In House Short Course Content
- SIGNAL AND DATA PROCESSING OVERVIEW
- Sensor Processing Functions
- Signal Processing
- Tracking: Data Association and Filtering
- Target Typing or Classification
- Situation Assessment and Sensor Tasking
- Definition of Terms
- Signal Processing
- TDI and Matched Filter
- Type I and II Errors
- Signal Processing Stages and Functions
- Alternative Signal Processing Approaches
- MULTIPLE FRAME SIGNAL PROCESSING
- Multiple Frame Processing: Advantages and Disadvantages
- Alternative Approaches and Comparisons
- Background Subtraction
- Detection in State Space
- The Optimal Matched Filters
- Track Before Detect
- Recent Advances
- DATA ASSOCIATION (CORRELATION) AND FILTERING ISSUES
- Critical Issues
- Complicating Factors and Misassociations
- Sparse Vs Dense Environments
- Basic Functions
- Multiple Sensor Approaches
- Impact of Algorithm Selection
- Performance Vs Required Resources
- System Decomposition Methodology
- Current Challenges in Algorithm Development
- Practical Single and Multiple Target Tracking
- Track Initiation and Termination
- Track Maintenance and Sensor Fusion
- Major Classes of Tracking Problems
- ESTIMATION METHODS FOR TARGET TRACKING
- Pertinent Probabilistic Methods
- Parameter Estimation and Kalman Filtering Methods
- Error Analysis with and without Model Errors
- DECISION AND TARGET IDENTIFICATION METHODS
- Pertinent Pattern Recognition Methods
- Linear and Quadratic Discriminant Scores
- Markov Chain Techniques
- ISOLATED TARGET TRACKING WITH FALSE SIGNALS
- Major Aspects of Track Processing
- Simple and Complex Algorithms
- Classical Nearest Neighbor
- Gated Optimal
- Probabilistic Data Association Filter (PDAF)
- Multiple Frame Association Approaches
- Processing Maneuvering Targets: IMM and Alternative Algorithms
- Impact of Dense Environment on Tracking Performance for Various Algorithms
- Characteristic of Performance Vs Required Resources
- GATE ANALYSIS
- Gating Approaches
- Traditional Vs A Two-Stage Gating
- Gate Sizing
- Gate Search: Potentially Resource Intensive Function
- Predicting the Number of Detections in a Gate
- Relative Observation Density As a Predictor of Sensor Effectiveness, Processing Complexity and Performance
- OPTIMAL MULTIPLE TARGET TRACKING METHODS
- Solutions for Various Optimization Criteria
- Minimum Mean Square Error (MMSE)
- Maximum A Posteriori Probability (MAP)
- Simultaneous MAP (JMAP)
- Basis Free MMSE
- Comparing Alternate Methods
- A "Simple" Two Target Example
- Computation of Two Optimal Solutions
- Evaluation of Alternative Solutions
- Unique Multiple Target Tracking Phenomena
- Pertinent Likelihood Formulas
- PRACTICAL MULTIPLE TARGET TRACKING APPROACHES
- Dangers of Independent Track Processing
- The Assignment Approach
- Algorithms and Algorithm Architecture
- Optimal and Sub-Optimal Assignment
- Important Sparse Matrix Considerations
- Performance Results Vs Target Density
- Track Initiation Approaches
- Morefield's Approach
- Alternative Algorithms: Advantages and Disadvantages
- Joint Probabilistic Data Association (JPDA)
- Reid's Multiple Hypothesis Tracking (MHT)
- Forming the Multiple Hypothesis Tree
- Combining, Trimming and Clustering
- Approach Comparison and Algorithm Selection
- MULTIPLE SENSOR TRACKING AND SENSOR DATA FUSION
- Alternative Multiple Sensor Approaches
- Comparing Track Initiation Methods
- Ambiguities and Number of Frames Needed
- Multiple Sensor Track Maintenance
- Track-to-Track Association Approaches
- Impact of Preferential Tracking
- Sensor Data Fusion
- Two-Source Processing
- N-Source Data Fusion
- Alternative Algorithm Architectures
- Centralized, Distributed, Hierarchical and Hybrid Fusion Approaches
- Recent Developments in Track Fusion and Tracklets
- Decorrelation of Sensor Tracks
- Tracklets from Shadow Tracker
- Characteristics of Approaches and Selection Criteria
- PERFORMANCE EVALUATION OF MULTIPLE TARGET TRACKING
- Ambiguities caused by Misassociation or Unresolved Closely Spaced Objects
- A Two Step Methodology
- Alternative Assignment Approaches
- Criteria for Selecting an Approach
- A Proposed Methodology
- Measures of Performance
- Critical Performance Evaluation Parameters
- MULTIPLE SENSOR TRACKING OF CLUSTERS OR SMALL TARGETS
- The Challenge of and Needs for Cluster Tracking
- A Powerful Method for Tracking Clusters or Small Extended Objects
- Model and Parameter Selection
- Centroid and Extent Tracking Algorithms
- Performance Evaluation and Recent Results
- ALGORITHM SIZING: PREDICTING REQUIRED RESOURCES
- Filter Sizing and Tradeoffs
- Sizing Track Maintenance Processing
- An Analytical Sizing Method
- Predicting Required Thru-Put and Memory
- Sizing Track Initiation Processing
- Number of Candidate Tracks Per Real and False Track Initiators
- Predicting Required Resources
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Copyright 1995 O.E. Drummond