Multi-Target Tracking & Multi-Sensor Data Fusion Training
|Commitment||3 days, 7-8 hours a day.|
|How To Pass||Pass all graded assignments to complete the course.|
|User Ratings||Average User Rating 4.8 See what learners said|
|Delivery Options||Instructor-Led Onsite, Online, and Classroom Live|
Multi-Target Tracking & Multi-Sensor Data Fusion Training Course – Hands-on
Multi-Target Tracking & Multi-Sensor Data Fusion Training Course – Customize it
- We can adapt this training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this training course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the training around the mix of technologies of interest to you (including technologies other than those included in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the training course in manner understandable to lay audiences.
Multi-Target Tracking & Multi-Sensor Data Fusion Training Course – Audience/Target Group
The target audience for this training course:
Multi-Target Tracking & Multi-Sensor Data Fusion Training Course – Objectives:
Upon completing this training 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, Fuzzy Logic.
Multi-Target Tracking & Multi-Sensor Data Fusion Training – Course Content
Introduction. Basic concepts & definitions. Target motion models, measurement models, coordinate systems.
The Kalman Filter. State estimation – least squares and Kalman filtering.
Other Linear Filters. Constant-gain and table look-up filters. Comparative performance.
Non-Linear Filters. When are they necessary? Extended Kalman Filter, Unscented Filter, Particle Filters.
Angle-Only Tracking. EKF, pseudo-linear, modified polar coordinates.
Maneuvering Targets: Adaptive Techniques. Noise models, maneuver detection, adaptive processes.
Maneuvering Targets: Multiple Model Approaches. Non-switching and switching models. Interacting Multiple Model. IMM design. Examples.
Single Target Correlation & Association. Correlation processing. Nearest neighbor, assignment algorithms.
Track Initiation, Confirmation & Deletion. M/N and sequential tests for confirmation. Track deletion criteria.
Using Measured Range Rate (Doppler). Implementation of constant-gain, Kalman and EKF using measured range rate.
Multi-target Correlation & Association. Extended nearest neighbor, optimal & sub-optimal assignment.
Probabilistic Data Association. Examples and implementation issues.
Multiple Hypothesis Approaches. Examples. Hypothesis merging and pruning.
Coordinate Conversions. Conversions between local systems and from local to global. Compensation for sensor motion.
Multiple Sensors. JDL Data Fusion Model. Levels of fusion.
Data Fusion Architectures. Fusion architectures. Report-to-Track and Track-to-Track. Associated Measurement Reports
Fusion of Data From Multiple Radars. Comparison of Report-to-Track and Track-to-Track processes. Colocated and non-colocated sensors.
Fusion of Data From Multiple Angle-Only Sensors. Correlation. Triangulation techniques for non-colocated.
Fusion of Data From Radar and Angle-Only Sensor. Correlation techniques. Differences between collocated and non-colocated sensors.
Sensor Alignment. Types of alignment problems. Impact of biases. Techniques.
Fusion of Target Type and Attribute Data. Bayesian, Dempster-Shafer, Fuzzy Logic. Impact of attribute data on correlation.
Performance Metrics. Quantizing system performance. Measures of track accuracy, continuity, initiation time, etc