Data Fusion Mathematics: Theory and Practice

Fills the Existing Gap of Mathematics for Data Fusion

Data fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants. Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process.

Focuses on Mathematical Tools That Use Data Fusion

This text explores the use of statistical/probabilistic signal/image processing, filtering, component analysis, image algebra, decision making, and neuro-FL–GA paradigms in studying, developing and validating data fusion processes (DFP). It covers major mathematical expressions, and formulae and equations as well as, where feasible, their derivations. It also discusses SDF concepts, DF models and architectures, aspects and methods of type 1 and 2 fuzzy logics, and related practical applications. In addition, the author covers soft computing paradigms that are finding increasing applications in multisensory DF approaches and applications.

This book:

• Explores the use of interval type 2 fuzzy logic and ANFIS in DF
• Covers the mathematical treatment of many types of filtering algorithms, target-tracking methods, and kinematic DF methods
• Presents single and multi-sensor tracking and fusion mathematics
• Considers specific DF architectures in the context of decentralized systems
• Discusses information filtering, Bayesian approaches, several DF rules, image algebra and image fusion, decision fusion, and wireless sensor network (WSN) multimodality fusion

Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance. This text is geared toward researchers, scientists, teachers and practicing engineers interested and working in the multisensor data fusion area.

填補數據融合數學的現有空白

數據融合（DF）將來自各種來源的大量信息進行算法、邏輯和必要時的智能融合，並使用人工智能（AI）。也被稱為傳感器數據融合（SDF），DF融合系統是各種應用的重要組成部分，包括車輛監控、航空航天系統、大型結構和大型工業自動化廠。《數據融合數學：理論與實踐》提供了對數據融合的全面概述，並提供了與DF直接相關的基本數學的適當和充分的理解。所涵蓋的材料可用於評估任何設計和開發的DF系統的性能。它試圖回答統一的數據融合數學是否可以從各種不同的數學概念中演化出來，並強調可以增加數據融合過程可信度的數學。

專注於使用數據融合的數學工具

本書探討了在研究、開發和驗證數據融合過程（DFP）中使用統計/概率信號/圖像處理、濾波、組件分析、圖像代數、決策製定和神經模糊遺傳算法範式的應用。它涵蓋了主要的數學表達式、公式和方程，以及在可行的情況下它們的推導。它還討論了SDF概念、DF模型和架構、第1型和第2型模糊邏輯的方面和方法，以及相關的實際應用。此外，作者還介紹了在多感測器DF方法和應用中越來越多應用的軟計算範式。

本書包括以下內容：

- 探討了在DF中使用區間型2模糊邏輯和ANFIS的應用
- 詳細介紹了多種類型的濾波算法、目標跟踪方法和運動學DF方法的數學處理
- 提供了單一和多感測器跟踪和融合的數學
- 考慮了分散式系統背景下的特定DF架構
- 討論了信息濾波、貝葉斯方法、多種DF規則、圖像代數和圖像融合、決策融合以及無線感測器網絡（WSN）多模態融合

《數據融合數學：理論與實踐》將幫助您整合DF數學，實現更高水平的融合活動和性能清晰度。本書適用於對多感測器數據融合領域感興趣並從事相關研究、科學家、教師和實踐工程師。