9 Best Books on Sensor and Data Fusion

“Multi-Sensor Data Fusion: An Introduction” Book Review: This book serves as an introductory guide to the theories and techniques of multi-sensor data fusion, covering both fundamental and advanced concepts. It commences with a comprehensive overview of the fundamental tools of linear algebra, calculus, and basic probability theory. Targeted at advanced undergraduate and first-year graduate students in the field of electrical engineering and computer science, the book is also a valuable resource for researchers and professional engineers seeking to expand their knowledge in the field.

“Tracking and Sensor Data Fusion (Mathematical Engineering)” Book Review: This book serves as an introduction to sensor data fusion, encompassing both information technology and the field of engineering science and informatics. Within its pages, the author shares their specific contributions to the development of this field, as well as delving into topics such as the internet of sensors. Additionally, the book provides insight into the potential for further research, engineering advancements, and product development in this field.

“Sensor Data Fusion Systems” Book Review: The book offers a methodology to address the issue of sensor data fusion. It presents a resolution for merging various types of sensory data, including those of the same kind. Moreover, it suggests techniques for pre-processing sensory data to ensure a particular level of input quality before the fusion process takes place. The book features a range of real-life scenarios, from security to battlefield analysis and other fields, where decision-making and fusion techniques are essential.

“Multi-Sensor Data Fusion with MATLAB®” Book Review: This book contains three tiers of multi-sensor data fusion (MSDF), including kinematic-level fusion, fuzzy logic and decision fusion, and pixel and feature level image fusion. It provides an overview of DF strategies, algorithms, and performance evaluation primarily for aerospace applications. The book delves into fuzzy logic, its properties, fuzzy sets, fuzzy logic operators, fuzzy propositions/rule-based systems, and defuzzification methods. Moreover, it features a new MATLAB graphical user interface for assessing fuzzy implication functions. It also covers image fusion algorithms based on principal component analysis, spatial frequency, and wavelets for fusing sensor-generated image data. The book presents techniques for integrating tracks obtained from imaging sensors and ground-based radar. Finally, the last chapters address how DF is employed in mobile intelligent autonomous systems and intelligent monitoring systems.

“Sensor and Data Fusion: A Tool for Information Assessment and Decision Making (Spie Press Monograph)” Book Review: This book demonstrates the advantages of sensor fusion by examining the traits of infrared, microwave, and millimeter-wave sensors, as well as the impact of atmospheric conditions on their performance. The book explores various subjects such as the use of multiple-sensor systems for applications like target identification, the phenomena of atmospheric signature generation, and techniques for merging data from multiple sensors to create tracking data fusion architectures. Furthermore, the book addresses the data fusion challenges related to tracking systems that utilize multiple radars.

“Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks (Lecture Notes in Electrical Engineering)” Book Review: This book presents resource-aware data fusion algorithms that aim to gather and merge data from various sources to make inferences. It enumerates techniques suitable for both centralized and distributed systems that can tackle challenges like sensor failure, technological constraints, and issues related to spatial and temporal coverage. The book evaluates the described algorithms using simulation and experimental results. It outlines methods to address actual problems faced by wireless sensor networks that may interfere with measurements, such as strong fluctuations in pressure, temperature, radiation, and electromagnetic noise. Additionally, it encompasses a case study that implements data fusion algorithms in a remote monitoring framework for sand production in oil pipelines.

“Mathematical Techniques in Multi-sensor Data Fusion (Electronic warfare/radar library)” Book Review: The book offers an overview of multisensor data fusion concepts, including key algorithms, utilization advice, and implementation issues. It presents a variety of mathematical and heuristic techniques for merging data from multiple sources. In addition, the book details the steps involved in implementing a data fusion system, such as selecting appropriate algorithms, determining functional architectures, and identifying requirements for supporting software. The text also covers man-machine interface requirements and database considerations.

“Passive Satellite Pose Estimation Based on PMD/CCD Sensor Data Fusion” Book Review: This book is really important for future missions where things are fixed or built in space. It talks about a special way to figure out where things are using a cool sensor called PMD. The book also talks about how different types of data can be combined together and why PMD sensors are super helpful for tasks like meeting up and connecting things in space.

“Data Fusion: Concepts and Ideas” Book Review: This book offers an overview of multisensor data fusion concepts and ideas, supported by a multitude of real-world examples from a variety of fields, and featuring an extensive list of contemporary references. The book adopts a consistent statistical framework to unify the various processes explored within, and covers a range of topics that present distinct challenges for electrical engineers and computer scientists alike, including neural networks, signal processing, statistical estimation, tracking algorithms, computer vision, and control theory.