Multi-sensor distributed detection and estimation fusion
Xiaojing Shen · Scientia Sinica Mathematica · 2014
With the development of the science and industry, there are many applied mathematics problems emerging from information science fields. The studies on these problems broaden and accelerate the study of pure mathematical theory. On the other hand, the progresses of pure mathematical theories and their innovation applications in information science contribute to the rapid development of industrial areas. The mutual penetration between mathematics and other subjects has become one of the main features of applied mathematics. For example, in the International Congress of Mathematicians (2010), there are two plenary lectures about the control and the image science respectively, seven invited lectures about Mathematical Aspects of Computer Science and eight invited lectures about Mathematics in Science and Technology. The multi-source information fusion is an important interdisciplinary research direction of mathematics and information science. In our thesis, we focus on two classes of basic problems: multi-sensor distributed detection fusion and multi-sensor distributed estimation fusion. The main innovations include: (1) For dependent measurements and the general multi-sensor distributed detection fusion systems, we derived an efficient algorithm to simultaneously search for optimal sensor rules and an optimal fusion rule. (2) For multi-sensor distributed estimation fusion systems with out-of-sequence measurements, error measurements and asynchronous measurements, we derived a unified estimation fusion algorithm which updates the whole trajectory and is optimal. (3) For uncertain dynamic systems with biases, we presented the criterion of minimizing Euclidean error and obtained an efficient minimized-Euclidean-error algorithm to estimate states based on multi-sensor and multi-algorithm fusion.