多传感器数据融合与卡尔曼滤波技术应用例程解析

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资源摘要信息:"***Multi-sensor-data-fusion_传感器融合_传感器_传感器融合_datafusion_多传感器" 在现代信息技术和自动化控制领域,传感器融合技术是一种至关重要的技术,它通过综合利用来自多个传感器的数据来提高系统的感知能力和决策质量。在标题中提到的"Multi-sensor-data-fusion",即多传感器数据融合,是一个涉及数据处理的复杂过程,它包括物理设备层面上多个不同类型传感器的数据集成,以及通过算法对这些数据进行处理和优化,以便得到比单独使用任何单一传感器更准确、更可靠的信息。 描述中提到的“卡尔曼滤波”,是一种广泛应用于传感器数据融合中的算法,特别适用于具有线性动态系统的状态估计问题。卡尔曼滤波器是一种迭代算法,它利用系统的动态模型和观测数据来预测下一个状态,并且不断地用观测数据来校正预测,以实现最优的状态估计。这种方法在航天、航空、机器人导航、信号处理以及自动控制等多个领域有广泛应用。 在多传感器数据融合中,卡尔曼滤波算法可以结合来自不同传感器的数据,例如GPS、加速度计、陀螺仪等,来提供准确的位置、速度和方向信息。例如,在自动驾驶汽车中,融合来自雷达、激光雷达(LiDAR)、摄像头等传感器的数据,可以帮助车辆更好地理解其周围环境,从而做出更为精确的行驶决策。 标签中所列出的"传感器融合"、"传感器"、"datafusion"以及"多传感器融合",均为同一概念的不同表述方式,均指向同一个核心知识点——多传感器数据融合技术。这是一项技术,它依赖于对不同类型的传感器数据进行处理和分析,并将这些数据集成到一个统一的环境中,以提供比单独使用任一传感器都更为全面和准确的信息。 至于压缩包子文件的文件名称列表中提到的"EKF_trace.m",很可能是一个包含卡尔曼滤波算法实现的Matlab脚本文件,用于处理和分析多传感器数据。Matlab是一种广泛用于算法开发、数据分析、工程计算以及数值计算的高性能语言和交互式环境。"trace.mat"可能是一个包含测试数据的Matlab数据文件,用于在"EKF_trace.m"文件中测试和展示卡尔曼滤波器的效果。"***.txt"文件可能是从某个网页上抓取或下载的文本文件,可能包含有关该算法或数据集的描述、说明或源代码,但具体情况需要打开文件进行确认。 总之,从标题、描述和标签中提取的关键知识点包括多传感器数据融合的概念、卡尔曼滤波算法的应用,以及相关的数据处理技术。这些技术的应用广泛,不仅限于特定的行业或领域,而是贯穿于许多需要精确实时数据处理和决策支持的系统中。
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英文,原生pdf格式带目录,2011版。主要内容包含传感器及其校准,数据融合架构及常用算法。 This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction" which was originally published by Springer-Verlag in 2007. The main changes in the new book are: New Material: Apart from one new chapter there are approximately 30 new sections, 50 new examples and 100 new references. At the same time, material which is out-of-date has been eliminated and the remaining text has been rewritten for added clarity. Altogether, the new book is nearly 70 pages longer than the original book. Matlab code: Where appropriate we have given details of Matlab code which may be downloaded from the worldwide web. In a few places, where such code is not readily available, we have included Matlab code in the body of the text. Layout. The layout and typography has been revised. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity with the basic tools of linear algebra, calculus and simple probability is recommended. Although conceptually simple, the study of mult-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often, the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In contrast, in this book the processes are unified by using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident. The book is illustrated with many real-life examples taken from a diverse range of applications and contains an extensive list of modern references.