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Sensors 2018, 18(11), 3791; https://doi.org/10.3390/s18113791

Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty

1
Autonomous Systems and Intelligent Control International Joint Research Center, Xi’An Technological University, Xi’an 710021, China
2
School of Mechatronic Engineering, Xi’An Technological University, Xi’an 710021, China
*
Author to whom correspondence should be addressed.
Received: 11 September 2018 / Revised: 17 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
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Abstract

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch–Tung–Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm. View Full-Text
Keywords: network flow theory; multitarget tracking; spectral clustering; A* search algorithm; RTS smoother; integer programming network flow theory; multitarget tracking; spectral clustering; A* search algorithm; RTS smoother; integer programming
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Ma, T.; Gao, S.; Chen, C.; Song, X. Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty. Sensors 2018, 18, 3791.

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