一、前言在雷达跟踪机动目标的数据处理中,普遍地采用了卡尔曼滤波方法。但在某些实时控制系统中,由于计算机的运算速度和内存容量有一定限制,使用卡尔曼滤波方法会遇到困难。尤其在高数据率的情况下更是这样。采用各种常增益α-β-γ滤波方法,可以减少运算量,但同时也降低了滤波性能。人们希望得到既能减少实时运算量,又能保持较好滤波性能的那种方法。对此,有不少文章进行了讨论。本文提出的分 I. Introduction In the data processing of radar tracking maneuvering targets, the Kalman filter method is widely used. However, in some real-time control systems, using the Kalman filter method may encounter difficulties due to limitations on the computing speed and memory capacity of the computer. This is especially true in the case of high data rates. Using a variety of constant-gain α-β-γ filtering methods can reduce the computational complexity, but also reduce the filtering performance. People want ways to reduce both real-time computation and better filtering performance. In this regard, there are many articles were discussed. This article presents the points