状态数据协方差交叉融合算法下分布式多传感器目标联合定位

doi:10.13229/j.cnki.jdxbgxb.20240444

摘要/Abstract

摘要：

针对单个传感器目标定位技术无法适应复杂的环境及易受外界干扰的问题，提出了状态数据协方差交叉融合算法下分布式多传感器目标联合定位方法。通过分布式多传感器观测目标物体的位置信息，并将位置信息作为目标状态数据；在此基础上将各传感器的目标状态观测值输入到卡尔曼滤波器中展开误差补偿，以提高后续目标联合定位的精度。由此，利用协方差交叉融合算法对误差补偿后的各传感器观测值展开融合，融合值即为最终的目标位置，以此完成目标联合定位。实验结果表明：该方法对10个目标物体的定位结果交并比均接近于1，且得到的目标物体位置坐标最接近物体的实际位置坐标，说明本文方法的抗干扰性能强、目标定位准确性高。

关键词: 协方差交叉融合, 分布式多传感器, 目标联合定位, 卡尔曼滤波, 误差补偿

Abstract:

Aiming at the problems of single sensor target localization technology being unable to adapt to complex environments and susceptible to external interference， a distributed multi-sensor target joint localization method based on state data covariance cross fusion algorithm is proposed. Observing the position information of target objects through distributed multiple sensors and using the position information as target state data； On this basis， input the target state observation values of each sensor into the Kalman filter for error compensation， in order to improve the accuracy of subsequent target joint positioning； Therefore， the covariance cross fusion algorithm is used to fuse the observation values of each sensor after error compensation， and the fusion value is the final target position， thereby completing the joint target localization. The experimental results show that the intersection to union ratio of the positioning results for 10 target objects is close to 1， and the obtained target object position coordinates are closest to the actual position coordinates of the object. The proposed method has strong anti-interference performance and high accuracy in target localization.

Key words: covariance cross fusion, distributed multi-sensor, joint target localization, kalman filtering, error compensation

中图分类号:

TN953

张鹏,周书玉,刘鹏. 状态数据协方差交叉融合算法下分布式多传感器目标联合定位[J]. 吉林大学学报(工学版), 2024, 54(11): 3417-3422.

Peng ZHANG,Shu-yu ZHOU,Peng LIU. Distributed multi-sensor target joint localization under state data covariance cross fusion algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3417-3422.

图/表 5

图1

表1

图2

表2

图3

参考文献 15

1	张文玥,娄小平,陈福笛. 移动机器人多传感器融合定位仿真研究[J]. 计算机仿真,2023,40(3):436-441.
	Zhang Wen-yue, Lou Xiao-ping, Chen Fu-di. Research on multi-sensor fusion localization simulation of mobile robot[J]. Computer Simulation,2023,40(3):436-441.
2	滕洪钊,邓朝晖,吕黎曙,等.多传感器信息融合的加工过程状态监测研究[J].机械工程学报,2022,58(6):26-41.
	Teng Hong-zhao, Deng Chao-hui, Lv Li-shu, et al. Research of process condition monitoring based on multi-sensor information fusion[J]. Journal of Mechanical Engineering, 2022, 58(6): 26-41
3	丁一鹏,柳润金.一种基于改进Viterbi频率估计技术的穿墙雷达目标定位算法[J].电子与信息学报,2022,44(4):1203-1211.
	Ding Yi-peng, Liu Run-jin. A Target location algorithm for through-wall radar based on improved viterbi frequency estimation technology[J]. Journal of Electronics and Information Technology, 2022, 44(4): 1203-1211.
4	Liu X B, Li H S, Xue J Y, et al. Location and tracking of environmental pollution sources under multi-UAV vision based on target motion model[J]. Soft Computing, 2023, 27(20): 15337-15351.
5	张沛朋,李冬锋.基于MCMC的无线传感器网络目标定位算法[J].计算机工程与设计,2022,43(7):1864-1871.
	Zhang Pei-peng, Li Dong-feng. Target localization in wireless sensor networks based on Markov-chain Monte-Carlo[J]. Computer Engineering and Design, 2022, 43(7): 1864-1871.
6	Liu Y S, Wang Q, Qin Y, et al. Microwave target location method based on the diamond NV color center[J]. Applied Optics, 2023, 62(16): 4275-4280.
7	谢德胜,徐友春,陆峰,等.基于多传感器信息融合的3维目标实时检测[J].汽车工程,2022,44(3):340-349, 339.
	Xie De-sheng, Xu You-chun, Lu-Feng, et al. Real-time detection of 3D objects based on multi-sensor information fusion[J]. Automotive Engineering, 2022, 44(3): 340-349, 339.
8	戴炳哲,张其林,侯文豪,等.内蒙古自治区真实地形和地球曲率对闪电定位影响[J].电瓷避雷器,2022,(2):66-72, 80.
	Dai Bing-zhe, Zhang Qi-lin, Hou Wen-hao, et al. Effects of real terrain and earth curvature on lightning location in inner mongolia autonomous region [J]. Electric Porcelain Arrester, 2022(2): 66-72, 80.
9	李宛桐,姜明,史静,等.基于地心坐标系的卫星导航测风平滑算法[J].气象科技,2022,50(2):171-178.
	Li Wan-tong, Jiang Ming, Shi Jing, et al. Smoothing algorithm of upper wind based on geocentric coordinate system for satellite navigation radiosonde system[J]. Meteorological Science and Technology, 2022, 50(2): 171-178.
10	周娟,林加顺,吴乃豪,等.一种联合PID控制与扩展卡尔曼滤波的磷酸铁锂电池荷电状态估算方法[J].电网技术,2023,47(4):1623-1632.
	Zhou Juan, Lin Jia-shun, Wu Nai-hao, et al. State of charge estimation for LiFeO₄ battery combining PID control and extended Kalman filter[J]. Power Grid Technology, 2023,47 (4): 1623-1632.
11	朱志锋,张绍义.用耦合方法研究Markov过程的f-遍历[J].数学学报:中文版,2022,65(6):1137-1142.
	Zhu Zhi-feng, Zhang Shao-yi. f-ergodicity of markov process by coupling method[J]. Journal of Mathematics: Chinese edition, 2022, 65 (6): 1137-1142.
12	杨云生,魏国东,柴凯,等.基于马尔可夫过程的舰船装备可用度分析[J].海军工程大学学报,2022,34(4):13-19.
	Yang Yun-sheng, Wei Guo-dong, Chai Kai, et al. Availability analysis of ship equipment based on Markov process[J]. Journal of Naval Engineering University, 2022, 34(4): 13-19.
13	成庶,刘嘉文,伍珣,等.基于特征提取与误差补偿的金属化薄膜电容器剩余寿命预测[J].中国电机工程学报,2022,42(7):2672-2681.
	Cheng Shu, Liu Jia-wen, Wu Xun, et al. Residual lifetime prediction of metallized film capacitors based on feature extraction and error compensation[J]. Chinese Journal of Electrical Engineering, 2022, 42(7): 2672-2681.
14	张大龙,余刚,李致远,等.考虑杆臂误差的组合导航分离协方差交叉算法[J].郑州大学学报:工学版,2022,43(3):8-14.
	Zhang Da-long, Yu Gang, Li Zhi-yuan, et al. Split covariance intersection algorithm for integrated navigation system considering lever arm error[J]. Journal of Zhengzhou University (Engineering Edition), 2022, 43(3): 8-14.
15	冯迅,杨健,周涛,等.基于注意力机制及类别层次结构的弱监督目标定位[J].软件学报,2023,34(10):4916-4929.
	Feng Xun, Yang Jian, Zhou Tao, et al. Target location and detection algorithm for complex scene of fan blade based on deep learning[J]. Journal of Software, 2023,34(10): 4916-4929.

相关文章 15

[1]	才华,寇婷婷,杨依宁,马智勇,王伟刚,孙俊喜. 基于轨迹优化的三维车辆多目标跟踪[J]. 吉林大学学报(工学版), 2024, 54(8): 2338-2347.
[2]	王宁,刘繁明. 基于约束优化的自适应衰减记忆平方根混合阶容积粒子滤波在惯性/卫星组合导航中的应用[J]. 吉林大学学报(工学版), 2024, 54(12): 3660-3672.
[3]	王小艺,刘迪一,于家斌,何卓昀,赵峙尧. 复杂风场环境下的多旋翼无人机编队故障检测方法[J]. 吉林大学学报(工学版), 2023, 53(3): 823-831.
[4]	徐卓君,王耀祥,黄兴,彭程. 多无人机地面移动目标搜寻和定位[J]. 吉林大学学报(工学版), 2023, 53(3): 832-840.
[5]	马永杰,陈敏. 基于卡尔曼滤波预测策略的动态多目标优化算法[J]. 吉林大学学报(工学版), 2022, 52(6): 1442-1458.
[6]	李文航,倪涛,赵丁选,张泮虹,师小波. 基于集合卡尔曼滤波的高机动救援车辆主动悬挂控制方法[J]. 吉林大学学报(工学版), 2022, 52(12): 2816-2826.
[7]	谢少彪,张宇,温凯瑞,张硕,刘宗明,齐乃明. 非合作目标强跟踪容积卡尔曼滤波运动状态估计[J]. 吉林大学学报(工学版), 2021, 51(4): 1482-1489.
[8]	李静,石求军,洪良,刘鹏. 基于车辆状态估计的商用车ESC神经网络滑模控制[J]. 吉林大学学报(工学版), 2020, 50(5): 1545-1555.
[9]	李静,石求军,刘鹏,户亚威. 基于纵向车速估算的商用车ABS神经网络滑模控制[J]. 吉林大学学报(工学版), 2019, 49(4): 1017-1025.
[10]	王德军,吕志超,王启明,张建瑞,丁建楠. 基于EKF及调制傅式级数的缸压辨识[J]. 吉林大学学报(工学版), 2019, 49(4): 1174-1185.
[11]	田彦涛, 张宇, 王晓玉, 陈华. 基于平方根无迹卡尔曼滤波算法的电动汽车质心侧偏角估计[J]. 吉林大学学报(工学版), 2018, 48(3): 845-852.
[12]	朱枫, 张葆, 李贤涛, 王正玺, 张士涛. 基于强跟踪卡尔曼滤波的陀螺信号处理[J]. 吉林大学学报(工学版), 2017, 47(6): 1868-1875.
[13]	李静, 张家旭, 张艳华, 陈立军. 采用强跟踪中心差分滤波器的汽车状态和参数估计[J]. 吉林大学学报(工学版), 2017, 47(5): 1329-1335.
[14]	鄢永耀, 刘伟, 付锦江. 高定位精度转台检测系统调整误差补偿[J]. 吉林大学学报(工学版), 2017, 47(3): 855-860.
[15]	林楠, 施树明, 马力, 隗海林. 含坡度变化率信息的道路坡度估计[J]. 吉林大学学报(工学版), 2016, 46(6): 1845-1850.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

参数名称		参数值
卡尔曼滤波器参数	初始位置	（0 m， 0 m）
卡尔曼滤波器参数	初始速度	（0 m/s， 0 m/s）
协方差交叉融合算法参数	状态转移矩阵维度	4×4
	融合权重	0.4
	融合周期	每5个采样周期一次

目标序号	本文方法	文献［3］方法	文献［4］方法
1	0.967 5	0.867 8	0.813 5
2	0.956 3	0.843 9	0.796 2
3	0.943 3	0.901 5	0.802 3
4	0.962 7	0.876 2	0.813 7
5	0.949 6	0.891 6	0.791 5
6	0.943 9	0.882 5	0.823 5
7	0.954 6	0.902 8	0.815 2
8	0.963 8	0.863 7	0.799 3
9	0.947 1	0.855 4	0.806 8
10	0.958 4	0.881 6	0.837 5