Fundamentals of Multi-Target Tracking (MTT) and Multi-Sensor Data Fusion (MSDF) Training by Tonex
Fundamentals of Multi-Target Tracking & Multi-Sensor Data Fusion training by Tonex is designed to provide an in-depth understanding of these two fields and their applications. It covers topics such as introduction to MTT, probability theory and Bayes’ rule, detection and tracking, data association, filtering and smoothing, and performance evaluation. It is suitable for professionals in defense, surveillance, robotics, and autonomous systems, and equips them with the skills and knowledge needed to develop effective tracking and fusion systems.
Multi-target tracking (MTT) is related to vision and signal processing that involves tracking multiple moving objects over time in a given scene. Participants will lean about MTT applications in various domains including defense, surveillance, autonomous driving, robotics, and object recognition. MTT aims to estimate the trajectories and states of multiple targets based on sensor measurements obtained from one or more sensors.
Multi-sensor data fusion systems combine information from multiple sensors to provide a more comprehensive and accurate understanding of the environment. Here are a few examples of multi-sensor data fusion systems in different application domains
Multi-sensor data fusion (MSDF) is a related field that deals with combining information from multiple sensors to improve the accuracy and reliability of target tracking systems. In many real-world scenarios, a single sensor may not provide sufficient information to accurately track targets due to occlusions, sensor limitations, or noisy measurements. By fusing data from multiple sensors, it becomes possible to overcome these limitations and obtain a more accurate and comprehensive understanding of the scene.
Learning Objectives:
After completing this course, the students will be able to:
- Define what Multi-target tracking (MTT) is
- Explain value proposition of data fusion
- Define the key features of data fusion and sensor integration
- List the functional requirements of multisensory fusion
- List the four pillars of data fusion and Multi-Sensor Data Fusion
- Identify the motivating factors behind Multi-sensor data fusion (MSDF)
- List the principal components of MTT and MSDF
- Apply cutting-edge tools, methods and techniques for multi-sensor integration
- Explain application of multisensory fusion applied to identification, target tracking, net-centric, TDL, situational and threat assessment
Who Should Attend
- Project managers, product managers, software and systems engineers, EW and TDL operators, scientists, R&D, military and law enforcement.
Course Topics
Introduction to Data and Sensor Fusion
- Data Fusion: who, why and when
- Data Fusion Application
- Classification of Data Fusion Techniques
- Data Fusion Modeling Technique
- Multisensor Fusion
- Overview of Perception, Localization, and Object Manipulation Capabilities
- Intelligence Analysis and Data Fusion
- Principles of Sensor Fusion
- Integration of Multiple Sensors
- Cameras, Inertial Measurement Units (IMUs)
- Tactile Sensors,
- Principles of Real-time Sensor Fusion
- Multi-Sensor Data Fusion
- Application Case Studies: Robotics, Autonomous Unmanned Systems, Fusing LIDAR and Radar Data
- Tactical Data Links (TDLs)
- Signals Intelligence (SIGINT)
- Electronic Warfare (EW)
- Advanced Radar Signals Collection and Analysis (ARSCA)
Fundamentals of Multi-target Tracking (MTT)
- Introduction to Multi-target tracking (MTT)
- MTT and applications in various fields
- Problem of simultaneously tracking multiple objects or targets in a scene over time
- How to estimate the positions, velocities, and other relevant attributes of the targets
Challenges with Multi-target Tracking (MTT)
- Data Association
- Determining which measurements originate from the same target
- Target Birth and Death
- Target Interactions
- Motion Model and Dynamics
- MTT algorithms to address challenges
- Kalman Filters
- Multiple Hypothesis Tracking (MHT)
- Particle Filters
- Graph-based Methods
- Deep Learning Approaches
Overview of Multi-sensor Data Fusion (MSDF)
- Sensor Data Acquisition
- Collecting data from multiple sensors
- Data Preprocessing
- Preprocessing methods to remove noise
- Data Association
- Fusion Algorithms
- Track Management
- Track Evaluation
Integration of Sensor Data
- Methods to Enhance the Accuracy, Reliability, and Completeness of the Gathered Information
- Data Alignment and Calibration
- Sensor Fusion Methods
- Averaging/Weighted Averaging
- Rule-Based Fusion
- Kalman Filters
- Particle Filtering
- Bayesian Networks
- Contextual Information
- Quality Assessment and Data Validation
- Time Synchronization
- Examples and Case Studies
Multi-Sensor Data Fusion (MSDF) Architecture, Design, and Implementation
- Sensor Types
- Sensor Common Characteristics
- Fusion System Design
- Architectures for Multi-Sensor Data Fusion
- Joint Directorate of Laboratories (JDL) Architecture
- Observe-Orient-Decide-Act (OODA) Loop
- Foundational Technologies
- Theory of Probability and Statistics
- Statistical Distributions
- Conjugate Distributions for Bayesian Inference
- Monte Carlo Simulation Techniques
- Bayesian Belief Networks
- Intelligent Agents
- Software Tools
- iDAS and Text
- Bayesian Belief Network Engine
- Argumentation Engine
- Kalman Filters
- Lidar and Radar Fusion with Kalman Filters
- Hyperspectral and Multispectral Sensing
- Semantic Methods
- Decision Fusion Techniques
- Semantic Information Fusion
Architecture of a Multi-sensor Data Fusion System
- General Framework for Multi-sensor Data Fusion Systems
- Interconnected Components and Modules
- Sensor Interface
- Data Fusion Engine
- Data Registration and Calibration
- Sensor Management
- Context Awareness
- Quality Assessment and Validation
- Knowledge Base
- User Interface
- Communication Infrastructure
Sensor Data Identification and Classification
- Preprocessing Sensor Data
- Feature Extraction
- Temporal Features
- Frequency Domain Features
- Statistical vs. Time-Series Sensor Data
- Classification Algorithms
- Supervised Learning
- Unsupervised Learning
- Deep Learning
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Ensemble Methods
- Model Training and Evaluation
- Post-processing and Decision Making
Overview of Multi-Sensor Data Fusion (MSDF) Algorithms
- Data Fusion Algorithms
- Signal level algorithms
- Weighted averages
- The Kalman filter (KF)
- Particle filtering (PF)
- k-Nearest Neighbor (k-NN)
- Decision Trees (DT)
- Support Vector Machines (SVM)
- Artificial Neural Network (ANN)
- Gaussian mixture model (GMM)
- k-Means
- Decision level algorithms
- Bayesian inference
- Fuzzy logic
- Applications of data fusion for health monitoring
- Activity recognition
- Fall detection and prediction
- Gait and ambulatory monitoring
- Analysis of Multiple Sensors and JDL Data Fusion Model
- Decision on Levels of Fusion
Case Studies: Examples of Multi-Sensor Data Fusion Systems
- Autonomous Unmanned Vehicles
- Unmanned Air Vehicles (UAVs)
- Surveillance Systems
- Robotics
- Defense Systems