信息融合在移动机器人目标定位中的应用研究
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摘要
当前移动机器人应用的研究重点之一是多传感器信息融合理论与目标定位技术相结合的方法,多传感器信息融合技术能够综合多个传感器提供的各个侧面信息,用以提高目标定位的精度,其定位性能更全面、更准确。
本文提出了一种抗野值的多次卡尔曼滤波的机动目标自适应跟踪方法。当测量值中存在野值时该方法自适应地修正传感器测量值,以达到修正Kalman滤波新息正交性的效果,并且根据目标的运动状态,调整观测噪声协方差的估计值,使其更符合实际的观测情况,同时解决了大步长滤波器的滞后性问题。理论和仿真结果的研究表明,本方法对提高机动目标的定位精度有着明显的优势。本文所作的主要工作如下:
首先,对信息融合的基本原理、意义、融合模型和估计理论作了较详细的介绍。
其次,针对卡尔曼滤波算法中野值及观测噪声协方差影响滤波精度的问题,提出了一种自适应的卡尔曼滤波算法,通过仿真实验,验证了该方法良好的定位效果。
然后,在传统分布式融合结构的基础上提出了基于卡尔曼滤波的分布式两层融合方法,通过仿真实验,验证了该方法的有效性。
最后,基于现有的实验平台设计了应用信息融合技术的移动机器人目标定位系统实现方案,及算法流程。该多传感器的信息融合系统将听觉定位、视觉和超声传感器结合起来形成了一种新的模型,为移动机器人的目标定位提出了一种创新性的新研究思路。
Nowadays, target locating with multi-sensor information fusion technology is one of the key technologies in researching on application of mobile robots. Multi-sensor information fusion technology is able to combine every aspect of information provided by several sensors; and increase the precision of target locating. The performance of the locating is more accurate and all-sided.
In this paper, an adaptively target tracking method based on double-Kalman filter in existence of outliers has been proposed. If there are outliers in measurements, the method can adaptively adjusting the measurement, to ensure the property of the innovation, meanwhile, it can change the measurement noise covariance based on the movement state of target, and it also solves the problem of the hysteretic of the big step filter. The simulation results proved that, the method is higher precision on target tracking. The main work of the paper is as follows:
Firstly, the basic principle, significance, fusion model and estimation theory are introduced more detailed.
Secondly, because the outliers and the measurement noise covariance infect the precision of Kalman filter, an adaptive Kalman filter is proposed. The simulation shows that the effect is good.
Thirdly, based on the normal distribution fusion structure, a multi-sensor optimal information decentralized fusion filtering with a two-layer fusion structure and Kalman filter is given for discrete time linear stochastic control systems. The simulation shows that the algorithm is effective.
Finally, Based on the Experimental platform, designed a set of information fusion locating system, and target calculation program flow chart. It combines visual sensors, audition sensors and the ultrasonic sensors to formation a new model of localization. And, proposed an innovative new method for mobile robot locating target.
本文提出了一种抗野值的多次卡尔曼滤波的机动目标自适应跟踪方法。当测量值中存在野值时该方法自适应地修正传感器测量值,以达到修正Kalman滤波新息正交性的效果,并且根据目标的运动状态,调整观测噪声协方差的估计值,使其更符合实际的观测情况,同时解决了大步长滤波器的滞后性问题。理论和仿真结果的研究表明,本方法对提高机动目标的定位精度有着明显的优势。本文所作的主要工作如下:
首先,对信息融合的基本原理、意义、融合模型和估计理论作了较详细的介绍。
其次,针对卡尔曼滤波算法中野值及观测噪声协方差影响滤波精度的问题,提出了一种自适应的卡尔曼滤波算法,通过仿真实验,验证了该方法良好的定位效果。
然后,在传统分布式融合结构的基础上提出了基于卡尔曼滤波的分布式两层融合方法,通过仿真实验,验证了该方法的有效性。
最后,基于现有的实验平台设计了应用信息融合技术的移动机器人目标定位系统实现方案,及算法流程。该多传感器的信息融合系统将听觉定位、视觉和超声传感器结合起来形成了一种新的模型,为移动机器人的目标定位提出了一种创新性的新研究思路。
Nowadays, target locating with multi-sensor information fusion technology is one of the key technologies in researching on application of mobile robots. Multi-sensor information fusion technology is able to combine every aspect of information provided by several sensors; and increase the precision of target locating. The performance of the locating is more accurate and all-sided.
In this paper, an adaptively target tracking method based on double-Kalman filter in existence of outliers has been proposed. If there are outliers in measurements, the method can adaptively adjusting the measurement, to ensure the property of the innovation, meanwhile, it can change the measurement noise covariance based on the movement state of target, and it also solves the problem of the hysteretic of the big step filter. The simulation results proved that, the method is higher precision on target tracking. The main work of the paper is as follows:
Firstly, the basic principle, significance, fusion model and estimation theory are introduced more detailed.
Secondly, because the outliers and the measurement noise covariance infect the precision of Kalman filter, an adaptive Kalman filter is proposed. The simulation shows that the effect is good.
Thirdly, based on the normal distribution fusion structure, a multi-sensor optimal information decentralized fusion filtering with a two-layer fusion structure and Kalman filter is given for discrete time linear stochastic control systems. The simulation shows that the algorithm is effective.
Finally, Based on the Experimental platform, designed a set of information fusion locating system, and target calculation program flow chart. It combines visual sensors, audition sensors and the ultrasonic sensors to formation a new model of localization. And, proposed an innovative new method for mobile robot locating target.
引文
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[2] [美]David L.Hall, James Linas编,杨露,耿伯英主译.多传感器数据融合手册[M].北京:电子工业出版社,2008.
[3]杨万海.多传感器数据融合及其应用[M].西安:电子科技大学出版社,2004.
[4] Hall D L,Mathematic. Techniques in Multisensor Data Fusion,Boston,[M]. Arteeh House, 1992.
[5] Hall DL,L linas. Handbook of Multisensor Data Fusion,Washington [D]. NY: CRC Press, 2001.
[6] Alouani A T, Gray J E. Theory of Distributed Estimation using Multiple Asynchronous Sensors [J]. IEEE Transactions on Aerospace Electronic Systems, 2005,41(2): 717-722.
[7] Bar-shalom Y. Hierarchical Tracking for the Real World [J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 846-850.
[8] Jung-Hyun Park, Kwee Bo Sim. A Design of Mobile Robot based on Network Camera and Sound Source Localization for Intelligent Surveillance System[A], International Conference on Control, Automation and Systems [C]. Edmonton, 2008: 198-201
[9] A .Anand Bardwaj, M. Anandaraj, K.Kapil, S. Vasuhi, Multi-sensor data fusion methods using sensor data compression and estimated weights[A]. IEEE-International Conference on Signal processing Communications and Networking[C], India: Madras Institute of Technology,Anna University Chennai India, 2008: 250-254.
[10]滕召胜.智能检测系统与数据融合[M].北京:机械工业出版社,2006.
[11]胡良梅.基于信息融合的图像理解方法研究[M].合肥:合肥工业出版社出版, 2007.
[12]韩崇昭,朱洪艳,等.多源信息融合[M].北京:清华大学出版社出版的,2006.
[13]耿峰,祝小平.基于模糊多传感器数据融合的目标跟踪系统[J].火力与指挥控制, 2008, 33(3): 93-96.
[14]李旭军.多传感器数据融合及其在潜艇目标识别中的应用: [硕士学位论文].武汉:武汉理工大学, 2006.
[15]胡勇.信息融合在模式识别中的应用研究: [硕士学位论文].合肥:合肥工业大学, 2004.
[16]王刚.证据理论及其在图像目标识别中的应用: [硕士学位论文].西安:西北工业大学, 2002.
[17]宋健,杨财,李红志,等. AYC系统基于多传感器数据融合的路面附着系数估计算法[J].清华大学学报(自然科学版),2009,49(5): 104-104.
[18]何友.多传感器信息融合及应用[M].北京:电子工业出版社, 2007.
[19]何友,薛培信,王国宏.一种新的信息融合功能模型[J].海军航空工程学院学报, 2008, 23(3): 241-248.
[20] Llinas J, Waltz E. Multisensor Data Fusion[M] . Artech House: Norwood, Massachusetts , 1990.
[21] Hall D L,Llinas J.Handbook of Multisensor Data Fusion[M]. CRC Press : The Electrical Engineering and Applied Signal Processing Series, 2001.
[22]何友.多目标多传感器分布信息融合算法研究:[硕士学位论文].北京:清华大学,1996.
[23]何友,彭应宁,陆大.多传感器数据融合模型评述[J].清华大学学报,1996, 36(9): 14-20.
[24]王征,刘宁庄,张建成.数据融合的方法及应用研究[J].自动化与仪器仪表,2006,126:77-80.
[25]朱泽君,黄涛,刘曦霞,等.多传感器数据融合技术研究现状及发展方向[J].舰船电子工程,2009,29(2):14-16.
[26]吕漫丽,孙灵芳.多传感器信息融合技术[J].通信与信息处理,2008,27(2):79-82.
[27]白云飞,曲尔光.多传感器信息融合技术及其应用[J].机械管理开发,2008,23(1):69-70.
[28]杨盘洪,朱军祥,赵建,等.多传感器数据融合机动目标跟踪算法[J].仪器仪表学报,2006,27(6):1679-1682.
[29]梁健.异类传感器数据融合技术应用[J].测控遥感与导航定位,2006,36(7):33-35.
[30]周样晶,江晶,赵杰.多传感器异步量测融合算法性能比较[J].火力与指挥控制,2009,34(7):40- 44 .
[31]罗志斌.异质传感器数据融合方法研究: [硕士学位论文].河南:河南大学,2009.
[32]杨盘洪,朱军祥,赵建安.等.多传感器数据融合机动目标跟踪算法[J].仪器仪表学报, 2006,6(27): 1679-1682.
[33]符拯,王书满,刘丙杰.自适应卡尔曼滤波的最新进展[J].战术导弹技术,2009,(6):62-66.
[34] Rashad S, Aboelmagd N. Online INS/GPS integration with a radial basis function neural network [J]. IEEE Aerospace and Electronic Systems Magazine, 2005, 20(3): 8-14.
[35]蒋恩松,李孟超,孙刘杰.一种基于神经网络的卡尔曼滤波改进方法[J].电子与信息学报,2007, 9(29):2073-2076.
[36] Al-Dhaher A G H, Mackesy D. Multi-sensor data fusion architecture[A].The third IEEE International Workshop on Haptic, Audio and Visual Environments and Their Applications[C]. Ottawa, Ontario, Canada, 2004:159-163.
[37] Hu Congwei, Chen Wu. Adaptive Kalman filtering for vehicle navigation [J]. Journal of Global Positiioning System , 2003, 2(1): 227-233.
[38] Qi lian Liang, Jerry M Mendel Equalization of non linear time-varying channels using type-2 fuzzy adaptive filters [J]. IEEE transaction on aerospace and electronics system,2000, (10): 110-115.
[39]胡绍林,孙国基.靶场外测数据野值点的统计诊断技术明[J].宇航学报,1999,20(2):68-74.
[40] Van dang, Tho. An adaptive Kalman Filter for radar tracking application [A]. Proceedings of Microwaves, Radar and Remote Sensing Symposium[C],Otewang, 2008:261-264.
[41]邓自立.最优估计理论及其应用[M].哈尔滨:哈尔滨工业大学出版社,2005.
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