基于分布式有限感知网络的多伯努利目标跟踪

吴孙勇; 王力; 李天成; 孙希延; 蔡如华

doi:10.16383/j.aas.c200481

基于分布式有限感知网络的多伯努利目标跟踪

doi: 10.16383/j.aas.c200481

吴孙勇^{1, 2,},
王力^1,,
李天成^3,,
孙希延^2,,
蔡如华^1,

1.
桂林电子科技大学数学与计算科学学院桂林 541004
2.
广西精密导航技术与应用重点实验室桂林 541004
3.
西北工业大学自动化学院西安 710071

基金项目: 国家自然科学基金(61861008, 62071389, 11661024), 广西自然科学基金(2016GXNSFAA380073), 广西研究生教育创新计划(2020YCXS084), 桂林电子科技大学数学与计算科学学院论文培优项目(2019YJSPY04)资助

详细信息

作者简介:
吴孙勇：桂林电子科技大学数学与计算科学学院教授. 2011年获西安电子科技大学信号与信息处理博士学位. 主要研究方向为多目标检测与跟踪, 阵列信号处理. 本文通信作者. E-mail: wusunyong121991@163.com

王力：桂林电子科技大学数学与计算科学学院硕士研究生. 主要研究方向为多目标检测与跟踪, 多传感器信息融合. E-mail: wangli1581960594@163.com

李天成：西北工业大学自动化学院教授. 2013年获英国伦敦南岸大学博士学位, 2015年获西北工业大学博士学位. 主要研究方向为分布式信息融合, 协作移动机器人和目标检测, 跟踪和轨迹预测的数据驱动算法. E-mail: t.c.li@nwpu.edu.cn

孙希延：桂林电子科技大学信息与通信工程学院教授. 主要研究方向为卫星通信, 卫星导航. E-mail: sunxiyan1@163.com

蔡如华：桂林电子科技大学数学与计算科学学院副教授. 主要研究方向为小波分析, 信号处理和粒子滤波. E-mail: ruhuac@guet.edu.cn

计量
- 文章访问数: 1270
- HTML全文浏览量: 626
- PDF下载量: 256
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-29
- 录用日期: 2021-01-15
- 网络出版日期: 2021-02-07
- 刊出日期: 2022-05-13

Multi-Bernoulli Target Tracking Based on Distributed Limited Sensing Network

WU Sun-Yong^{1, 2
,},
WANG Li^1
,,
LI Tian-Cheng^3
,,
SUN Xi-Yan^2
,,
CAI Ru-Hua^1
,

1.
Mathematics and Computer Science College, Guilin University of Electronic Technology, Guilin 541004
2.
Guangxi Key Laboratory of Precision Technology and Application, Guilin 541004
3.
School of Automation, Northwestern Polytechnical University, Xi＇an 710071

Funds: Supported by National Natural Science Foundation of China (61861008, 62071389, 11661024), Guangxi Natural Science Foundation (2016GXNSFAA380073), Guangxi Graduate Education Innovation Plan Project (2020YCXS084), Thesis Training Program of School of Mathematics and Computational Science, Guilin University of Electronic Technology (2019YJSPY04)

More Information

Author Bio:
WU Sun-Yong　Professor at the School of Mathematics and Computational Science, Guilin University of Electronic Science and Technology. He received his Ph. D. degree in Signal and Information Processing from Xidian University in 2011. His research interest covers multi-target detection and tracking, array signal processing. Corresponding author of this paper

WANG Li　Master student at the School of Mathematics and Computational Science, Guilin University of Electronic Technology. His research interest covers multi-target detection and tracking, multi-sensor information fusion

LI Tian-Cheng　Professor at the School of Automation, Northwestern Polytechnical University (NPU). He received the first Ph. D. degree from London South Bank University, UK in 2013, and the second Ph. D. degree from NPU, China, in 2015. His research interest covers distributed information fusion, collaborative mobile robots and data-driven algorithms for target detection, tracking, and trajectory forecasting

SUN Xi-Yan　Professor at the School of Information and Communication Engineering, Guilin University of Electronic Technology. Her research interest covers statellite communications, navigation satellite

CAI Ru-Hua　Associate professor at the School of Mathematics and Computational Science, Guilin University of Electronic Science and Technology. His research interest covers wavelet analysis, signal processing and particle filtering

摘要

摘要: 针对感知范围受限的分布式传感网多目标跟踪问题, 在多伯努利滤波跟踪理论基础上提出分布式视场互补多伯努利关联算术平均融合跟踪方法. 首先, 通过视场互补扩大传感器感知范围, 其中, 局部公共区域只互补一次以降低计算成本. 其次, 每个传感器分别运行局部多伯努利滤波器, 并将滤波后验结果与相邻传感器进行泛洪通信使得每个传感器获取多个相邻传感器的后验信息. 随后, 通过距离划分进行多伯努利关联, 将对应于同一目标的伯努利分量关联到同一个子集中, 并对每个关联子集进行算术平均融合完成融合状态估计. 仿真实验表明, 所提方法在有限感知范围的分布式传感器网络中能有效地进行多目标跟踪.
- 有限感知范围 /
- 视场互补 /
- 多伯努利关联 /
- 算术平均融合 /
- 多目标跟踪
Abstract: In order to solve the problem of multi-target tracking in distributed sensor networks with limited sensing range (LSR), a distributed arithmetic average (AA) fusion multi-Bernoulli filter is proposed based on field of view complementation and multi-Bernoulli association. First, the sensor＇s sensing range is expanded by complementing the field of view, in which the local common areas are complemented only once to reduce the calculation cost. Secondly, each sensor separately operates a local multi-Bernoulli filter and conducts flood communication between neighbor sensors over the filter posteriors, so that each sensor can obtain posterior information of multiple sensors. Then, multi-Bernoulli correlation is performed by distance division to associate Bernoulli components corresponding to the same target to the same subset, and the AA fusion is performed for each associated subset to complete fusion state estimation. Simulation results show that the proposed method can effectively track multiple targets in distributed sensor networks with LSR.
- Limited sensing range /
- complementary field of view /
- multi-Bernoulli correlation /
- arithmetic average fusion /
- multi-target tracking

HTML全文

图 1 有限传感范围分布式传感器网络

Fig. 1 Distributed sensor networks with limited sensing range

下载: 全尺寸图片幻灯片

图 2 分布式传感器网络与真实轨迹

Fig. 2 Distributed sensor networks and real trajectories

下载: 全尺寸图片幻灯片

图 3 各传感器视场互补后滤波跟踪的TNOSPA

Fig. 3 Tracking error TNOSPA of local sensors with complementary field of view

下载: 全尺寸图片幻灯片

图 4 M1情况下目标跟踪性能

Fig. 4 Target tracking performance in M1

下载: 全尺寸图片幻灯片

图 5 M2情况下目标跟踪性能

Fig. 5 Target tracking performance in M2

下载: 全尺寸图片幻灯片

图 6 第7个传感器跟踪性能对比结果

Fig. 6 The sensor 7 tracks performance comparison results

下载: 全尺寸图片幻灯片

图 7 多传感器多伯努利滤波AA融合仿真效果

Fig. 7 Multi-sensor multi-Bernoulli filter AA fusion simulation effect

下载: 全尺寸图片幻灯片

图 8 目标数为11的仿真效果

Fig. 8 Simulation effect with the target number of 11

下载: 全尺寸图片幻灯片

图 9 本文方法在不同存活率下的跟踪性能

Fig. 9 The tracking performance of this paper under different survival rates1

下载: 全尺寸图片幻灯片

图 10 本文方法在不同检测概率下的跟踪性能

Fig. 10 The tracking performance of this paper under different detection probability

下载: 全尺寸图片幻灯片

图 11 不同方法的 TN-OSPA 误差统计图

Fig. 11 TN-OSPA error statistics of different methods

下载: 全尺寸图片幻灯片

表 1 目标初始位置和存活时间

Table 1 Target＇s initial position and survival time

目标	出生位置	出生时间 (s)	死亡时间 (s)
目标1	[−596.14, −606.75]	1	70
目标2	[307.38, 693.2]	10	65
目标3	[692.7, 206.8]	20	80
目标4	[700, 200]	30	60
目标5	[−603.9, −588.93]	40	100
目标6	[294.12, 705.41]	50	100

下载: 导出CSV

表 2 单次MC平均运行时间

Table 2 Average running time per MC

方法	时间(s)
未互补估计(M1)	2.7923
视场互补估计(M2)	9.8989
共享估计(M3)	32.7096

下载: 导出CSV

表 3 单次MC平均运行时间

Table 3 Average running time per MC

方法	时间(s)
单互补估计	9.9252
量测聚类估计	10.4984
未互补融合估计	15.5495
单共享估计	31.5351
互补融合估计	45.5696

下载: 导出CSV

参考文献(37)

[1]	杨小军. 无线传感器网络下分布式决策融合方法综述. 计算机工程与应用, 2012, 48(11): 1-6. doi: 10.3778/j.issn.1002-8331.2012.11.001 Yang Xiao-jun. Review of distributed decision fusion in wireless sensor networks. Computer Engineering and Applications, 2012, 48(11): 1-6. doi: 10.3778/j.issn.1002-8331.2012.11.001
[2]	陈辉, 韩崇昭. 机动多目标跟踪中的传感器控制策略的研究. 自动化学报, 2016, 42(4): 34-45. Chen Hui, Han Chon-Zhao. Sensor control strategy for maneuvering multi-target tracking. Acta Automatica Sinica, 2016, 42(4): 34-45.
[3]	Shu S L, Lin H L, Ma J, Li X Y. Multi-sensor distributed fusion estimation with applications in networked systems: A review paper. Information Fusion, 2017, 38: 122-134. doi: 10.1016/j.inffus.2017.03.006
[4]	李正杰, 谢军伟, 张浩为, 蔡保杰, 葛佳昂. 基于集中式MIMO雷达的多目标跟踪功率分配优化算法. 空军工程大学学报(自然科学版), 2019, 20(5): 76-82. Li Zheng-Jie, Xie Jun-Wei, Zhang Hao-Wei, Cai Bao-Jie, Ge Jia-Ang. Multi-target tracking power allocation optimization algorithm based on centralized MIMO radar. Journal of Air Force Engineering University (Natural Science Edition), 2019, 20(5): 76-82.
[5]	Mahler R P S. Advances In Statistical Multisource Multitarget Information Fusion. Artech House, 2014.161-644
[6]	Vo B N, MA W K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104. doi: 10.1109/TSP.2006.881190
[7]	Vo B T, Vo B N, Cantoni A. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Transactions on Signal Processing, 2007, 55(7): 3553 -3567. doi: 10.1109/TSP.2007.894241
[8]	Mahler R P S, Ebrary I. Statistical Multisource-Multitarget Information Fusion. Norwood, MA: Artech House, 2007.
[9]	Vo B T, Vo B N, Cantoni A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Transactions on Signal Processing, 2009, 57(2): 409-423. doi: 10.1109/TSP.2008.2007924
[10]	Li T C, Wang X X, Liang Y, Pan Q. On arithmetic average fusion and its application for distributed multi-Bernoulli multitarget tracking. IEEE Transactions on Signal Processing, 2020, 68: 2883–2896.
[11]	陈辉, 贺忠良, 邓东明, 李国财. 高斯混合多伯努利滤波器基于柯西施瓦兹散度的传感器控制方法. 电子学报, 2020, 48(4): 706-716. doi: 10.3969/j.issn.0372-2112.2020.04.012 Chen Hui, He Zhong-Liang, Deng Dong-Ming, Li Guo-Cai. Sensor control using Cauchy-Schwarz divergence via Gaussian mixture multi-Bernoulli filter. Acta Electronica Sinica, 2020, 48(4): 706-716. doi: 10.3969/j.issn.0372-2112.2020.04.012
[12]	王佰录. 基于随机集理论的分布式多传感器多目标跟踪技术研究[博士学位论文]. 电子科技大学, 中国, 2018 Wang Bai-Lu. Distributed Multi-sensor Multi-target Tracking in the Framework of Random Finite Sets [Ph. D. dissertation]. University of Electronic Science and Technology of China, China, 2018
[13]	Ren W, Beard R W, Atkins E M. Information consensus in multivehicle cooperative control. IEEE Control systems magazine, 2007, 27(2): 71-82. doi: 10.1109/MCS.2007.338264
[14]	Li T C, Fan H Q, Garcia J, Corchado J M. Second-order statistics analysis and comparison between arithmetic and geometric average fusion: application to multi-sensor target tracking. Information Fusion, 2019, 51: 233-243. doi: 10.1016/j.inffus.2019.02.009
[15]	Li G C, Battistelli G, Yi W, Kong L J. Distributed multi-sensor multi-view fusion based on generalized covariance intersection. Signal Processing, 2020, 166: 107246. doi: 10.1016/j.sigpro.2019.107246
[16]	Wang B L, Yi W, Hoseinnezhad R, Li S Q, Yang X B. Distributed fusion with multi-Bernoulli filter based on generalized covariance intersection. IEEE Transactions on Signal Processing, 2016, 65(1): 242-255.
[17]	王佰录, 易伟, 李溯琪, 孔令讲, 杨晓波. 分布式多目标伯努利滤波器的网络共识技术. 信号处理, 2018, 34(01): 1-12. Wang Bai-Lu, Yi Wei, Li Su-Qi, Kong Ling-Jiang, Yang Xiao-Bo. Consensus for distributed multi-Bernoulli filter. Signal Processing, 2018, 34(1): 1-12.
[18]	Da K, Li T C, Zhu Y F, Fan H Q, Fu Q. Kullback-Leibler averaging for multitarget density fusion. In: Proceedings of the International Symposium on Distributed Computing and Artificial Intelligence. Springer, 2019. 253−261
[19]	Li T C, Corchado J M, Sun S D. Partial consensus and conservative fusion of gaussian mixtures for distributed PHD fusion. IEEE Transactions on Aerospace and Electronic Systems, 2017, 55(5): 2150-2163.
[20]	Li T C, Hlawatsch F. A distributed particle-PHD filter with arithmetic-average PHD fusion. arXiv preprint arXiv: 1712.06128
[21]	Li T C, Liu Z G, Pan Q. Distributed Bernoulli filtering for target detection and tracking based on arithmetic average fusion. IEEE Signal Processing Letters, 2019, 26(12): 1812-1816. doi: 10.1109/LSP.2019.2950588
[22]	Gao L, Battistelli G, Chisci L. Multiobject fusion with minimum information loss. IEEE Signal Process Letters, 2020, 27: 201-205. doi: 10.1109/LSP.2019.2963817
[23]	Kim H, Granstrǒm K, Gao L, Battistelli G, Kim S, Wymeersch H. 5G mmWave cooperative positioning and mapping using multi-model PHD filter and map fusion. IEEE Transactions on Wireless Communications, 2020, 19(6): 3782-3795.
[24]	卢建华, 韩旭, 李冀鑫. 带宽受限下的基于一致性的分布式融合估计器. 控制与决策, 2016, 31(12): 2155-2162. Lu Jian-Hua, Han Xu, Li Ji-Xin. Consensus-based distributed fusion estimator with communication bandwidth constraints. Control and Decision, 2016, 31(12): 2155-2162.
[25]	Olfati S R, Sandell N F. Distributed tracking in sensor networks with limited sensing range. In: Proceedings of the American Control Conference. Washington, USA: IEEE, 2008. 3157−3162
[26]	Gan J, Vasic M, Martinoli A. Cooperative multiple dynamic object tracking on moving vehicles based on sequential monte carlo probability hypothesis density filter. In: Proceedings of the 19th International Conference on Intelligent Transportation Systems. Rio, Brazil: IEEE, 2016. 2163−2170
[27]	Kamal A T, Farrell J A, Roy-Chowdhury A K. Information weighted consensus filters and their application in distributed camera networks. IEEE Transactions on Automatic Control, 2013, 58(12): 3112-3125. doi: 10.1109/TAC.2013.2277621
[28]	Ilic N, Stankovic M S, Stankovic S S. Adaptive consensus-based distributed target tracking in sensor networks with limited sensing range. IEEE Transactions on Control Systems Technology, 2013, 22(2): 778-785.
[29]	Li T C, Elvira V, Fan H Q, Corchado J M. Local-diffusion based distributed SMC-PHD filtering using sensors with limited sensing range. IEEE Sensors Journal, 2018, 19(4): 1580-1589.
[30]	Yi W, Jiang M, Li S Q, Wang B L. Distributed sensor fusion for RFS density with consideration of limited sensing ability. In: Proceedings of the 20th International Conference on Information Fusion. Xi＇an, China: IEEE, 2017. 1−6
[31]	Li S Q, Battistelli G, Chisci L, Yi W, Wang B L, Kong L J. Multi-sensor multi-object tracking with different fields-of-view using the LMB filter. In: Proceedings of the 21st International Conference on Information Fusion. Cambridge, UK: IEEE, 2018. 1201−1208
[32]	Vo B N, Singh S, Doucet A. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245. doi: 10.1109/TAES.2005.1561884
[33]	Mahler R P S. "Statistics 101" for multi-sensor, multitarget data fusion. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 53-64. doi: 10.1109/MAES.2004.1263231
[34]	李天成, 范红旗, 孙树栋, 粒子滤波理论, 方法及其在多目标跟踪中的应用. 自动化学报, 2015, 41(12): 1981-2002. LI Tian-Cheng, Fan Hong-Qi, Sun Shu-Dong, Particle filtering: theory, approach, and application for multitarget tracking. Acta Automatica Sinica, 2015, 41(12): 1981-2002.
[35]	Ye J C, Bresler Y, Moulin P. Asymptotic global confidence regions in parametric shape estimation problems. IEEE Transactions on Information Theory, 2000, 46(5): 1881-1895. doi: 10.1109/18.857798
[36]	刘国营, 陈秀宏. 多目标跟踪算法的最优子模式分配概率度量. 计算机工程, 2013, 39(5): 293-296. doi: 10.3969/j.issn.1000-3428.2013.05.065 Liu Guo-Ying, Chen Xiu-Hong. Optimal sub pattern assignment probability metric for multi-target tracking algorithm. Computer Engineering, 2013, 39(5): 293-296. doi: 10.3969/j.issn.1000-3428.2013.05.065
[37]	Li T C, Corchado J M, Chen H M. Distributed flooding-then-clustering: A lazy networking approach for distributed multiple target tracking. In: Proceedings of the 21st International Conference on Information Fusion. Cambridge, UK: IEEE, 2018.