SIA OpenIR  > 水下机器人研究室
自主水下机器人被动目标跟踪及轨迹优化方法研究
其他题名Research On AUV Bearings-only target tracking and optimization of trajectory
王艳艳1,2
导师封锡盛 ; 刘开周
分类号TP242
关键词自主水下机器人 被动目标跟踪 非线性滤波 数据关联算法 轨迹优化
索取号TP242/W38/2016
页数105页
学位专业模式识别与智能系统
学位名称博士
2016-12-03
学位授予单位中国科学院沈阳自动化研究所
学位授予地点沈阳
作者部门水下机器人研究室
摘要随着科技的发展,自主水下机器人技术越来越成熟,智能水平和作业能力不断提高,使得其应用范围越来越广泛。自主水下机器人的灵活性和隐蔽性的特点使得它更适合完成水下动态目标的跟踪任务。由于自主水下机器人长度有限,通过搭载舷侧阵被动声纳,获取目标航行时声源的固有频谱特性来得到目标的方位,这种仅能获得的目标方位信息的跟踪方式称为被动目标跟踪。被动目标跟踪系统其基本问题是用测量的方位求解目标的位置、速度等运动参数,其特点是非线性以及系统的可观测程度依赖于观测平台的机动轨迹。特别是自主水下机器人与自主交汇问题的研究一直是既困难又富有挑战性的课题。为此,本文在总结和分析国内外现有研究工作的基础上,针对上述问题,对自主水下机器人被动目标跟踪及轨迹优化方法进行了研究。1) 分析了自主水下机器人被动目标跟踪的研究背景和意义,概述了被动目标跟踪可观测性、目标运动分析算法、数据关联算法和观测平台机动策略的国内外研究现状,在此基础上提出自主水下机器人被动目标跟踪亟需解决的问题。2) 根据自主水下机器人被动目标跟踪的特点,选择合理的坐标系和目标运动模型,建立自主水下机器人被动跟踪匀速直线运动目标的数学模型,深入研究了被动目标跟踪系统中静止观测平台和机动观测平台对匀速直线运动目标的可观测性条件。3) 针对传感器受限引起的初始距离误差较大和复杂的海洋环境下的系统噪声统计不精确问题,利用强跟踪滤波器原理建立基于强跟踪滤波算法,该方法具有较强的关于模型参数失配的鲁棒性,较低的关于系统噪声及初始状态统计特性的敏感性等优点,为了提高滤波算法的稳定性、实时性和收敛性,采用改进的CKF算法,即SRCKF算法,对SRCKF中的每个容积点采用STF进行更新,设计滤波增益以抑制噪声对系统状态估计的影响,使系统状态估计快速收敛。仿真实验对比了几种滤波算法在不同初始状态及不同噪声环境下的性能,结果表明基于强跟踪平方根容积卡尔曼滤波的BOTMA算法具有更好的鲁棒性和快速收敛性。4) 针对杂波环境下的自主水下机器人被动目标跟踪的实时性问题,引入了最大熵模糊聚类方法,建立快速实时数据关联的方法:最大熵模糊聚类概率数据关联滤波器(MEFC-PDAF)和最大熵模糊联合概率数据关联滤波器(MEFC-JPDAF)。为了提高被动目标跟踪的实时性,利用模糊聚类隶属度代替目标关联概率的权重,并根据关联事件准则对确认矩阵拆分,有效剔除了无效观测,提高算法效率。仿真数据和外场试验数据结果表明基于最大熵模糊聚类的被动目标跟踪方法能够快速准确的对数据进行分类,实现杂波环境下单目标及多目标的实时跟踪,特别是在多目标距离较近场景下,MEFC-JPDAF算法能够快速准确的估计出不同目标的运动状态,并有效抑制杂波干扰。利用轨迹优化的设计方法对被动目标跟踪系统中观测平台轨迹求解,并详细推导了最大化FIM矩阵行列和最小化距离估计误差准则,给出了两种性能指标下的观测平台最优机动策略。针对自主水下机器人机动能力和传感器探测范围受限等约束问题,提出了基于多约束的自主水下机器人轨迹优化方法,建立了包含自主水下机器人机动能力,传感器探测范围等多种约束条件下的轨迹优化模型,构建了基于提高定位精度和目标交汇概率的目标函数,并通过仿真验证了该方法的合理性。仿真结果表明使用多约束轨迹优化方法,实时规划自主水下机器人机动方式,能够实现自主水下机器人与目标快速交汇任务,证明了算法的可行性和有效性。
其他摘要With the development of ? science and technology, AUV’s intelligent level and working ability are ? constantly advance, that make its application field more and more widely. Because ? of AUV's flexibility and imperceptibility, we use AUV to track motion target under ? water, such as tracking the life habit of marine organism and controlling the ? coastal man-made noise. Usually, AUV’s length is limited, so install array ? passive sonar in the carrying side, the passive sonar only can obtain the ? target’s azimuth, so it usually named as bearings-only target tracking(BOT). The ? basic problem of BOT is how to use azimuth information completed target ? motion analysis(TMA). TMA theory is still not enough ? perfect and TMA results cannot satisfy need in pracrice. Especially the AUV ? autonomous intersection task research is always a problem which is difficult ? and challenging. In this thesis, the summary and analysis on the basis of ? existing research at home and abroad, aiming at these problems, the AUV ? passive target tracking and trajectory planning method is studied. The main contents of this ? thesis are as follows: 1) Firstly, the research status ? of autonomous underwater vehicle bearings-only target tracking is introduced, ? and the application background and significance of BOT are analyzed. ? Summarized the observability of BOT system、target motion analysis algorithm、data association algorithm and observer’s maneuver strategy, then put ? forward the problem which need to solved in a hurry when we use AUV as an ? observer in BOT system. 2) Based on the characteristics ? of autonomous underwater vehicle bearings-only target tracking, selecting a ? reasonable coordinate system and target model, establishing AUV bearings-only ? target tracking model assuming target motion in a straight line with constant ? velocity. Further study of observability in BOT system at two different ? situations, the observer’s trajectory is static or motor when the target ? motion in a straight line with constant velocity. 3) BOT system is a typical ? nonlinear system and the initial state is usually uncertainty that seriously ? decreases the estimation accuracy of system state. To deal with this problem, ? this thesis proposes a Bearings Only Target motion analysis algorithm Based ? on Strong Tracking SRCKF that combines the strong tracking filter with ? robustness and the SRCKF with the advantages of high accuracy and easy ? implementation. Each cubature point of SRCKF is updated by STF, the effects ? of noises on system state estimation are suppressed by optimizing filter ? gains, and the system state estimation converges to real values quickly. ? Simulation experiment compared several algorithms of bearing target tracking ? algorithm under different initial condition and noise environment. The ? performance of the experimental results show that this strong tracking SRCKF ? filter has better robustness and faster convergence. 4 For real time target bearings ? –only tracking with AUV in clutter enviroment, a category novel fast data ? association method is proposed, maximum entropy fuzzy cluster method is ? introduced, gets two metods: maximum entropy fuzzy cluster probabilistic data ? association filter (MEFC-PDAF) and maximum entropy fuzzy joint probabilistic ? data association filter (MEFC-JPDAF). In order to improve the real-time ? performance of target tracking, using the fuzzy cluster membership degree ? instead of the target association probability weighting, and according to the ? associated event rule to confirm the matrix resolution, effectively ? eliminating the invalid observation, improve the efficiency of algorithm. The ? simulation data and field data results shows that using MEF method can ? classify datas quickly and accurately, realized the goal of single taget ? motion analysis and multi-target motion analysis in clutter environment. ? Especially when two targets are very near to each other, MEFC-JPDAF algorithm ? can accurately estimate each target’s motion state, and effectively restrain the ? noise interference. Using optimal ? theory calculate the AUV’s optimization trajectory, and getting optimal ? maneuver strategy under two indexes, respectively maximize the FIM matrix and ? minimizing the distance estimation error. Considered all strong constiains, ? such as AUV’s mobility and the sensor’s detection range, proposed a new ? optimization method which establish Trajectory optimization model including ? AUV mobility and sensor detection range and other constraints. Building the ? objective fuction which based on improving the positioning accuracy and ? meetiong probability. Simulation shows that along the planned trajectory, AUV ? can meet with the target at the same position.
语种中文
产权排序1
文献类型学位论文
条目标识符http://ir.sia.cn/handle/173321/19453
专题水下机器人研究室
作者单位1.中国科学院沈阳自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
王艳艳. 自主水下机器人被动目标跟踪及轨迹优化方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2016.
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