Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
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发表时间：
2007-02
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通讯作者：
R. Mahler
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作者：
R. Mahler
这一全面的资源为您提供了对有限集统计（FISST）的深入了解-这是一种最近开发的方法，它将大部分信息融合统一在一个单一的概率（实际上是贝叶斯）范式下。这本书帮助你掌握FISST的概念，技术和算法，这样你就可以使用FISST来解决该领域的现实挑战。您将学习如何建模，融合和处理高度分散的信息源，并检测和跟踪非合作的个人/平台组和传统的非合作目标。你会发现专家系统理论的许多方面都有严格的贝叶斯统一。此外，这本书提出了多源多目标问题的系统积分和微分，为设计严格的新技术提供了一种方法。这本容易理解和详细的书有超过3,000个方程，90个清晰的例子，70个解释性的数字和60个解决方案的练习。
This comprehensive resource provides you with an in-depth understanding of finite-set statistics (FISST) - a recently developed method which unifies much of information fusion under a single probabilistic, in fact Bayesian, paradigm. The book helps you master FISST concepts, techniques, and algorithms, so you can use FISST to address real-world challenges in the field. You learn how to model, fuse, and process highly disparate information sources, and detect and track non-cooperative individual/platform groups and conventional non-cooperative targets. You find a rigorous Bayesian unification for many aspects of expert systems theory. Moreover, the book presents systematic integral and differential calculus for multisource-multitarget problems, providing a methodology for devising rigorous new techniques. This accessible and detailed book is supported with over 3,000 equations, 90 clear examples, 70 explanatory figures, and 60 exercises with solutions.