Data Fusion Mathematics: Theory and Practice(Hardcover)
暫譯: 數據融合數學:理論與實踐(精裝版)
Jitendra R. Raol
- 出版商: CRC
- 出版日期: 2015-08-27
- 售價: $8,420
- 貴賓價: 9.5 折 $7,999
- 語言: 英文
- 頁數: 600
- 裝訂: Hardcover
- ISBN: 1498720978
- ISBN-13: 9781498720977
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其他版本:
Data Fusion Mathematics: Theory and Practice
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商品描述
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.
商品描述(中文翻譯)
填補數據融合的數學現有空白
數據融合(Data Fusion, DF)將來自各種來源的大量信息進行結合,並通過算法、邏輯,必要時使用人工智慧(Artificial Intelligence, AI)進行智能融合。數據融合也被稱為傳感器數據融合(Sensor Data Fusion, SDF),DF 融合系統是用於各種應用的重要組件,包括監控車輛、航空系統、大型結構和大型工業自動化工廠。《數據融合數學:理論與實踐》提供了數據融合的全面概述,並對與 DF 直接相關的基本數學進行了適當和充分的理解。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變而來,並強調可以為數據融合過程增添可信度的數學。
專注於使用數據融合的數學工具
本書探討了統計/概率信號/圖像處理、過濾、成分分析、圖像代數、決策制定和神經模糊邏輯–遺傳算法(neuro-FL–GA)範式在研究、開發和驗證數據融合過程(Data Fusion Processes, DFP)中的應用。它涵蓋了主要的數學表達式、公式和方程式,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、類型 1 和 2 模糊邏輯的各個方面和方法,以及相關的實際應用。此外,作者還涵蓋了在多感測器 DF 方法和應用中越來越多應用的軟計算範式。
本書:
- 探討了在 DF 中使用區間類型 2 模糊邏輯和自適應神經模糊推理系統(ANFIS)
- 涵蓋了多種過濾算法、目標追蹤方法和運動學 DF 方法的數學處理
- 提出了單一和多傳感器追蹤及融合的數學
- 考慮了去中心化系統背景下的特定 DF 架構
- 討論了信息過濾、貝葉斯方法、幾種 DF 規則、圖像代數和圖像融合、決策融合以及無線傳感器網絡(Wireless Sensor Network, WSN)多模態融合
《數據融合數學:理論與實踐》整合了數據融合中的概念、過程、方法和途徑,幫助您整合 DF 數學並實現更高水平的融合活動和性能清晰度。本書面向對多感測器數據融合領域感興趣並從事相關工作的研究人員、科學家、教師和實踐工程師。