Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking  

作　　者：Qiang GUO Long TENG Tianxiang YIN Yunfei GUO Xinliang WU Wenming SONG 

机构地区：[1]College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China [2]China National Aeronautical Radio Electronics Research Institute,Shanghai 200233,China [3]College of Automation,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2023年第11期1647-1656,共10页信息与电子工程前沿（英文版）

基　　金：Project supported by the Technology Foundation for Basic Enhancement Plan,China (No.2021-JCJQ-JJ-0301);the National Major Research and Development Project of China (No.2018YFE0206500);the National Natural Science Foundation of China (No.62071140);the National Special for International Scientific and Technological Cooperation of China (No.2015DFR10220)。

摘　　要：The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory.This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets,leveraging the advantages of both data-driven and model-based algorithms.The time-varying constant velocity model is integrated into the Gaussian process(GP)of online learning to improve the performance of GP prediction.This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking.Through the simulations,it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

关 键 词：Target tracking Gaussian process DATA-DRIVEN Online learning Model-driven Probabilistic data association 

分 类 号：TN953[电子电信—信号与信息处理]