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水下被动目标跟踪中的数据关联与滤波方法研究
Alternative TitleResearch on data association and filtering in underwater passive target tracking
丁一
Department海洋信息技术装备中心
Thesis Advisor张瑶
Keyword水下被动目标跟踪 状态估计 卡尔曼滤波算法 多目标数据关联
Pages87页
Degree Discipline控制理论与控制工程
Degree Name硕士
2020-05-26
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract水下目标的检测与跟踪技术是探索海洋的一种重要技术手段。其中,以自主水下机器人(Autonomous Underwater Vehicle,以下简称AUV)为观测载体的被动目标跟踪以布放便利、灵活性高、成本低等优点,广泛应用于水面战舰跟踪、水下目标监控等各种军用和民用领域。由于被动声呐仅通过目标的辐射信号对其进行探测,所以量测结果中仅有一维的目标方位信息,具有一定隐蔽性,但对目标的参数估计难度较大,需要选取合适的滤波算法。且水下环境里杂波密度高,噪声干扰严重,对目标的跟踪过程还需要寻求有效的数据关联算法来保持对目标航迹的跟踪。因此,需要对被动目标跟踪领域中的滤波算法和数据关联算法展开更加深入的研究。本论文以某重点部署项目为依托,对水下复杂环境下的被动目标跟踪中涉及的数据关联与跟踪滤波算法开展深入的研究与分析,提出了一系列提升跟踪能力的算法,并通过AUV平台搭载被动声呐载荷开展目标跟踪技术湖/海上试验验证。论文的主要工作内容如下:对多目标跟踪的理论基础进行简要介绍,研究了适用于目标平稳运动的常速度模型、常加速度模型等,以及适用于目标机动运动下的“当前”模型。对二维平面内的系统可观测性问题进行了分析和证明,同时简要介绍了各类关联跟踪门的原理。开展适用于被动跟踪的非线性滤波算法对比分析。在理想无杂波的水下环境中,对传统的扩展卡尔曼滤波、无迹卡尔曼滤波、交互式多模型算法,进行了深入的理论研究,在不同的场景下进行了仿真性能对比分析,为后续的工程应用提供了理论基础。对基于声呐设备特性的跟踪滤波算法进行设计与改进。针对水下复杂的环境,选取合适的滤波算法,结合湖/海上试验数据,对滤波算法提出了改进,包括:基于多模型初值并行滤波的目标参数初始化滤波方法、自适应实时误差估计方法、基于BP神经网络辅助校正的卡尔曼滤波等方法,以上方法降低了初值选取敏感度,提升了系统模型的匹配度以及辨识目标机动的能力,使跟踪滤波算法与实际声呐系统及应用场景紧密结合,具有一定的创新性。数据关联的作用是对落入关联区域内的多个量测点作判断与分析,剔除杂波,形成稳定航迹,从而提升目标跟踪的准确性。论文在对基本的数据关联算法(最近邻、概率数据关联、联合概率数据关联)进行了分析的基础上,提出了一种基于模糊聚类的数据关联算法,将该算法与传统数据关联算法通过仿真与外场试验数据进行对比,验证其优越性。
Other AbstractUnderwater target detection and tracking technology is an important technical means to explore the ocean. Passive target tracking with autonomous underwater vehicle as observation carrier is widely used in various military and civil fields, such as surface battleship tracking, underwater target monitoring and so on, because of its advantages of convenient deployment, high flexibility and low cost. And passive sonar only detects the target's radiation signal, the measurement results only have one-dimensional target orientation information, which has a certain concealment. However, it is difficult to estimate the state of the target. And in the underwater environment, the clutter density is high and the noise interference is serious. Therefore, a more suitable data correlation algorithm should be sought to solve this problem. It is very important to study the filtering algorithm and data association algorithm in the field of passive target tracking. Based on a key deployment project, this paper conducts in-depth research and analysis on data association and tracking filtering algorithm of passive target tracking in underwater complex environment, using the existing heavy AUV platform carrying passive sonar load to carry out the lake / sea test verification of target tracking technology. The main content of the paper is as follows: In this paper, the theoretical basis of multi-target tracking is briefly introduced, and the CV model, CA model and CT model which are suitable for the steady motion of the target, as well as the "current" model which is suitable for the maneuvering motion of the target are studied. The observability of the system in two-dimensional plane is analyzed and proved. At the same time, the principle of all kinds of associated tracking gates is briefly introduced. The nonlinear filtering algorithm for passive tracking is compared and analyzed. Under ideal conditions, the traditional extended Kalman filter, unscented Kalman filter and interactive multi-model algorithm are studied, and the simulation performance is compared and analyzed in different scenarios, which provides a theoretical basis for subsequent engineering applications. The tracking filtering algorithm based on the characteristics of sonar equipment is designed and improved. Selecting the appropriate filtering algorithm for the complex underwater environment and combining with lake / sea test data, the filtering algorithm is improved, including initial parameter filtering method based on multi-model initial value parallel filtering, adaptive real-time error estimation method, BP neural network assisted Kalman filter method and so on. The above method reduces the initial value selection sensitivity, improves the matching degree of the system model and the ability to identify the target maneuver, and makes the tracking filter algorithm closely integrated with the actual sonar system and application scenarios, which is innovative. The function of data association is to judge and analyze multiple measurement points falling into the associated area, to eliminate clutter and form a stable track, so as to improve the accuracy of target tracking. Based on the analysis of basic data association algorithms (nearest neighbor algorithm, probabilistic data association algorithm, joint probabilistic data association algorithm ), the paper proposes a data association algorithm based on fuzzy clustering (FCM). The algorithm is compared with the traditional data correlation algorithm through simulation and test data to verify its superiority.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/27130
Collection海洋信息技术装备中心
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
丁一. 水下被动目标跟踪中的数据关联与滤波方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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