采用自适应一致性UKF的分布式目标跟踪

郑斌琪; 李宝清; 刘华巍; 袁晓兵

doi:10.3788/OPE.20192701.0260

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采用自适应一致性UKF的分布式目标跟踪

信息科学 | 更新时间：2020-08-13

- 采用自适应一致性UKF的分布式目标跟踪
- Distributed target tracking based on adaptive consensus UKF
- 光学精密工程 2019年27卷第1期页码：260-270
- 作者机构：
  
  1.中国科学院上海微系统与信息技术研究所微系统技术重点实验室，上海 201800
  2.中国科学院大学，北京 100049
- 作者简介：
  
  [ "郑斌琪(1978-)，男，浙江宁波人，博士研究生，2001年、2004年于中国科学技术大学分别获得学士、硕士学位。主要从事特种无线传感器网络、传感器信息融合技术研究。Email:bqzheng@mail.sim.ac.cn" ]
  [ "袁晓兵 (1969-)，男，吉林长春人，博士生导师，研究员，1991年于浙江大学获得学士学位，1997年、2000年分别于中国科学院长春光学精密机械与物理研究所获得硕士、博士学位。主要从事无线传感器网络、物联网相关应用技术研究。Email: sinowsn@mail.sim.ac.cn" ]
- 基金信息：
  
  微系统技术重点实验室基金资助项目(6142804010205)
- DOI：10.3788/OPE.20192701.0260
  中图分类号： TP394.1;TH691.9
- 收稿日期：2018-06-28，
  
  录用日期：2018-8-20，
  
  纸质出版日期：2019-01-25
- 稿件说明：
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郑斌琪, 李宝清, 刘华巍, 等. 采用自适应一致性UKF的分布式目标跟踪[J]. 光学精密工程, 2019,27(1):260-270.

Bin-qi ZHENG, Bao-qing LI, Hua-wei LIU, et al. Distributed target tracking based on adaptive consensus UKF[J]. Optics and precision engineering, 2019, 27(1): 260-270.
郑斌琪, 李宝清, 刘华巍, 等. 采用自适应一致性UKF的分布式目标跟踪[J]. 光学精密工程, 2019,27(1):260-270. DOI： 10.3788/OPE.20192701.0260.

Bin-qi ZHENG, Bao-qing LI, Hua-wei LIU, et al. Distributed target tracking based on adaptive consensus UKF[J]. Optics and precision engineering, 2019, 27(1): 260-270. DOI： 10.3788/OPE.20192701.0260.

摘要

由于传统分布式跟踪方法在先验噪声协方差与其实际值不相匹配时跟踪误差较大，提出了一种采用自适应一致性无迹卡尔曼滤波的分布式目标跟踪方法，该方法首先执行分布式UKF算法得到对当前移动目标状态的估计值，然后通过一个系统错误检测机制，确定是否需要对噪声协方差值进行更新。如需要，则根据当前获得的测量信息去估计当前噪声协方差，并联合该估计值和先前的噪声协方差值获得一个新的先验噪声协方差值。最后根据新获得的噪声协方差值对获得的目标状态估计值进行修正。实验结果表明该方法具有较好的准确性和鲁棒性：在噪声未知环境下，基于ACUKF的分布式跟踪方法相比于基于容积信息滤波和基于分布式无迹卡尔曼滤波的跟踪方法，最大跟踪误差值分别减少了49.93%和51.46%;在目标过程噪声发生动态变化的情况下，提出的方法相比于上述两种传统跟踪方法，跟踪误差值分别减少了40.67%和40.06%。

Abstract

As the traditional distributed methods of target tracking may suffer from performance degradation owing to mismatch between the noise distributions assumed as a priori and the actual ones

a distributed target tracking method was proposed based on adaptive consensus unscented Kalman filter to improve the accuracy and robustness of the tracking results. More specifically

at each time step

a distributed UKF (DUK) would be implemented to obtain the estimations of the moving target. Next

an online fault-detection mechanism was adopted to judge if it was necessary to update current noise covariance. If it was necessary

the estimations of the current noise covariance would be calculated according to the measurement information. By utilizing a weighting factor

the filter would combine the last noise covariance matrices with the estimations to obtain the new noise covariance matrices. Finally

the state estimations would be corrected according to the new noise covariance matrices and previous state estimations. The experiment results demonstrate that: in unknown noise environments the tracking errors of the proposed method are reduced by as much as 49.93% and 51.46% when compared with those of the distributed tracking methods based on the cubature information filter and DUK

respectively; in dynamic noise environments the tracking errors of the proposed method are reduced by as much as 40.67% and 40.06% when compared with those of the above two traditional methods

respectively. These results demonstrate that the proposed method performs well in terms of accuracy and robustness on distributed tracking with uncertain noise.

关键词

Keywords

references

王秀友, 范建中, 刘华明, 等.自适应交互式融合的视觉跟踪[J].光学精密工程, 2017, 25(9):2499-2507. http://www.eope.net/CN/abstract/abstract17204.shtml

WANG X Y, FAN J Z, LIU H M, XU D Q.Compound control of photoelectric tracking by using adaptive Kalman filtering algorithm[J]. Opt.Precision Eng ., 2017, 25(9):2499-2507.(in Chinese) http://www.eope.net/CN/abstract/abstract17204.shtml

杨德东, 毛宁, 杨福才, 等.利用最佳伙伴相似性的改进空间正则化判别相关滤波目标跟踪[J].光学精密工程, 2018, 26(2):492-502.

YANG D D, MAO N, YANG F C, et al.Improved SRDCF object tracking via the Best-Buddies Similarity[J]. Opt.Precision Eng. , 2018, 26(2):492-502.(in Chinese)

KAMAL A T, BAPPY J, FARRELL J, et al ..Distributed multi-target tracking and data association in vision networks.[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence , 2016, 38(7):1397-1410.

ZHANG H, ZHOU X, WANG Z, et al.Adaptive consensus-based distributed target tracking with dynamic cluster in sensor networks[J]. IEEE Transactions on Cybernetics , 2018, 99:1-12.

ZHENG B, FU P, LI B, et al ..A robust adaptive unscented kalman filter for nonlinear estimation with uncertain noise covariance[J]. Sensors , 2018, 18(3):808-822.

JULIER S J, UHLMANN J K.Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE , 2004, 92(3):401-422.

LEE D J.Nonlinear estimation and multiple sensor fusion using unscented information filtering[J]. IEEE Signal Processing Letters , 2008, 15:861-864.

ARDESHIRI T, ÖZKAN E, ORGUNER U, et al.Approximate bayesian smoothing with unknown process and measurement noise covariances[J]. IEEE Signal Processing Letters , 2015, 22(12):2450-2454.

田俊林, 胡晓阳, 游安清.利用自适应卡尔曼滤波实现光电跟踪中的复合控制[J].光学精密工程, 2017, 25(7):1941-1947.

TIAN J L, HU X Y, YOU A Q.Compound control of photoelectric tracking by using adaptive Kalman filtering algorithm[J]. Opt.Precision Eng. , 2017, 25(7):1941-1947.(in Chinese)

GAO S, HU G, ZHONG Y.Windowing and random weighting‐based adaptive unscented Kalman filter[J]. International Journal of Adaptive Control & Signal Processing , 2015, 29(2):201-223.

LI W, SUN S, JIA Y, et al ..Robust unscented Kalman filter with adaptation of process and measurement noise covariances[J]. Digital Signal Processing , 2016, 48(C):93-103.

BAR-SHALOM Y, DAUM F, HUANG J.The probabilistic data association filter[J]. IEEE Control Systems , 2009, 29(6):82-100.

FU P, TANG H, CHENG Y, et al ..An energy-balanced multi-sensor scheduling scheme for collaborative target tracking in wireless sensor networks[J] .International Journal of Distributed Sensor Networks, 2017, 13(3):155014771769896.

YU Y.Consensus-based distributed linear filter for target tracking with uncertain noise statistics[J]. IEEE Sensors Journal , 2017, 17(15):4875-4885.

CHEN Y, ZHAO Q, AN Z, et al.Distributed multi-target tracking based on the K-MTSCF algorithm in camera networks[J]. IEEE Sensors Journal , 2016, 16(13):5481-5490.

JIA B, PHAM K D, BLASCH E, et al ..Information weighted consensus-based cooperative space object tracking to overcome malfunctioned sensors and noisy links[C]. International Conference on Information Fusion.IEEE , 2015: 1286-1292. https://ieeexplore.ieee.org/document/7266705

XIAO L, BOYD S, LALL S.A scheme for robust distributed sensor fusion based on average consensus[C]. International Symposium on Information Processing in Sensor Networks.IEEE , 2005: 9. https://www.researchgate.net/publication/221933082_A_Scheme_for_robust_distributed_sensor_fusion_based_on_average_consensus

KATRAGADDA S, SANMIGUEL J C, CAVALLARO A.Consensus protocols for distributed tracking in wireless camera networks[C]. International Conference on Information Fusion.IEEE , 2014: 1-8. https://ieeexplore.ieee.org/document/6916005

OLFATI-SABER R.Distributed Kalman filter with embedded consensus filters[C]. Decision and Control , 2005 and 2005 European Control Conference.Cdc-Ecc '05. IEEE Conference on.IEEE , 2006: 8179-8184. https://ieeexplore.ieee.org/document/1583486

LI W, JIA Y.Consensus-based distributed multiple model UKF for jump markov nonlinear systems[J]. IEEE Transactions on Automatic Control , 2012, 57(1):227-233.

HAJIYEV C, SOKEN H E.Robust adaptive unscented Kalman filter for attitude estimation of pico satellites[J]. International Journal of Adaptive Control & Signal Processing , 2014, 28(2):107-120.

GAO S, HU G, ZHONG Y.Windowing and random weighting‐based adaptive unscented Kalman filter[J]. International Journal of Adaptive Control & Signal Processing , 2015, 29(2):201-223.

ZHOU J, STEFAN K, OTMAR L.INS/GPS tightly-coupled integration using adaptive unscented particle filter[J]. Journal of Navigation , 2010, 63(3):491-511.

LI W, WEIG, HAN F, et al ..Weighted average consensus-based unscented Kalman filtering[J]. IEEE Transactions on Cybernetics , 2015, 46(2):558-567.

CHANDRA K P B, GU D W, POSTLETHWAITE I.Square root cubature information filter[J]. IEEE Sensors Journal , 2013, 13(2):750-758.

CHEN F, DELANNAY D, DE V C.An autonomous framework to produce and distribute personalized team-sport video summaries:a basketball case study[J]. IEEE Transactions on Multimedia , 2011, 13(6):1381-1394.

FU P, CHENG Y, TANG H, et al ..An effective and robust decentralized target tracking scheme in wireless camera sensor networks[J]. Sensors , 2017, 17(3):639-652.

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