多传感器融合技术.doc

多传感器信息融合原理及其应用摘要: 随着科学技术的发展, 传感器得到了广泛的应用。多个传感器的应用可以弥补各自的不足,这就导致了多传感器信息融合技术的产生。在 1960 年,卡尔曼发表了他的著名的文章,该论文描述了对离散数据线性滤波器问题的迭代解。从此, 由于在数据计算方法的优势, 卡尔曼滤波器成了研究和应用的主题, 特别是在自动或辅助导航领域。卡尔曼滤波器是一系列数学方程式, 通过最小化均方误差, 它们为过程的状态估计提供了有效的计算(迭代)方法。该滤波器在许多方面具有强大的功能: 支持过去、现在和未来的估计,并且在建模的系统的精确特性未知的情况下依然可行。该文章对卡尔曼滤波器做了应用性的介绍,然后结合实际应用给予了阐述。关键字: 传感器,信息融合,滤波器,应用,发展 Multi-sensor information fusion theory and its application Abstract : As the development of science and technology, sensors have been widely used. Multi-sensors have the advantage of cover ing the shortage , which stimulated the development of the technology of multi-sensors information fusion. In 1960, . Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in puting, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter isa set of mathematical equations that provides an putational (recursive) means to estimate the state ofa process, ina way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is purpose of this paper is to provide a practical introduction to the principal of multi-data fusion,and have a introduction of the widely used kalman filter then illustrate it with a practical application. Keywords: sensors, information fusion, filter, application, development 1 、引言信息融合或信息融合技术也称为多传感器融合技术,作为一种多源信息协调处理技术术语,在不同的问题领域,其实现方法、步骤和增益优化准则都不同。本文介绍了多传感器信息融合的基本原理,并结合具体应用实例对常用的卡尔曼滤波器进行了讨论[1] 。 2 、信息融合的基本概念多传感器信息融合实际上是对人脑综合处理复杂问题的一种功能模拟。在多传感器系统中,各种传感器提供的信息可能具有不同的特征:时变的或者是不变的,实时的或者非实时的,快变的或者缓慢的,模糊的或者不确定的,精确的或者不完整的,可靠的或者非可靠的,相互支持的或互补的,也可能是相互矛盾或冲突的。多传感器信息融合的基本原理就像人脑综合处理信息的过程一样,它充分的利用多个传感器资源,通过对各种传感器及其观测信息的合理支配与使用, 将各个传感器在空间和时间上的互补与冗余信息依据某种优化准则组合起来,