期刊文献+

用自适应扩展卡尔曼滤波跟踪水下目标的算法研究 被引量:2

Adaptive Extended Kalman Filter for Underwater Target Tracking
下载PDF
导出
摘要 针对短基线声纳系统跟踪水下目标的问题,建立了状态方程和观测方程,提出了一种自适应扩展卡尔曼滤波的跟踪算法,该算法包含八个基本步骤。将声波传输时延转换为水下目标的距离,用卡尔曼滤波的方法对数据中的噪声进行滤波。对目标的匀速航行和机动航行进行了仿真实验,实验结果表明了该算法的正确性和有效性。最后将该算法用于水下目标的实测数据,收到了良好的效果。 The underwater target tracking problem is investigated in a short-base line sonar system. Based on the state and observation equations, it proposes an approach of adaptive extended Kalman filter, which consists of eight steps. Signal time delay is converted to underwater distance, and noise is filtered out with Kalman filter. The approach is evaluated with simulations where the target is at either regular or flexible speed. Also it is tested with real datasets. The results from the experiments confirm the effectiveness of the approach.
出处 《科学技术与工程》 2007年第15期3710-3714,共5页 Science Technology and Engineering
关键词 卡尔曼滤波 目标跟踪 信号时延 声纳 Kalman filter target tracking signal time delay sonar
  • 相关文献

参考文献4

二级参考文献8

  • 1[1]P.L. Bolgler. Rdar Principle with Applocations to Tracking Systems. John Willey&Sons,New York, 1990
  • 2[2]A. Gelb, ed. Applied Optimal Estimation. The MIT. PRESS Cambridge, 1974
  • 3[3]S.S. Blackman. Multiple Target Tracking with Radar Applications. Artech House. Dedham. 1986
  • 4[4]周宏仁,敬忠良,王陪德.机动目标跟踪.北京:国防工业出版社,1994
  • 5ANDERSON K L,ILTIS R A.A distributed bearing-only tracking algorithm using reduce sufficient statistics[J].IEEE Transaction on Aerospace and Electronic Systems,1996,32 (1):339-349.
  • 6ILTIS R A,ANDERSON K L.A consistent estimation criterion for multisensor bearing-only tracking[J].IEEE Transactions on Aerospace and Electronic System,1996,32(1):108-120.
  • 7马驰洲,杨亦春.基于Kalman滤波的被动声定位后置处理[C]//中国声学学会2003年青年学术会议,济南,2003.
  • 8许江湖,张永胜,嵇成新.使用常速和常角速度模型跟踪机动目标[J].情报指挥控制系统与仿真技术,2001(9):35-40. 被引量:2

共引文献4

同被引文献24

  • 1Kalman R E. A new approach to linear filtering and prediction problems. Transaction of the ASME-Journal of Basic Engineering, 1960:35-45.
  • 2Zaknich A. Principles of adaptive filters and self-learning systems. Springer, 2005.
  • 3Ere M, Atherton D P, Bather J A. Adaptive Kalman filters for manoeuvring target tracking. In: Proc. IEE Collo- quium Target Tracking and Data Fusion, 1998.
  • 4Bahari M H, Karsaz A, Khaloozadeh H. High maneuver target tracking based on combined Kalman filter and fuzzy logic. In: Proc Information, Decision and Control IDC, 2007:59--64.
  • 5Sriyananda H. A simple method for the control of divergence in Kalman-filter algorithms. International Journal of Control, 1972; 16(6): 1101--1106.
  • 6Hu C, Chen W, Chen Y, Liu D. Adaptive Kalman filter- ing for vehicle navigation. Journal of Global Positioning Systems, 2003; 2(1): 42 47.
  • 7Sage A P, Husa G A. Adaptive filtering with unknown prior statistics. In: Proc. Joint Automatic Control Conf., 1969: 760--769.
  • 8Lee T S. Theory and application of adaptive fading memory Kalman filters. IEEE Trans. Circuits and Systems, 1988; 35(4): 474- 477.
  • 9Almagbile A, Wang J, Ding W. Evaluating the performances of adaptive Kalman filter methods in CPS/INS integration. Journal of Global Positioning Systems, 2010; 9(1): 33-40.
  • 10Hide C, Moore T, Smith M. Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. In: Proc. Position Location and Navigation Symp., PLANS 2004: 227--233.

引证文献2

二级引证文献17

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部