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Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin
Tianli Ma1,2, Song Gao3,4, Chaobo Chen5
1Autonomous Systems and Intelligent Control International Joint Research Center, Xi'An Technological University, Xi'an 710021, China. matianli111@xatu.edu.cn.
Sensors (Basel, Switzerland)
|November 9, 2018
Summary
This study introduces an Adaptive Network Graph Segmentation (ANGS) algorithm for multitarget tracking with measurement origin uncertainty. ANGS demonstrates superior tracking accuracy and robustness compared to existing methods in simulations.
Area of Science:
- Computer Science
- Signal Processing
- Robotics
Background:
- Multitarget tracking is challenging due to measurement origin uncertainty.
- Existing algorithms may struggle with complex clutter environments.
Purpose of the Study:
- To develop a novel algorithm for robust multitarget tracking.
- To address the problem of measurement origin uncertainty in tracking.
Main Methods:
- Formulated multitarget tracking as an Integer Programming problem in a cost flow network.
- Employed Adaptive Spectral Clustering with the Nyström Method for network partitioning.
- Utilized parallel A* search for global optimal solutions within sub-networks.
- Applied Track Mosaic and Rauch-Tung-Striebel (RTS) smoother for trajectory extraction.
Main Results:
- The proposed Adaptive Network Graph Segmentation (ANGS) algorithm achieved higher tracking accuracy.
- ANGS demonstrated improved robustness against varying clutter intensities.
- Performance was superior to A* search, successive shortest-path (SSP), and shortest path faster (SPFA) algorithms.
Conclusions:
- ANGS provides an effective solution for multitarget tracking with measurement origin uncertainty.
- The algorithm's performance is validated through simulations in diverse clutter conditions.
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