基于交互式多模型的水下机动目标跟踪

高文娟; 李亚安; 陈 晓; 陈志光

doi:10.11993/j.issn.1673-1948.2015.03.007

基于交互式多模型的水下机动目标跟踪

doi: 10.11993/j.issn.1673-1948.2015.03.007

西北工业大学航海学院, 陕西西安, 710072

基金项目: 国家自然科学基金(51409214)

详细信息

作者简介:
高文娟(1990-), 女, 在读硕士, 主要研究方向为水下目标跟踪.

中图分类号: TJ630.34; TB566
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- 被引次数: 0
出版历程
- 刊出日期: 2015-06-20

Application of IMM to Underwater Maneuver Target Tracking

School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an 710072, China

摘要

摘要: 为了解决水下机动目标跟踪的实时性和可靠性问题, 在交互式多模型(IMM)的框架下对水下机动目标跟踪进行了分析, 建立了目标运动方程和观测方程。交互式多模型滤波算法的选择直接影响到跟踪的精度, 在跟踪滤波方面, 针对交互式多模型滤波过程中观测方程非线性对滤波性能的影响, 分别将扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF) 2种滤波算法与交互式多模型算法相结合。仿真结果表明, 交互式多模型算法与UKF算法结合的滤波精度更高, 能够更有效、可靠地达到跟踪机动目标的目的。
- 水下机动目标 /
- 交互式多模型(IMM) /
- 扩展卡尔曼滤波(EKF) /
- 无迹卡尔曼滤波(UKF)
Abstract: For improving the real-time property and reliability of underwater maneuver target tracking, the interacting multiple model(IMM) algorithm is applied to underwater maneuver target tracking. Based on the IMM algorithm, a target maneuvering model and a measurement model are established. Because the tracking accuracy depends on filtering method, the extended Kalman filter(EKF) and the unscented Kalman filter(UKF) are combined with the IMM algorithm, respectively. Simulation shows that the IMM algorithm combined with UKF can achieve higher filtering accuracy, hence can track maneuver target more effectively and reliably.
- underwater maneuver target /
- interacting multiple model(IMM) /
- extended Kalman filter(EXF) /
- unscented Kalman filter(UKF)

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参考文献(8)

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