基于SPRT检验的并行递推次优Sage滤波器

提出了一种新的并行递推次优Sage滤波算法，在新方法中，设计了一种附加伴随滤波器的并行滤波结构，消除了结果常值偏差，并引入SPRT检验方法，通过检验模型的噪声统计是否发生了扰动，达到对噪声统计调整进行控制的目的，使得滤波器可以有效跟踪时变噪声，并减少了计算量。

The Parallel Recursive Sub-optimal Sage Filter Based on SPRT

Sage filter can simultaneously estimate the expectation values and the corresponding variance matrices of the unknown system and measurement noises. But in practice,we find that there are often constant biases in the results of the classic sub_optimal Sage filter,caused by the inaccurate estimation of the noise expectations.So a novel structure of two parallel filters that proceed simultaneously is presented in this paper.This new structure can not only eliminate the result biases of the classic sub_optimal Sage filter but also provide a more improved accuracy. In addition,SPRT method is used to control the adjustment of the noise statistics,so as to ameliorate the tracking performance of time_variant noises,however,the classic Sage filter does not suit the dynamic system of time_variant noises.Furthermore,the calculation burden is released and the numerical stability is improved by applying SPRT. Then a completely new parallel sub_optimal Sage adaptive filter is provided in this paper.The specific characters of the new filter can be summarized as follows: 1) There are always constant biases in the results of classic sub_optimal Sage adaptive filter,so a parallel filter structure of adding a concomitant filter is designed to eliminate these constant biases; 2) SPRT testing method is applied to the above parallel structure to test whether the statistical properties of the model noises are disturbed or not,so as to control the adjustment of the statistics.Then the filter's performance of tracking time_variant noises is significantly improved and the calculation burden is also released. In a word,the new adaptive Sage filter attempts to innovate the standard sub_optimal Sage filter,and make it practical.