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水下机器人海洋声场测绘方法研究
Alternative TitleResearch on Mapping of Underwater Acoustic Fields Using Underwater Vehicles
孙洁
Department海洋机器人卓越创新中心
Thesis Advisor张艾群 ; 俞建成
Keyword水下机器人 平台噪声 采样优化 声场重构 导航定位
Pages136页
Degree Discipline机械电子工程
Degree Name博士
2020-05-20
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract本文依托国家自然科学基金面上项目“基于多水下滑翔机的海洋水声信道特征参数测绘研究(61673370)”,重点研究其中涉及到的水下机器人噪声分析、利用水下机器人的可控性与移动性进行优化测量、基于测量数据构建特征参数的空间分布场、以及水下机器人的导航定位四方面的研究内容,并考虑现阶段水声环境观测实验频繁重复开展受限从而影响算法性能验证的问题,构建了基于海洋-声学耦合模式的仿真系统。主要研究工作和结果如下:1. 构建基于海洋-声学耦合模式的模拟实验系统。将已成熟发展的海洋模式温盐场数据输入BELLHOP 3D模型,实现了在模拟真实海洋的环境中从仿真区域选定至声学参数输出的端到端自动化运行,支撑本文后续算法性能验证工作的开展。2. 开展水下机器人噪声特性分析。针对已开发的未搭载水听器的“海鲸2000” 自主水下机器人(AUV)分析其辐射噪声特性为后续声学应用提供参考;针对已开发的搭载水听器的“海翼”水下滑翔机分析其流噪声和机械噪声特性,并提出一种联合卷积滤波阈值方法的自噪声滤除方法,实现平台噪声滤除。3. 基于压缩感知重构方法开展水下机器人优化采样策略研究。将压缩感知算法引入海洋声场重构,针对水下机器人的连续运动特性无法实现空间随机点采样、以及一般梳状采样等路径无法充分采样海洋声场空间变化从而降低压缩感知重构场精度的问题,提出一种结合有限等距性质(Restricted Isometry Property)以及水下机器人连续运动约束的基于遗传算法的测量矩阵优化策略,提高重构场精度。4. 开展克里金-压缩感知海洋声场重构方法研究。针对水下机器人沿轨迹连续采样影响稀疏变换矩阵与测量矩阵相干性的问题,引入随机虚拟样本,提出克里金方法与压缩感知相结合的克里金-压缩感知方法,使感知矩阵尽可能满足有限等距性质。进一步引入加权函数区分虚拟样本和真实样本,提出加权克里金-压缩感知方法,实现更高精度的水下声场重构。5. 开展基于单信标的水下滑翔机导航定位算法研究。水下声场测量与空间位置相对应,为了降低由平台位置估计误差引入的测量扰动需要改进其水下导航定位性能。结合由动力学方程推导的关键运动参数--攻角和漂角建立水下滑翔机的运动模型用于水下位置预测,采用单信标发射声信号的传播时间差乘以水中声速得到的水下滑翔机与信标之间距离差作为声学测量,利用扩展卡尔曼滤波(EKF)算法实现水下滑翔机的动态导航定位。通过引入后续测量,利用RTS平滑算法进一步改善水下滑翔机的导航定位性能。
Other AbstractThis dissertation is supported by the General Program of National Natural Science Foundation of China, titled “Research on mapping of underwater acoustic channels using multiple underwater gliders” under Grant 61673370. The key issues are to understand the noise characteristics of underwater vehicles, to design the optimal sampling path of underwater vehicles, to construct a spatial field from sampled measurements, and to precisely navigate and localize underwater vehicles for a more accurate reconstruction. Considering that underwater acoustic experiments cannot be frequently carried out, a simulation system based on the ocean-acoustic coupled model is constructed. This dissertation mainly includes the following contents. 1. Constructing a simulation system based on the ocean-acoustic coupled model. The temperature and salinity data from a developed ocean model is input into the BELLHOP 3D model, which realizes end-to-end automatic operation from the simulation area selection to the acoustic parameter output in a simulated ocean environment, supporting the algorithm verification in the subsequent research. 2. Noise characteristics analysis of underwater vehicles. We analyze radiated noise characteristics of a developed “Sea Whale 2000” autonomous underwater vehicle (AUV) with no installed hydrophone to provide a reference for potential acoustic applications. For the developed Sea-Wing acoustic underwater glider, self-noise characteristics, including hydrodynamic ?ow noise and mechanical noise, are analyzed. Based on the acquired knowledge, a joint convolution ?ltering and thresholding method is proposed to remove the glider noise from noisy data recorded during sea trials. All glider noise could be removed from the recorded data. 3. Research on an optimized sampling strategy of underwater vehicles based on compressive sensing (CS). CS algorithm is firstly introduced into the acoustic field reconstruction. Then aiming at the problem that general lawnmower trajectories cannot adequately sample the coherent structure of acoustic fields, a genetic algorithm-based measurement matrix optimization method that combines the restricted isometry property (RIP) of CS and the continuous motion constraints of underwater vehicles is proposed to improve the reconstruction accuracy. 4. Research on a kriged compressive sensing (KCS) approach to reconstruct acoustic fields from measurements collected by underwater vehicles. Aiming at the problem that the continuous sampling characteristics of underwater vehicles along trajectories affects the coherence between the sparse transformation matrix and measurement matrix, a KCS approach is established by adding random virtual samples from kriging estimated fields to make the sensing matrix satisfy the RIP as much as possible. A weighting function is then introduced to distinguish between virtual samples and real samples, then a weighted KCS approach is proposed to further improve the reconstruction accuracy. 5. Research on navigation and localization algorithms for underwater gliders. Acoustic field sampling relies on the spatial position of underwater vehicles. To improve the sample position accuracy, the vehicles’ navigation and positioning performance needs to be improved. An extended Kalman Filter (EKF)-based method, combining a motion model of underwater gliders with acoustic measurements from a single beacon, is proposed to estimate the glider positions in a predict-update cycle. Two parameters, the attack and drift angles, calculated based on the coef?cients of hydrodynamic forces are introduced into the glider model. The travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To improve the EKF estimate, the RTS smoothing algorithm is adopted for each gliding cycle by introducing subsequent measurements.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/27163
Collection海洋机器人卓越创新中心
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
孙洁. 水下机器人海洋声场测绘方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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