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.
Highlights
The purpose of multitarget tracking is to jointly estimate the number of targets and their state of motion from sensor data [1]
Our main contributions are: (1) a parallel network search framework is presented to cope with the multitarget tracking in the presence of measurement origin uncertainty; and (2) we proposed a adaptive spectral clustering algorithm based on the Nyström Method to obtain the network segmentation results for an unknown cluster number data set
The optimal subpattern assignment (OSPA) [31] metric is used for performance assessment
Summary
The purpose of multitarget tracking is to jointly estimate the number of targets and their state of motion from sensor data [1]. It has applied a scaling push relabel method to find the optimal solution Under this framework, Zhang [18] formulated the multitarget data association problem as a maximum a posteriori (MAP) problem. The Shortest Path Faster algorithm (SPFA) is used to solve the Integer Programming problem of the min-cost flow network and quickly obtains the global optimal solution in [20]. Our main contributions are: (1) a parallel network search framework is presented to cope with the multitarget tracking in the presence of measurement origin uncertainty; and (2) we proposed a adaptive spectral clustering algorithm based on the Nyström Method to obtain the network segmentation results for an unknown cluster number data set.
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