Price: $3,999.00

Length: 3 Days
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Sensor and Data Fusion Training Bootcamp

Data fusion combines data from multiple sources to improve the potential values and interpretation performances of the source data, and to produce a high-quality visible representation of the data.

Fusion techniques are useful for a variety of applications, ranging from object detection, recognition, identification and classification, to object tracking, change detection, decision making, etc.

It has been successfully applied in the space and earth observation domains, computer vision, medical image analysis and defense security.

Remote sensing data fusion, as one of the most commonly used techniques, aims to integrate the information acquired with different spatial and spectral resolutions from sensors mounted on satellites, aircraft and ground platforms to produce fused data that contains more detailed information than each of the sources.

For military applications, the intelligence extracted from data fusion makes it easier for analysts to identify potential adversaries and their targets. By organizing and collecting large volumes of collected data, relevant patterns can be recognized using data fusion.

The goal behind this technology is to increase the effectiveness of military objectives by giving a more complete, integrated view of situations to enable a quicker response while eliminating errors caused by individual failure.

This is crucial because the U.S. military relies upon diverse sensing sources in the battlefield. As a result of the vast resources providing soldiers with information, the military incorporates probability algorithms into sensor fusion systems that effectively process the sensors’ information.

Sensor and Data Fusion Training Bootcamp Course by Tonex

Sensor and Data Fusion Training Bootcamp covers technologies, tools and methods to automatically manage multi sensor data filtering, aggregation, extraction and fusing data useful to intelligence analysts and war fighters.

Learn about application of artificial neural network technology to data fusion for target recognition, airborne target recognition, activity-based intelligence, C4ISR, Electronic Warfare (EW), radar and EO-IR thermal imaging sensors, missile defense, cyber warfare, air, space and maritime surveillance, net-centric warfare, Activity-based Intelligence, effects-based operations process control, proactive maintenance and industrial automation.

Data fusion is a data analysis technique that combines and correlates data about a single subject from different sources, to be able to derive additional insights and intelligence from that data.

The intelligence extracted from data fusion makes it easier for analysts to identify potential adversaries and their targets. By organizing and collecting large volumes of collected data, relevant patterns can be recognized using data fusion.

The artificial neural network based fusion architectures are discussed along with multi-source and multi-sensor data fusion, alternative learning algorithms, parallel distributed processing, real-time automatic target recognition decisions based on performance, and large volumes of structured, unstructured data from disparate sources and low vs. high-level fusion.

Learning Objectives:

After completing this course, the student will be able to:

  • Define what data fusion approach is
  • Identify the motivating factors behind data fusion
  • 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
  • Understand what data fusion and its applications are
  • List the principal components of data fusion systems
  • Define Extracting, Transforming and Loading (ETL) operations
  • Practice using the data fusion
  • Select the right data fusion systems for your mission and application
  • Assemble and manage information gathering and integration from a variety of sources
  • 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 Agenda

Introduction to Data Fusion

  • Basic Concepts
  • Data Fusion: who, why and when
  • Data Fusion Application
  • Data Fusion Modeling Technique
  • Multisensor Fusion
  • Simple example illustrating the data fusion principle
  • Data Base A
  • Data Base B
  • Data unique to A and B
  • Common Characteristics
  • Data Alignment and Association
  • Linking via Common Characteristics
  • Fused Database
  • Data from A and B
  • The Fusion Process
  • Enhanced targeting
  • Linking variables
  • The key to a reliable data fusion
  • Data Fusion Validation
  • Key Elements of the Validation Process Split-sample Validation
  • Data Fusion Architectures
  • Report-to-Track
  • Track-to-Track
  • Associated Measurement Reports
  • Fusion of Data From Multiple Radars
  • Fusion of Data From Radar and Angle-Only Sensor

Intelligence Analysis and Data Fusion

  • Collate, Evaluate and Analyze Multiple Types of Information
  • Data Integrating and Interpreting
  • Information into usable Intelligence
  • Manipulating Query Expressions
  • Describing Data Sources
  • String Matching
  • Schema Matching and Mapping
  • General Schema Manipulation Operators
  • Data Matching
  • Query Processing
  • Data Warehousing and Caching
  • Taxonomy, Ontologies and Knowledge Representation
  • Incorporating Uncertainty into Data Integration
  • Perception and Sensing
  • Sensor and Data Fusion
  • Dynamic World Modeling
  • General Framework for Data Fusion
  • Principles for Integrating Multi-Sensor Information
  • Techniques for Fusion of Numerical Properties
  • Prediction
  • Discrete State Transition Equations
  • Matching Observation to Prediction
  • The Mahalanobis Distance
  • The Kalman Filter Update Equations
  • Fusion of Symbolic Properties
  • Principles for Symbolic Fusion

Principles of Sensor Fusion

  • Sensor Fusion Overview
  • Fusion Applications
  • Terminology for Fusion Systems
  • Sensor Fusion vs. Data Fusion
  • Information Fusion
  • Multi-sensor Data Fusion
  • Multi-sensor Integration
  • ​Track objects in real time with Sensor Fusion
  • Errors in Raw Data
  • Multisensor Fusion Architecture
  • Architectural Taxonomy
  • Fusion Methods
  • Bayesian Networks
  • Probabilistic Grids
  • The Kalman Filter
  • Markov chain Monte Carlo
  • Alternatives to Probability

Real-time Sensor Fusion

  • Technical Strategic Intelligence Data
  • Robotics
  • Autonomous Cars
  • Fusing Lidar and Radar Data
  • Multi-Sensor Data Fusion
  • Tactical Data Link (TDL)
  • Signals Intelligence (SIGINT)
  • Electronic Warfare (EW)
  • Advanced Radar Signals Collection and Analysis (ARSCA)
  • Hyperspectral and Multispectral Sensing
  • Fundamentals of Synthetic Aperture Radar Signal Processing
  • Electro-Optical and Infrared EO/IR Systems
  • Autonomous Unmanned Systems
  • Battlefield Acoustics Signal Processing

Multi-Sensor Data Fusion

  • Target Tracking and Identification
  • Situation and Threat Assessment
  • State of the art
  • Test and Evaluation issues and methods
  • Cognitive Error and Bias
  • Advanced Analysis Techniques
  • Threat Analysis
  • Fusing information and Building Intelligence

Multi-level Data Fusion Modeling

  • Planning and Tools
  • Tasking
  • Real-time Monitoring
  • Secondary Exploitation
  • Level 1 Fusion
  • Object Refinement
  • Fusing multiple heterogeneous data sources
  • Higher Level Fusion dealt with Levels 2-4
  • big conceptual jump from Level 1 to Level 2
  • Situation Refinement
  • Threat Refinement
  • Process Refinement
  • Sensors and Platforms (manned and unmanned)
  • Effects options
  • Networks and Terminals
  • Information Exchange Requirements (IERs)
  • Encryption and Data Integrity

 Multi-Sensor Data Fusion 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 aText
  • 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

Classification of Data Fusion Techniques

  • Classification Based on the Relations between the Data Sources
  • Classification Based on the Abstraction Levels
  • JDL Data Fusion Classification
  • The JDL data fusion framework
  • Classification Based on the Type of Architecture
  • Data Association Techniques
  • Nearest Neighbors and -Means
  • Probabilistic Data Association
  • Joint probabilistic data association (JPDA)
  • Multiple Hypothesis Test
  • Distributed Joint Probabilistic Data Association
  • Distributed Multiple Hypothesis Test
  • Maximum Likelihood and Maximum Posterior
  • The Kalman Filter
  • Decision Fusion Methods
  • The Bayesian Methods
  • The Dempster-Shafer Inference
  • Abductive Reasoning

Data Fusion and Activity-Based Intelligence (ABI)

  • Process Flow for Intelligence
  • Collect, characterize and locate activities and transactions
  • Target Motion and Measurement Models
  • Activity-Based Intelligence Methodology
  • Intelligence Discipline of Activity-based Intelligence (ABI)
  • Activity-based Intelligence (ABI) Applied
  • ABI and Effective Tactical Data Science
  • Multispectral and Hyperspectral Imaging Techniques
  • Medical Engineering & Physics
  • Wearable sensors
  • Sensor placement
  • 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


Sensor and Data Fusion Training Bootcamp

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