基于前向神经网络的非线性平滑滤波器设计研究

文章阐述了基于前向神经网络的非线性平滑滤波器的设计问题，分析了上述平滑滤波器的存在性及其滤波误差方差的组成，给出了有限观测序列下网络学习的性能指标，由此提出了一种次优网络学习算法，它具有很好的抑制白噪声能力。     

相关信号DOA估计的超分辨算法及其改进

常见的超分辨算法,如MUSIC算法、Capon算法等,只能对非相关信号到达角进行估计,无法分辨两个相关回波信号。介绍的d-MUSIC算法解决了这个问题,文中给出了这种算法的适用范围和空间谱特性,提出增加泰勒展开阶数的方法来提高算法的测角精度。     

Dual smoothing ionospheric gradient monitoring algorithm for dual-frequency BDS GBAS

In this study, a Dual Smoothing Ionospheric Gradient Monitor Algorithm (DSIGMA) was developed for Code-Carrier Divergence (CCD) faults of dual-frequency Ground-Based Augmentation Systems (GBAS) based on the BeiDou Navigation Satellite System (BDS). Divergence-Free (DF) combinations of the signals were used to form test statistics for a dual-frequency DSIGMA. First, the single-frequency DSIGMA was reviewed, which supports the GBAS approach service type D (GAST-D) for protection against the effect of large ionospheric gradients. The single-frequency DSIGMA was used to create a novel input scheme for the dual-frequency DSIGMA by introducing DF combinations. The steady states of the test statistics were also analysed. The monitors were characterized using BDS measurement data, whereby standard deviations of 0.0432 and 0.0639 m for the proposed two test statistics were used to calculate the monitor threshold. An extensive simulation was designed to assess the monitor performance by comparing the Probability of Missed Detection (PMD) according to the differential error with the range domain PMD limits under different fault modes. The results showed that the proposed algorithm has a higher integrity performance than the single-frequency monitor. The minimum detectable divergence with the same missed probability is less than 50% that of GAST-D.     

高速采样测量数据处理方法研究

探讨在新一代高精度测速体制下，外测数据原有处理方法应用到高采样率数据时的缺陷，提出将原始采样数据分段、分层进行多次处理，结合二步最小二乘、等距样条拟舍和区间长度自适应的时间多项式中心平滑技术，可降低每次处理的数据量，并逐步滤除周期噪声与色噪声，提高了处理结果的精度。     

惯性测量组合误差系数的时变分析与建模

为了提高惯性测量组合误差系数的标定精度,对惯性测量组合一次通电条件下多次测量的误差系数通过平滑滤波和最小二乘拟合等方法进行分析处理,建立了时漂误差模型。结合某惯性测量组合的相关计算结果,证明了时漂系数更能反映误差系数一次通电条件下的变化规律。     

预测-校正EKF算法在自主导航中的应用

为提高航天器自主导航系统非线性估计精度,通过分析EKF线性化方法的缺 点,基于数学分析理论研究了非线性函数线性化展开点以及雅可比矩阵取值点不同对线性化 逼近程度的影响,然后对传统EKF算法的线性化展开方式进行了改进,提出了一种基于中值 定理和平滑估计的预测-校正EKF（FR\|EKF）新算法。仿真结果表明,该算法在精度上优于 EKF算法。     

拐点平滑滤波技术研究

提出了滤波算法的一种新的有效点判断原则，即拐点平滑法；介绍了该技术的数学模型及滤波原理；通过仿真计算，探讨了应用该判据时滤波半径、拟合阶次等滤波参数对位置参数滤波结果的影响情况，并进行了实测数据解算。该判据在中心平滑算法的最佳应用条件下，综合考虑了截断误差，提高了飞行器位置参数的滤波精度，尤其适用于加速度剧烈变化的飞行轨迹。     

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.