track initiation – recursive and batch approaches.
• Architectures for sensor fusion.
• Sensor alignment – Why do we need it and how do we do it?
• Attribute Fusion, including Bayesian methods, Dempster-Shafer, and Fuzzy Logic.

Course Syllabus:

1. Introduction. Basic concepts & definitions. Target motion models, measurement models, and coordinate systems.
2. The Kalman Filter. State estimation – least squares and Kalman filtering.
3. Other Linear Filters. Constant-gain and table look-up filters. Comparative performance.
4. Non-Linear Filters. When are they necessary? Extended Kalman Filter, Unscented Filter, Particle Filters.
5. Angle-Only Tracking. EKF, pseudo-linear, modified polar coordinates. Multi-Target Tracking and Multi-Sensor Data Fusion Training
6. Maneuvering Targets: Adaptive Techniques. Noise models, maneuver detection, adaptive processes.
7. Maneuvering Targets: Multiple Model Approaches. Non-switching and switching models. Interacting Multiple Model. IMM design. Examples.
8. Single Target Correlation & Association. Correlation processing. Nearest neighbor, assignment algorithms.
9. Track Initiation, Confirmation & Deletion. M/N and sequential tests for confirmation. Track deletion criteria.
10. Using Measured Range Rate (Doppler). Implementation of constant-gain, Kalman, and EKF using measured range rate.
11. Multi-target Correlation & Association. Extended nearest neighbor, optimal & sub-optimal assignment.
12. Probabilistic Data Association. Examples and implementation issues.
13. Multiple Hypothesis Approaches. Examples. Hypothesis merging and pruning.
14. Coordinate Conversions. Conversions between local systems and from local to global. Compensation for sensor motion.
15. Multiple Sensors. JDL Data Fusion Model. Levels of fusion. Multi-Target Tracking and Multi-Sensor Data Fusion Training
16. Data Fusion Architectures. Fusion architectures. Report-to-Track and Track-to-Track. Associated Measurement Reports
17. Fusion of Data From Multiple Radars. Comparison of Report-to-Track and Track-to-Track processes. Colocated and non-colocated sensors.
18. Fusion of Data From Multiple Angle-Only Sensors. Correlation. Triangulation techniques for non-colocated.
19. Fusion of Data From Radar and Angle-Only Sensor. Correlation techniques. Differences between collocated and non-colocated sensors.
20. Sensor Alignment. Types of alignment problems. Impact of biases. Techniques.
21. Fusion of Target Type and Attribute Data. Bayesian, Dempster-Shafer, Fuzzy Logic. Impact of attribute data on correlation.
22. Performance Metrics. Quantizing system performance. Measures of track accuracy, continuity, initiation time, etc

Multi-Target Tracking and Multi-Sensor Data Fusion Training Workshop Recap, Q/A, and Evaluations