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Online multi-target intelligent tracking using a deep long-short term memory network

Chinese Journal of Aeronautics 2023年第9期36卷 313-329页

作者： Yongquan ZHANG Zhenyun SHI Hongbing JI Zhenzhen SU School of Electronic Engineering Xidian UniversityXi’an 710071China School of Computer Science and Technology Xidian UniversityXi’an 710071China

Multi-target tracking is facing the difficulties of modeling uncertain motion and observation *** tracking algorithms are limited by specific models and priors that may mismatch a real-world *** this paper,considering the model-free purpose,we present an online Multi-Target Intelligent tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM ***,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,***,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement ***,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and *** results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.

关键词： Data association Deep long-short term memory network Historical sequence Multi-target tracking Target tuple set track management

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Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

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Science China Earth Sciences 2016年第12期59卷 86-96页

作者： Jinbin FU Jinping SUN Songtao LU Yingjing ZHANG School of Electronic and Information Engineering Beihang University Department of Electrical and Computer Engineering Iowa State University

To date, Wald sequential probability ratio test(WSPRT) has been widely applied to track management of multiple hypothesis tracking(MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT(SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

关键词： multiple target tracking multiple hypothesis tracking Shiryayev sequential probability ratio test track management track score

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