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CLC number: TN953

On-line Access: 2015-11-04

Received: 2015-05-06

Revision Accepted: 2015-06-23

Crosschecked: 2015-10-20

Cited: 0

Clicked: 2360

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yun-fei Guo

http://orcid.org/0000-0001-7887-4312

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.11 P.985-994

http://doi.org/10.1631/FITEE.1500149


A modified variable rate particle filter for maneuvering target tracking


Author(s):  Yun-fei Guo, Kong-shuai Fan, Dong-liang Peng, Ji-an Luo, Han Shentu

Affiliation(s):  Automation School, Hangzhou Dianzi University, Hangzhou 310018, China

Corresponding email(s):   gyf@hdu.edu.cn

Key Words:  Maneuvering target tracking, Prolonged smooth regions, Variable rate model, Maneuver detection


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Yun-fei Guo, Kong-shuai Fan, Dong-liang Peng, Ji-an Luo, Han Shentu. A modified variable rate particle filter for maneuvering target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11): 985-994.

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Abstract: 
To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

This paper presents a hybrid work that integrates the well-known technologies for maneuver-detection (MD), variable rate particle filtering (VRPF) and unscented particle filter (UPF), as the name of the proposed filter 'MD-VR-UPF' indicates. Although new contribution is limited, the simulation results show that good result has been achieved. Overall, the paper is well written.

改进的变速率粒子滤波及其在机动目标跟踪中的应用

目的:针对一类特定的机动目标跟踪问题(目标长时间近似匀速直线运动,偶尔强机动运动),提出一种兼顾机动期间跟踪精度与非机动期间系统负担的非线性滤波方法。
创新点:提出便于工程应用的基于笛卡尔坐标系的变速率模型和改进的变速率粒子滤波;提出机动检测方法而非多模型方法动态调整变速率粒子滤波的核心参数。通过在改进的变速率粒子滤波中嵌入无味粒子滤波,进一步改进估计精度。
方法:首先,提出笛卡尔坐标系下的变速率采样模型。此时状态驻留时间不再与测量周期保持一致,从而可以减少目标非机动期间的状态更新次数。其次,提出一种基于机动检测的改进变速率粒子滤波方法,当目标机动或非机动时,动态调整变速率模型的Gamma分布参数。目标机动时,状态更新更频繁,粒子分布范围更广;目标非机动时,状态更新次数较少,降低系统存储和计算负担。最后,在改进的变速率粒子滤波中采用无味粒子滤波,进一步提高估计精度。
结论:改进的变速率粒子滤波方法采用笛卡尔系而非本体坐标系,需要的先验信息更少,更便于工程应用。通过动态调整算法中的分布参数,采用无味粒子滤波方法,可以兼顾目标在非机动和机动期间的跟踪性能,同时降低系统的计算和存储负担。

关键词:机动目标跟踪;长期平滑区域;变速率模型;机动检测

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Reference

[1]Bar-Shalom, Y., Li, X.R., 1995. Multitarget-Multisensor Tracking: Principles and Techniques. Yaakov Bar-Shalom.

[2]Bar-Shalom, Y., Li, X.R., Kirubarajan, T., 2001. Estimation with Applications to Tracking and Navigation. Johh Wiley & Sons, New York, USA.

[3]Bunch, P., Godsill, S., 2013. Particle smoothing algorithms for variable rate models. IEEE Trans. Signal Process., 61(7):1663-1675.

[4]Farina, A., Ristic, B., Benvenuti, D., 2002. Tracking a ballistic target: comparison of several nonlinear filters. IEEE Trans. Aerosp. Electron. Syst., 38(3):854-867.

[5]Godsill, S.J., Vermaak, J., Ng, W., et al., 2007. Models and algorithms for tracking of maneuvering objects using variable rate particle filters. Proc. IEEE, 95(5):925-952.

[6]Julier, S., Uhlmann, J., Durrant-Whyte, H.F., 2000. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Contr., 45(3):477-482.

[7]Li, T.C., Bolic, M., Djuric, P.M., 2015. Resampling methods for particle filtering: classification, implementation, and strategies. IEEE Signal Process. Mag., 32(3):70-86.

[8]Li, X.R., Jilkov, V.P., 2003. Survey of maneuvering target tracking. Part I: dynamic models. IEEE Trans. Aerosp. Electron. Syst., 39(4):1333-1364.

[9]Li, X.R., Jilkov, V.P., 2010. Survey of maneuvering target tracking. Part II: motion models of ballistic and space targets. IEEE Trans. Aerosp. Electron. Syst., 46(1):96-119.

[10]Li, X.R., Zhao, Z.L., Jilkov, V.P., 2001. Practical measures and test for credibility of an estimator. Proc. Workshop on Estimation, Tracking, and Fusion: a Tribute to Yaakov Bar-Shalom, p.481-495.

[11]Nemeth, C., Fearnhead, P., Mihaylova, L., 2014. Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments. IEEE Trans. Signal Process., 62(5):1245-1255.

[12]Ru, J.F., Jilkov, V.P., Li, X.R., et al., 2009. Detection of target maneuver onset. IEEE Trans. Aerosp. Electron. Syst., 45(2):536-554.

[13]Schoenecker, S., Willett, P., Bar-Shalom, Y., 2013. The ML-PMHT multistatic tracker for sharply maneuvering targets. IEEE Trans. Aerosp. Electron. Syst., 49(4):2235-2249.

[14]Ulker, Y., Gunsel, B., 2012. Multiple model target tracking with variable rate particle filters. Dig. Signal Process., 22(3):417-429.

[15]van der Merwe, R., Doucet, A., de Freitas, N., et al., 2000. The unscented particle filter. NIPS, p.584-590.

[16]Whiteley, N., Johansen, A.M., Godsill, S., 2011. Monte Carlo filtering of piecewise deterministic processes. J. Comput. Graph. Stat., 20(1):119-139.

[17]Yang, W., Wang, Z.X., Fu, Y.W., et al., 2015. Joint detection, tracking and classification of a manoeuvring target in the finite set statistics framework. IET Signal Process., 9(1):10-20.

[18]Zhang, W., Zuo, J.Y., Guo, Q., et al., 2015. Multisensor information fusion scheme for particle filter. Electron. Lett., 51(6):486-488.

[19]Zhang, Y.J., Geng, Z., 2013. Detection of target maneuver from bearings-only measurements. IEEE Trans. Aerosp. Electron. Syst., 49(3):2028-2034.

[20]Zuo, J.Y., Jia, Y.N., Zhang, Y.Z., et al., 2013. Adaptive iterated particle filter. Electron. Lett., 49(12):742-744.

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