Learn · Perception Algorithms for Autonomous Systems
Lecture 14 of 76Week 3
Lecture 14: Introduction to Tracking & Challenges to Multi-Target tracking
Prof. Anirban Roy · IIT Kanpur
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- Perception Algorithms for Autonomous Systems, IIT Kanpur (at NPTEL)
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All the lectures
- Lecture 1 - Introduction to Autonomous Systems
- Lecture 02 : Introduction to Autonomous Systems (Military Use Cases)
- Lecture 03: Review of Estimation Paradigms : The Least Square Fit (1st Order , 2nd Order and 3rd Order Fit ))
- Lecture 04: Prerequisite of Kalman Filter (Repeat) Kalman Filter Fundamentals
- Lecture 05: Kalman Filter Properties
- Lecture 06: Target Dynamics Model (Maneuvering & Non-Maneuvering)
- Lecture 07: Sensor Measurement Model and False Alarm Simulation
- Lecture 08: Non linear Kalman Filter Family --- The Extended Kalman Filter
- Lecture 09: Non linear Kalman Filter Family --- The Pseudo-measurement Kalman Filter
- Lecture 10: Pseudo-linear Kalman Filter and Extended Kalman Filter Application In Bearing Angle Only Tracking Problem
- Lecture 11: Bearing Angle Only Tracking Problem (contd.) Comparison – PMF vs EKF
- Lecture 12: Statistical linearization and The Unscented Kalman Filter
- Lecture 13: Summary – Explained in Board
- Lecture 14: Introduction to Tracking & Challenges to Multi-Target tracking
- Lecture 15 (Part A): Multi-Target Tracking stages
- Lecture 15 (Part B): Multi-Target Tracking stages (Contd.)
- Lecture 16: Nearest Neighbour hood association and Example
- Lecture 17 (Part A): Theory of Probabilistic Data Association
- Lecture 17 (Part B): Theory of Probabilistic Data Association (Contd.)
- Lecture 18: PDAF Implementation including Track Coalescence Problem & Solution
- Lecture 19: Multi Hypothesis Tracking
- Lecture 19 (Part B): Multi Hypothesis Tracking
- Lecture 20: Hypothesis Oriented MHT implementation
- Lecture 21: Track-Oriented MHT Concept
- Lecture 22 - Part A: Implementation of Track Oriented MHT
- Lecture 22 - Part B: Implementation of Track Oriented MHT (Contd)
- Lecture 23: Multi-target tracking Algorithm Performance Comparisons
- Lecture 24: MHT Performance Indices
- Lecture 25: Optimal Sub-Pattern Assignment Metric
- Lecture 26 - Part A: Concept of Maneuvering Target Tracking and stochastic Hybrid System
- Lecture 26 - Part B: Concept of Maneuvering Target Tracking and stochastic Hybrid System (Contd.)
- Lecture 27: Interacting Multiple Model Algorithm for Maneuvering Target Tracking Stages
- Lecture 28: Problems and Solution for IMM Algorithms
- Lecture 29: Example of IMM tracking
- Lecture 30 - Part A: Application of IMM algorithm for GPS Navigation Problem
- Lecture 30 - Part B: Application of IMM algorithm for GPS Navigation Problem (Contd.)
- Lecture 31 - Part A: GPS Navigation Problem (contd.)
- Lecture 31 - Part B: GPS Navigation Problem (contd.)
- Lecture 32: ((GPS Navigation Problem (contd.))
- Lecture 33: Problems related to target tracking
- Lecture 34: Continuous Kalman Filter Problems
- Lecture 35: Information Filter Problem
- Lecture 36: Implementation of Cramer Rao Lower Bound in target tracking Problems
- Lecture 37: Problems on Bearing Angle only Target Tracking
- Lecture 38: Bearing Angle Only Target Tracking Problem (contd.)
- Lecture 39: Part 1 - Introduction to Sensor Fusion
- Lecture 39: Part 2 - Introduction to Sensor Fusion (Contd)
- Lecture 40: Problems Of Measurement Fusion
- Lecture 41: Recursive Estimation using Bayes principle
- Lecture 42: Kalman Filter Based Measurement Augmentation
- Lecture 43: Controller based Measurement Augmentation- Spacecraft Attitude Estimation & Control
- Lecture 44: Part 1 - Controller based Measurement Augmentation - Baro Altimeter Fusion
- Lecture 44: Part 2 - Controller based Measurement Augmentation - Baro Altimeter Fusion (Contd.)
- Lecture 45: Complementary Filter & CBR
- Lecture 46: State Vector Fusion
- Lecture 47: Decentralized State Vector Fusion
- Lecture 48: Error Ellipse using Covariance matrix
- Lecture 49 Part A: Introduction to Classification and Machine learning techniques
- Lecture 49 Part B: Introduction to Classification and Machine learning techniques (Contd.)
- Lecture 50: Supervisory learning process flow & Methodologies
- Lecture 51: Supervisory learning Methodologies (Naïve Bayes Classifier Example)
- Lecture 52: Supervisory learning Methodologies (Linear Regression)
- Lecture 53: Part A - Supervisory learning Methodologies (Linear Regression Tree)
- Lecture 53: Part B - Supervisory learning Methodologies (Linear Regression Tree) Contd.
- Lecture 54: Supervisory learning Methodologies (Logistic Regression)
- Lecture 55: Logistic Regression Solution Mechanism
- Lecture 56: Support Vector machine
- Lecture 57: Support Vector Classifier and Support Vector Machine
- Lecture 58: SVM Optimization solution
- Lecture 59: Fundamentals of Neural Network based classifiers
- Lecture 60: Convolutional Neural Network
- Lecture 61: Bias-Variance trade-off in Machine learning
- Lecture 62: Miscellaneous (Problems)
- Lecture 63: Part 1 - Miscellaneous (Problems)
- Lecture 63: Part 2 - Miscellaneous (Problems) Contd.
- Lecture 64 - Conclusion (few practical DTC Systems)