基于UKF和信息融合的航天器自主导航方法

X射线脉冲星导航利用X射线辐射脉冲到达时间 (Time of Arrival, TOA)作为信息输入,星敏感器导航利用星光角距等作为信息输入,是两种不同机理的天文导航方法。提出一种将脉冲星TOA和星敏感器星光角距测量结合的信息融合天文自主导航方法,设计了一种利用激光光量子模拟脉冲星X射线辐射光子的半物理仿真系统用于算法验证,并基于无迹卡尔曼滤波(Unscented Kalman Filter, UKF)使用真轨道参数做了仿真试验。结果表明,基于UKF的信息融合方法比基于EKF（Extended Kalman Filter）的信息融合方法性能更好,与仅使用脉冲星或星敏感器的导航方法相比,能将位置估计精度分别提高52.7%和43.6%,速度估计精度分别提高82.2%和70.5%。     

高斯粒子滤波器及其在非线性估计中的应用

为了解决非线性、非高斯系统估计问题,讨论了一种新的滤波方法——高斯粒子滤波算法。通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。在符合高斯假设和一定的粒子数的情况下,谈算法可以获得近似最优解。与粒子滤波算法相比,其优点是不需要重采样步骤和不存在粒子退化现象。在滤波精度、运算时间等方面与扩展卡尔曼滤波、Unscented滤波、高斯厄米特滤波及一般的粒子滤波进行了比较分析,仿真结果表明该算法性能优于其他算法。     

非线性估计理论的最新进展

扩展Kalman滤波(EKF)是应用最广的非线性估计方法，然而它存在实现性差、计算量大、估计精度低等缺陷，这些问题起源于。EKF采用了Taylor展开近似。在阐明非线性估计的本质，剖析EKF等传统方法的特点及缺陷的基础上，从非线性估计革新的两条发展思路——非Taylor展开的线性变换及非线性变换出发，分别对插值滤波、Unscented滤波、粒子滤波和神经网络滤波这四种近年来最具特色的新方法进行介绍和评述。通过分析这些方法的工作原理、性能特点、必要性和可行性，将非线性估计最新进展的思想传承、本质内涵、地位与作用予以展现，指出各方法的现存问题、发展潜力和最具可实现性的发展方向。同时强调了各种算法的选取须根据具体应用场合和条件，在主要性能指标之间综合权衡。     

Interlaced optimal-REQUEST and unscented Kalman filtering for attitude determination

 Aimed at low accuracy of attitude determination because of using low-cost components which may result in non-linearity in integrated attitude determination systems, a novel attitude determination algorithm using vector observations and gyro measurements is presented. The various features of the unscented Kalman filter (UKF) and optimal-REQUEST (quaternion estimator) algorithms are introduced for attitude determination. An interlaced filtering method is presented for the attitude determination of nano-spacecraft by setting the quaternion as the attitude representation, using the UKF and optimal-REQUEST to estimate the gyro drifts and the quaternion, respectively. The optimal-REQUEST and UKF are not isolated from each other. When the optimal-REQUEST algorithm estimates the attitude quaternion, the gyro drifts are estimated by the UKF algorithm synchronously by using the estimated attitude quaternion. Furthermore, the speed of attitude determination is improved by setting the state dimension to three. Experimental results show that the presented method has higher performance in attitude determination compared to the UKF algorithm and the traditional interlaced filtering method and can estimate the gyro drifts quickly.     

数据链航迹延迟与 ESM点迹间断融合跟踪

数据链与电子支援措施(Electronic Supporting Measures,ESM)融合跟踪是 1种典型的异类多传感器信息融合。针对数据链航迹存在延迟和 ESM点迹间断情况下的数据链航迹与 ESM点迹融合跟踪,提出了复杂情况下的数据链与 ESM融合跟踪方法,并对融合跟踪的精度进行分析研究。首先,对数据进行时间对准,保证数据链和 ESM数据率一致;然后,通过构造基于不敏变换(Unscented Transformation,UT)的非线性量测转换和误差估计方法,将数据链数据对准到 ESM数据;最后,通过引入随机补偿机制对数据链延迟进行补偿,并与 ESM数据进行关联融合。实验结果表明,有延迟补偿时的融合跟踪精度取决于延迟补偿均值与实际延迟的贴近程度。结果可为信息综合处理装备的发展和设计提供借鉴。     

一种用于深空高动态微弱信号的频率估计算法

研究了低载噪比与高动态环境下的深空测控系统频率估计算法,在分析已有方法不足的基础上,提出了一种基于无迹卡尔曼滤波(UKF)的闭环载波跟踪方法。此方法结合了锁频环鉴别器和UKF的优点,获得了宽的估计范围,高的估计精度和低的载噪比门限。在分析UKF模型的基础上,此方法还减少了原有UKF算法的运算量。仿真过程模拟了接收机的高动态运动轨迹,结果表明此法具有较好的动态适应能力、收敛性能和跟踪精度,能够有效地完成低载噪比与高动态环境下的频率估计。此法与基于扩展卡尔曼滤波(EKF)的频率估计算法相比,具有更低的频率估计误差,因此有着良好的应用前景。     

The adaptive Gaussian mixtures unscented Kalman filter for attitude determination using light curves

The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaussian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kalman Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non-linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can converge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state.