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

Qiang GUO; Long TENG; Tianxiang YIN; Yunfei GUO; Xinliang WU; Wenming SONG

doi:10.1631/FITEE.2300348

Your Location：

Home >

Browse articles >

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

Regular Papers | Updated：2023-10-12

- Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking
- 基于混合驱动高斯过程学习的强机动多目标跟踪方法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 11, Pages: 1647-1656(2023)
- Affiliations：
  
  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
- Author bio：
  
  †E-mail: guoqiang@hrbeu.edu.cn
  ‡Corresponding author
  tianxiangyin@hdu.edu.cn
  gyf@hdu.edu.cn
- Funds：
- DOI：10.1631/FITEE.2300348
  CLC： TN953
- Received：18 May 2023，
  
  Accepted：04 October 2023，
  
  Published：0 November 2023
- Accepted：
Scan QR Code
Qiang GUO, Long TENG, Tianxiang YIN, et al. Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1647-1656.
DOI：

Qiang GUO, Long TENG, Tianxiang YIN, et al. Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1647-1656. DOI： 10.1631/FITEE.2300348.

摘要

现有机动目标跟踪方法在杂波环境中强机动目标的跟踪性能并不令人满意。本文提出一种混合驱动方法，利用数据驱动和基于模型算法的优点跟踪多个高机动目标。将时变恒速（CV）模型集成到在线学习的高斯过程（GP）中，提高高斯过程的预测性能。进一步与广义概率数据关联（GPDA）算法相结合，实现多目标跟踪。通过仿真实验可知，与广泛使用的机动目标跟踪算法如交互式多模型（IMM）和数据驱动的高斯过程运动跟踪器（GPMT）相比，提出的混合驱动方法具有显著的性能优势。

Abstract

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

Keywords

references

Aftab W , Mihaylova L , 2020 . On the impact of different kernels and training data on a Gaussian process approach for target tracking . Proc IEEE 23 rd Int Conf on Information Fusion , p. 1 - 6 . https://doi.org/10.23919/FUSION45008.2020.9190413 https://doi.org/10.23919/FUSION45008.2020.9190413

Aftab W , Mihaylova L , 2021 . A learning Gaussian process approach for maneuvering target tracking and smoothing . IEEE Trans Aerosp Electron Syst , 57 ( 1 ): 278 - 292 . https://doi.org/10.1109/TAES.2020.3021220 https://doi.org/10.1109/TAES.2020.3021220

Da K , Li TC , Zhu YF , et al. , 2021 . Recent advances in multisensor multitarget tracking using random finite set . Front Inform Technol Electron Eng , 22 ( 1 ): 5 - 24 . https://doi.org/10.1631/FITEE.2000266 https://doi.org/10.1631/FITEE.2000266

Deng LC , Li D , Li RF , 2020 . Improved IMM algorithm based on RNNs . J Phys Conf Ser , 1518 : 012055 . https://doi.org/10.1088/1742-6596/1518/1/012055 https://doi.org/10.1088/1742-6596/1518/1/012055

Guo YF , Fan KS , Peng DL , et al. , 2015 . A modified variable rate particle filter for maneuvering target tracking . Front Inform Technol Electron Eng , 16 ( 11 ): 985 - 994 . https://doi.org/10.1631/FITEE.1500149 https://doi.org/10.1631/FITEE.1500149

Guo YF , Tharmarasa R , Rajan S , et al. , 2016 . Passive tracking in heavy clutter with sensor location uncertainty . IEEE Trans Aerosp Electron Syst , 52 ( 4 ): 1536 - 1554 . https://doi.org/10.1109/TAES.2016.140820 https://doi.org/10.1109/TAES.2016.140820

Guo YF , Li Y , Tharmarasa R , et al. , 2019 . GP-PDA filter for extended target tracking with measurement origin uncertainty . IEEE Trans Aerosp Electron Syst , 55 ( 4 ): 1725 - 1742 . https://doi.org/10.1109/TAES.2018.2875555 https://doi.org/10.1109/TAES.2018.2875555

Guo YF , Li Y , Ren X , et al. , 2020a . Multiple maneuvering extended target tracking based on Gaussian process . Acta Autom Sin , 46 ( 11 ): 2392 - 2403 (in Chinese) . https://doi.org/10.16383/j.aas.c180849 https://doi.org/10.16383/j.aas.c180849

Guo YF , Li Y , Xue AK , et al. , 2020b . Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes . Signal Process , 172 : 107547 . https://doi.org/10.1016/j.sigpro.2020.107547 https://doi.org/10.1016/j.sigpro.2020.107547

Guo YF , Zhu JJ , Zhou S , et al. , 2022 . A joint model and data driven track segment association algorithm for manoeuvring target tracking . IET Radar Sonar Nav , 16 ( 10 ): 1670 - 1680 . https://doi.org/10.1049/rsn2.12288 https://doi.org/10.1049/rsn2.12288

Huber MF , 2014 . Recursive Gaussian process: on-line regression and learning . Patt Recogn Lett , 45 : 85 - 91 . https://doi.org/10.1016/j.patrec.2014.03.004 https://doi.org/10.1016/j.patrec.2014.03.004

Li TC , Hlawatsch F , 2021 . A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters . Inform Fusion , 73 : 111 - 124 . https://doi.org/10.1016/j.inffus.2021.02.020 https://doi.org/10.1016/j.inffus.2021.02.020

Li TC , Su JY , Liu W , et al. , 2017 . Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond . Front Inform Technol Electron Eng , 18 ( 12 ): 1913 - 1939 . https://doi.org/10.1631/FITEE.1700379 https://doi.org/10.1631/FITEE.1700379

Li TC , Liu ZG , Pan Q , 2019a . Distributed Bernoulli filtering for target detection and tracking based on arithmetic average fusion . IEEE Signal Procss Lett , 26 ( 12 ): 1812 - 1816 . https://doi.org/10.1109/LSP.2019.2950588 https://doi.org/10.1109/LSP.2019.2950588

Li TC , Chen HM , Sun SD , et al. , 2019b . Joint smoothing and tracking based on continuous-time target trajectory function fitting . IEEE Trans Autom Sci Eng , 16 ( 3 ): 1476 - 1483 . https://doi.org/10.1109/TASE.2018.2882641 https://doi.org/10.1109/TASE.2018.2882641

Liu JX , Wang ZL , Xu M , 2020 . DeepMTT: a deep learning maneuvering target-tracking algorithm based on bi-directional LSTM network . Inform Fusion , 53 : 289 - 304 . https://doi.org/10.1016/j.inffus.2019.06.012 https://doi.org/10.1016/j.inffus.2019.06.012

Liu XC , Lyu C , George J , et al. , 2022 . A learning distributed Gaussian process approach for target tracking over sensor networks . Proc 25 th Int Conf on Information Fusion , p. 1 - 8 . https://doi.org/10.23919/FUSION49751.2022.9841315 https://doi.org/10.23919/FUSION49751.2022.9841315

Pan Q , Ye XN , Zhang HC , 2005 . Generalized probability data association algorithm . Acta Electron Sin , 33 ( 3 ): 467 - 472 . https://doi.org/10.3321/j.issn:0372-2112.2005.03.021 https://doi.org/10.3321/j.issn:0372-2112.2005.03.021

Rasmussen CE , Williams CKI , 2006 . Gaussian Processes for Machine Learning . MIT Press , Cambridge, USA .

Sun MW , Davies ME , Proudler I , et al. , 2020 . A Gaussian process based method for multiple model tracking . Proc Sensor Signal Processing for Defence Conf , p. 1 - 5 . https://doi.org/10.1109/SSPD47486.2020.9272174 https://doi.org/10.1109/SSPD47486.2020.9272174

Tian WM , Fang LL , Li WD , et al. , 2022 . Deep-learning-based multiple model tracking method for targets with complex maneuvering motion . Remote Sens , 14 ( 14 ): 3276 . https://doi.org/10.3390/rs14143276 https://doi.org/10.3390/rs14143276

Wang LP , Zhan RZ , Huang Y , et al. , 2021 . Joint tracking and classification of extended targets with complex shapes . Front Inform Technol Electron Eng , 22 ( 6 ): 839 - 861 . https://doi.org/10.1631/FITEE.2000061 https://doi.org/10.1631/FITEE.2000061

Wu WH , Cai YC , Jin HB , et al. , 2021 . Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems . Front Inform Technol Electron Eng , 22 ( 1 ): 79 - 87 . https://doi.org/10.1631/FITEE.2000105 https://doi.org/10.1631/FITEE.2000105

Xiong W , Zhu HF , Cui YQ , 2022 . Recurrent adaptive maneuvering target tracking algorithm based on online learning . Acta Aeronaut Astronaut Sin , 43 ( 5 ): 325250 (in Chinese) . https://doi.org/10.7527/S1000-6893.2021.25250 https://doi.org/10.7527/S1000-6893.2021.25250

Zhang D , Liu MQ , Zhang SL , et al. , 2018 . Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks . Front Inform Technol Electron Eng , 19 ( 4 ): 544 - 556 . https://doi.org/10.1631/FITEE.1601695 https://doi.org/10.1631/FITEE.1601695

Zhang XR , He FH , Zheng TY , 2019 . An LSTM-based trajectory estimation algorithm for non-cooperative maneuvering flight vehicles . Proc Chinese Control Conf , p. 8821 - 8826 . https://doi.org/10.23919/ChiCC.2019.8866249 https://doi.org/10.23919/ChiCC.2019.8866249

Zheng Z , Cai SC , 2021 . A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities . Front Inform Technol Electron Eng , 22 ( 10 ): 1334 - 1350 . https://doi.org/10.1631/FITEE.2000362 https://doi.org/10.1631/FITEE.2000362

Zhou R , Feng Y , Bin D , et al. , 2020 . Multi-UAV cooperative target tracking with bounded noise for connectivity preservation . Front Inform Technol Electron Eng , 21 ( 10 ): 1494 - 1503 . https://doi.org/10.1631/FITEE.1900617 https://doi.org/10.1631/FITEE.1900617

Zhu Y , Liang S , Wu XJ , et al. , 2021 . A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain . Front Inform Technol Electron Eng , 22 ( 8 ): 1114 - 1126 . https://doi.org/10.1631/FITEE.2000209 https://doi.org/10.1631/FITEE.2000209

Views

183

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Joint target tracking using an autonomous underwater vehicle and underwater sensor networks for underwater applications

Ahybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

Multi-agent differential game based cooperative synchronization control using a data-driven method

Related Author

Yi YANG

Boon-Chong SEET

Zhenkai ZHANG

Zhaohong LV

Wei LU

Liyong ZHANG

Yuhu WU

Chongquan ZHONG

Related Institution

Wuhan Maritime Communication Research Institute

Electrical and Electronic Engineering Department, Auckland University of Technology

Ocean College, Jiangsu University of Science and Technology

School of Control Science and Engineering, Dalian University of Technology

Electronic Information School, Wuhan University

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰