Multi-Sensor Data Fusion with MATLAB by Jitendra R. Raol

By Jitendra R. Raol

Utilizing MATLAB® examples anywhere attainable, Multi-Sensor information Fusion with MATLAB explores the 3 degrees of multi-sensor info fusion (MSDF): kinematic-level fusion, together with the speculation of DF; fuzzy good judgment and choice fusion; and pixel- and feature-level picture fusion. The authors elucidate DF concepts, algorithms, and function review commonly for aerospace purposes, even if the tools is usually utilized to structures in different components, akin to biomedicine, army security, and environmental engineering. After proposing numerous worthy thoughts and algorithms for DF and monitoring functionality, the booklet evaluates DF algorithms, software program, and platforms. It subsequent covers fuzzy good judgment, fuzzy units and their houses, fuzzy good judgment operators, fuzzy propositions/rule-based platforms, an inference engine, and defuzzification tools. It develops a brand new MATLAB graphical consumer interface for comparing fuzzy implication services, ahead of utilizing fuzzy common sense to estimate the unknown states of a dynamic method by way of processing sensor facts. The publication then employs crucial part research, spatial frequency, and wavelet-based photograph fusion algorithms for the fusion of snapshot information from sensors. It additionally offers techniques for combing tracks acquired from imaging sensor and ground-based radar. the ultimate chapters speak about how DF is utilized to cellular clever independent platforms and clever tracking structures. Fusing sensors’ info can result in various advantages in a system’s functionality. via real-world examples and the evaluate of algorithmic effects, this distinct e-book presents an realizing of MSDF ideas and techniques from a realistic perspective. pick out MATLAB courses can be found for obtain on www.crcpress.com

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Image segmentation and clustering techniques are employed to detect the position of the target in the background image. Data association techniques such as nearest neighbor Kalman filters (NNKFs) and PDAF are used to track the target in the presence of clutter. Chapter 11 presents procedures for combing tracks obtained from imaging sensor and ground-based radar, as well as some methods and algorithms and their performance evaluation studies. Chapters 12 and 13 of Part IV briefly discuss some DF aspects as applicable to other systems; however, the importance of the field need not be underestimated.

When building an MSDF system, the following aspects pertaining to an actual application are of great importance: (1) use of optimal techniques and numerically stable and reliable algorithms for estimation, prediction, and image processing; (2) choice of data fusion architectures such as sensor nodes and the decision-making unit’s connectivity and data transmission aspects; (3) accuracy that can be realistically achieved by a data fusion process or system and conditions under which data fusion provides improved performance; and (4) keeping track of the data collection environment in a database management system.

Information from a variety of data sources, such as sensors, a priori information, databases, human input (called humint, for human intelligence), and electronic intelligence, is normally required and collected for DF. This could, in an overall sense, be called intelligence-collection assets, regardless of whether a particular sensor is intelligent or not. The idea is that some intelligence could have been used somewhere in the sensor system or NW. The sensor system could be called intelligent if it is supported by mechanisms of artificial intelligence (AI), which utilize artificial neural networks (ANNs) for learning, fuzzy logic (rule-based fuzzy approximate reasoning for logical decision making), and/or evolutionary algorithms for detection, acquisition, or preprocessing of measurements.

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