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近几年来,卡尔曼滤波器～（[1],[2]）已在导弹或飞机的跟踪,以及宇宙飞船的轨道测定等方面得到了广泛的应用。在这些应用中容易碰到的一个问题是常常不能够精确在知道初始条件,噪声模型（过程噪声和观测噪声）,以及系统模型的先验统计量,而这个统计量对于最佳滤波器的设计来说恰恰是非常重要的。 例如,在太空宇宙飞船的轨道测定问题中,观测通常是以多普勒,计数多普勒或距离数据的形式提供的。这些数据易受振荡器的不稳定性、电离层中的骚扰,接收机的噪声以及计数器的量化噪声的影响,它们合在一起构成了观测噪声。
In recent years, Kalman filter ([1], [2]) has been widely used in the tracking of missiles or aircraft and the orbit determination of spacecraft. One of the problems that can be easily encountered in these applications is often not accurately knowing the initial conditions, the noise model (process noise and observation noise), and the a priori statistics of the system model, which are important to the design of the best filter It is precisely very important. For example, in orbit determination of space-based spacecraft, observations are usually provided in the form of Doppler, count-doppler or distance data. These data are susceptible to oscillator instability, ionospheric harassment, receiver noise, and counter quantization noise, which together make up the observed noise.