A comprehensive review of the literature on manoeuvring target tracking for both uncluttered and cluttered measurements is presented. Various discrete-time dynamical models including nonrandom input, random-input and switching or hybrid system manoeuvre models are presented.
The problem of manoeuvre detection is covered.We are going to discuss single target tracking using single model and multiple models. Further more we are going to describe multiple target tracking using multiple models.
Table of Contents
1. Introduction
2. New topics in target tracking
3. Overview of object tracking
4. Target tracking
Research Objectives and Topics
This work explores the foundational principles and advanced methodologies of target tracking, with a specific focus on mathematical modeling, maneuver detection, and multiple-model estimation algorithms for both single and multiple target tracking scenarios.
- Theoretical foundations of state-space models and target dynamics
- Maneuver detection techniques using hypothesis testing
- Analysis of single-model versus multiple-model tracking approaches
- Structural components of Interacting Multiple Model (IMM) algorithms
- Challenges and complexities in multiple target tracking (MTT)
Excerpt from the Book
Maneuver and Non-maneuver Target
Target motions are normally classified into two classes of modes: maneuver and non-maneuver.
A non-maneuvering motion is the straight and level motion at a constant velocity, sometimes also referred to as the uniform motion.
All the other motions belong to the maneuvering mode.
Summary of Chapters
Introduction: This chapter provides a high-level overview of the tracking domain, distinguishing between general object tracking and specific target tracking, including the categorization of target maneuvers.
New topics in target tracking: This section highlights emerging research areas, such as target tracking within wireless sensor networks and the application of compressive sensing for multiple target localization.
Overview of object tracking: This chapter defines the core concepts of object tracking, outlining the three fundamental stages of data processing: extraction, recognition, and tracking.
Target tracking: This comprehensive chapter details the mathematical modeling of target motion, maneuver detection strategies, and the structural implementation of multiple-model estimation algorithms like the IMM and IMM-JPDAF.
Keywords
Target tracking, Object tracking, Kalman Filter, State-space models, Maneuver detection, IMM algorithm, Data association, Wireless sensor network, Compressive sensing, Mathematical modeling, Multiple target tracking, Sensor fusion, Tracking algorithms, Trajectory estimation, Markov models
Frequently Asked Questions
What is the primary focus of this document?
The document focuses on the theory and algorithmic implementation of target tracking, specifically addressing how to handle maneuvering targets using various state-space and multiple-model approaches.
What are the central themes of the work?
The central themes include target motion modeling, maneuver detection, the limitations of single-model trackers, and the structural design of advanced multiple-model estimation algorithms.
What is the primary research goal?
The goal is to provide a structured overview of how target state trajectories can be accurately estimated in dynamic environments where the target behavior may change over time.
Which scientific methods are primarily discussed?
The text discusses state-space mathematical modeling, Kalman filter-based estimation, likelihood ratio tests for maneuver detection, and Interacting Multiple Model (IMM) algorithms.
What does the main body cover?
The main body covers the transition from basic non-maneuvering models to complex multiple-model algorithms, the mathematical representation of sensors, and the extension of tracking to multiple targets.
How would you characterize this work with keywords?
The work is best characterized by terms such as Target Tracking, Kalman Filter, Maneuver Detection, IMM Algorithm, and State-Space Modeling.
How do single-model approaches differ from multiple-model approaches?
Single-model approaches assume a fixed mode, which often leads to poor performance when a target maneuvers; multiple-model approaches use a bank of filters to adapt to mode changes and reduce model mismatch.
Why is maneuver detection essential in target tracking?
Maneuver detection is crucial because it allows the tracking algorithm to switch between different process noise models or filter structures, ensuring that the state estimate remains accurate when a target deviates from a constant-velocity path.
What is the significance of the IMM-JPDAF algorithm?
The IMM-JPDAF is designed for multiple target tracking; it combines the maneuver-handling capabilities of the IMM algorithm with Joint Probabilistic Data Association to handle the difficulty of associating measurements with specific targets in cluttered environments.
- Quote paper
- Mohamed El-Ghoboushi (Author), 2015, Single and multiple target tracking, Munich, GRIN Verlag, https://www.grin.com/document/310580
