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A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
Xuedong Wang1, Tiancheng Li2, Shudong Sun3
1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China. xuedong.wang@mail.nwpu.edu.cn.
Sensors (Basel, Switzerland)
|November 24, 2017
Summary
Particle filtering (PF) algorithms have advanced significantly for single and multiple target tracking, despite challenges like false data and complex scenarios. Ongoing research addresses limitations in efficiency and uncertainty for robust tracking systems.
Area of Science:
- Computer Science
- Signal Processing
- Robotics
Background:
- Particle filtering (PF) is crucial for target tracking.
- Advances in PF have been made over the last decade.
- Tracking involves single or multiple targets with potential data issues.
Purpose of the Study:
- To review recent advancements in particle filtering for target tracking.
- To analyze challenges in single and multi-target tracking scenarios.
- To highlight areas for future research in particle filtering.
Main Methods:
- Review of single-target PF improvements (importance proposal, efficiency, degeneracy, constrained systems).
- Analysis of multi-target PF challenges (birth/termination, false alarms, M2T uncertainty).
- Categorization of multi-target PF approaches (M2T association-based, finite set statistics-based).
Main Results:
- Significant progress in single-target PF algorithms.
- Persistent challenges in multi-target tracking, including data association and scenario uncertainty.
- Identification of limitations in current mainstream multi-target PF methods.
Conclusions:
- Particle filtering has seen notable progress for target tracking.
- Further research is needed to address complex multi-target tracking scenarios and integrated management.
- Enhanced PF algorithms are essential for robust real-world tracking applications.
