After introducing the JDL data fusion model, several methods for describing sensor and data fusion architectures are presented. Data fusion algorithm taxonomies and a general description of the algorithms and methods used for detection, classification, identification, and state estimation and tracking are discussed next. This is followed by consideration of situation and threat assessment. Subsequent sections of this course more fully develop the classical inference, Bayesian, Dempster-Shafer, voting logic, artificial neural network, and fuzzy logic data fusion algorithms. Radar tracking system design considerations, multiple sensor registration, track initiation in clutter, Kalman filtering, interacting multiple models, and data fusion maturity as it affects real-time tracking complete the in-depth topics treated in the course.

Examples are offered to demonstrate the advantages of multisensor data fusion in systems that use microwave and millimeter-wave detection and tracking radars, laser radars (imagery and range data), and forward-looking IR sensors (imagery data). Many of the data fusion techniques also apply when it is desired to combine information from almost any grouping of sensors as long as they can supply the input data required by the fusion algorithm.

- Application of modern sensors to sensor and data fusion
- Advantages of multisensor data fusion for object discrimination and state estimation
- Multisensor data fusion principles, algorithms, and architectures that enable the assessment of new and existing systems.
- Taxonomies for target detection, classification, identification, and state estimation algorithms.
- Skills needed to develop and apply data fusion algorithms in more complex situations.
- Practical applications.

- Engineers, scientists, managers, designers, military operations personnel, and other users of multisensor data fusion for target detection, classification, identification, and tracking
- Those interested in selecting appropriate sensors for specific applications and applying data fusion techniques to advanced dynamic systems, such as classification of airborne targets, ground-based targets, and underwater targets
- Developers and users of real-time algorithms for intelligent machine development and multiple sensor technologies for non-cooperative target recognition

**Defense Applications of Multisensor Systems and Data Fusion**- Need for smart sensors
- Defining detection, classification, and identification

**Sensor Systems**- Multiple sensor phenomenology
- Military data fusion architectures
- Benefits of multiple sensor systems
- Atmospheric and obscurant effects on IR and MMW sensors
- Influence of sensor application on choice of MMW frequency and IR wavelength band

**Sensor and Data Fusion: What is it?**- Levels 0, 1, 2, 3, 4 and 5 fusion processing
- Duality of data fusion and resource management
- Data fusion architectures

**Taxonomy of Detection, Classification, and Identification Data Fusion Algorithms**- Physical models
- Feature-based inference:
- Parametric (Classical inference, Bayesian, Dempster-Shafer, others)
- Information-theoretic models (templates, artificial neural networks, clusters, voting, figures of merit, pattern recognition, others)

- Cognitive-based (knowledge-based expert systems, fuzzy set theory, others)

**Taxonomy of State Estimation and Tracking Data Fusion Algorithms**- Search directions
- Correlation and association of data and tracks:
- Data alignment
- Data and track association (prediction gates, correlation metrics, data and track-to-track association)
- Position, kinematic, and attribute estimation

**Fusion Levels 2, 3, 4, and 5**- Situation assessment (Level 2)
- Threat assessment (Level 3)
- Refinement of the data fusion process (Level 4)
- Human-computer interaction issues (Level 5)

**Classical Inference and Decision Theory**- Confidence interval
- Sample size for a desired margin of error
- Decision theory (choosing between two hypotheses) and significance tests
- Statistical significance
- z-test for a population mean
- t-test for a population mean
- One- and two-sided tests
- Type 1 and Type 2 errors
- Power of a test

**Bayesian Inference**- Conditional probabilities
- Bayes' rule with illustrative examples
- Comparison of Bayesian and classical inference
- The Monty Hall problem and cancer diagnosis as Illustrative applications of Bayes&8217; rule
- Bayes&8217; rule in terms of odds probability
- Bayesian inference fusion process with multiple sensor data
- Recursive updating of posterior probabilities
- Multispectral sensor example
- Mine detection example
- Freeway incident detection example

**Dempster-Shafer Evidential Reasoning**- Dempster-Shafer fusion process
- Probability mass
- Uncertainty interval
- Dempster&8217;s rule
- Comparison with Bayesian inference
- Probability mass function origination examples
- Using known characteristics of data gathered by the sensors
- Using confusion matrices

- Modifications of D-S theory with examples
- Pignistic transferable belief, plausibility transform, plausible and paradoxical reasoning, and other modifications of the original theory

**Artificial Neural Networks**- Linear classifiers
- Adaptive linear combiner
- Capacity of linear classifiers

- Nonlinear classifiers
- Madaline
- Feedforward networks
- Capacity of nonlinear classifiers

- Generalization
- Learning rules
- Linear mean square algorithms
- Perceptron rule
- Back propagation algorithm

- Linear classifiers

**Fuzzy Logic**- Fuzzy logic definitions and applications
- Elements of fuzzy systems
- Fuzzy sets
- Membership functions
- Production rules

- Fuzzy logic processing and examples
- Inverted pendulum
- Influence of fuzzy set widths and slopes
- Fuzzy Kalman filter and scene classifier

- Fuzzy neural network examples

**Voting Logic**- Definition
- Detection modes and confidence levels
- System detection and false alarm probabilities
- Relation of detection and false alarm probabilities to confidence levels
- False alarm probability selection
- Three-sensor examples

**Radar Tracking System Design Considerations**- Measurements versus tracks
- Characteristics of tracking radars
- Measures of quality for tracking
- Tracker design issues
- State space and coordinate conversion

**Multiple Sensor Registration**- A requirement for multisensor tracking
- Specific functional requirements
- Error sources
- Impact of bias errors on tracking
- Bias error budget

**Track Initiation in Clutter**- What is the initiation problem?
- The Sequential Probability Ratio Test (SPRT)
- Definition of the decision criteria
- Example application of the SPRT
- Properties of the SPRT

- Practical recommendations for track initiation

**Introduction to Kalman Filtering**- What is it and what does it do?
- The Kalman filter equations
- The filtering process
- Filter initialization
- Error covariance and Kalman filter recursive equations
- The Kalman gain: Keeping it large enough
- Models for the process noise covariance matrix Q

**Interacting Multiple Models (IMM)**- What is it and what does it do?
- The IMM process
- The components of a model

- Applications

**Multiple-Sensor Tracking Architectures and Data Fusion Maturity**- Data Fusion/Track Management Options
- Data fusion maturity

__Text:__

The text, **Sensor and Data Fusion: A Tool for Information Assessment and Decision Making**, Second Edition, Lawrence A. Klein (SPIE, PM 222, 2012), and lecture notes are distributed on the first day of the course. The notes are for participants only and are not available for sale.

In addition to the course text, Dr. Klein is the author of **Millimeter-Wave and Infrared Multisensor Design and Signal Processing**, (Artech House, 1997), which describes multisensor applications, design, and performance; and *Sensor Technologies and Data Requirements for ITS* (Artech House, 2001), which discusses sensor applications to traffic and transportation management systems. He collaborated with colleagues to prepare a review of data fusion methods that emphasize fusion of image features that aid object tracking using particle filtering, a Bayesian technique. It appears as âï¿½ï¿½Sensor and Data Fusion: Taxonomy, Challenges, and Applicationsâï¿½ï¿½ in the *Handbook on Soft Computing for Video Surveillance* (Francis and Taylor, 2011). Dr. Klein received his PhD in electrical engineering from New York University in 1973. He is a past reviewer for the *IEEE Transactions on Antennas and Propagation, IEEE Transactions on Geoscience and Remote Sensing*, and *IEEE Transactions on Aerospace and Electronic Systems*.

## Course: TRO-397 Duration: 3 Days FEE: $1,499 CEUs: 2.16

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