导航解算中的有色噪声及其协方差矩阵自适应拟合

使用Kalman滤波进行动态导航定位解算需要涉及函数模型和随机模型, 而在实际应用中, 精确的函数模型和随机模型很难直接给出, 因此, 动态Kalman滤波的精度和可靠性将会受到函数模型误差和随机模型误差的影响. 在假设观测噪声和动力学模型噪声主要是具有一阶自相关特性的有色噪声的基础上, 提出了一种基于移动窗口的有色噪声函数模型和随机模型的自适应拟合法. 给出了计算有色噪声估值和噪声协方差矩阵的表达式, 并利用实测数据验证了模型及算法的可行性和实用性. 计算结果表明, 该算法能有效抵制有色噪声对导航滤波结果的影响.      

紊流风场下的无人机飞行状态卡尔曼滤波估计

为了模拟紊流风场下的无人机飞行状态响应,减小紊流风场对无人机飞行速度的影响,使用数值法建立了紊流风场有色噪声模型,生成了符合大气紊流相关特性的紊流风场并对其进行数值仿真。在此基础上,通过状态扩增处理将系统噪声白化,设计出一种基于有色噪声的无人机飞行速度卡尔曼滤波器,该滤波器解决了基本卡尔曼滤波仅适用于白噪声的情况。仿真结果表明,使用此滤波器可有效减小紊流风场对无人机飞行速度的影响,进而满足飞行速度控制输入的精度要求。     

基于四元数的UKF多传感器卫星姿态确定

对采用磁强计和太阳敏感器的卫星姿态模型,应用无迹卡尔曼滤波(UKF)算法确定卫星姿态。由四元数描述姿态,在算法中将其转换成旋转矢量,另设计了四元数求均值的迭代算法,解决了四元数在UKF算法中的处理。仿真结果表明:广义卡尔曼滤波(EKF)算法对初值依赖性较大,UKF算法不受初值影响且精度高于EKF,具快速性、精度高和稳定性强等优点。     

Kalman滤波的铷原子钟控制算法

以GPS接收机输出的1pps信号为参考信号,采用Kalman滤波算法对铷原子钟的参数进行估计,计算铷原子钟的频率调整量,对铷原子钟进行调整,使其和UTC时间保持同步。实验结果表明,受驯铷原子钟输出1pps与UTC（NTSC）钟差的标准差优于3.5 ns,钟差峰峰值优于15 ns,100 s采样的Allan方差为1.83×10 -12 ,10000 s采样的Allan方差为6.1×10 -13 。实验证明了基于Kalman滤波的铷原子钟控制算法,使铷钟获得了较好的准确性和长期稳定性,且对其短期稳定性影响最小,是一种可靠稳定的铷钟控制方法。     

A two-step robust adaptive filtering algorithm for GNSS kinematic precise point positioning

In kinematic navigation and positioning, abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance. Generally, robust adaptive filtering algorithm is used to reduce the influence of them on positioning results. However, it is difficult to accurately identify and separate the influence of abnormal observations and kinematic model disturbances on positioning results, especially in the application of kinematic Precise Point Positioning (PPP). This has always been a key factor limiting the performance of conventional robust adaptive filtering algorithms. To address this problem, this paper proposes a two-step robust adaptive filtering algorithm, which includes two filtering steps: without considering the kinematic model information, the first step of filtering only detects the abnormal observations. Based on the filtering results of the first step, the second step makes further detection on the kinematic model disturbances and conducts adaptive processing. Theoretical analysis and experiment results indicate that the two-step robust adaptive filtering algorithm can further enhance the robustness of the filtering against the influence of abnormal observations and kinematic model disturbances on the positioning results. Ultimately, improvement of the stability and reliability of kinematic PPP is significant.     

基于递推更新卡尔曼滤波的磁偶极子目标跟踪

针对磁性目标跟踪问题,以磁偶极子等效场源模型为基础,建立磁性目标跟踪的离散状态空间模型,将磁偶极子目标实时跟踪问题转化为状态空间模型的滤波估值问题。针对磁性目标初始条件难以获得且现有卡尔曼类滤波算法在大初始误差条件下容易出现发散的问题,提出一种递推观测更新的卡尔曼滤波算法,将现有的一步观测更新描述为递推更新过程,等效降低大初始误差带来的大非线性误差。仿真与实测数据测试结果表明,本文算法具有良好的精度和收敛性,能够有效抑制磁偶极子跟踪中由于大初始误差导致的滤波发散,适于实际应用。     

Onboard orbit determination using GPS observations based on the unscented Kalman filter

Spaceborne GPS receivers are used for real-time navigation by most low Earth orbit (LEO) satellites. In general, the position and velocity accuracy of GPS navigation solutions without a dynamic filter are 25 m (1σ) and 0.5 m/s (1σ), respectively. However, GPS navigation solutions, which consist of position, velocity, and GPS receiver clock bias, have many abnormal excursions from the normal error range for space operation. These excursions lessen the accuracy of attitude control and onboard time synchronization. In this research, a new onboard orbit determination algorithm designed with the unscented Kalman filter (UKF) was developed to improve the performance. Because the UKF is able to obtain the posterior mean and covariance accurately by using the second-order Taylor series expansion through the sampled sigma points that are propagated by using the true nonlinear system, its performance can be better than that of the extended Kalman filter (EKF), which uses the linearized state transition matrix to predict the covariance. The dynamic models for orbit propagation applied perturbations due to the 40 × 40 geo-potential, the gravity of the Sun and Moon, solar radiation pressure, and atmospheric drag. The 7(8)th-order Runge–Kutta numerical integration was applied for orbit propagation. Two types of observations, navigation solutions and C/A code pseudorange, can be used at the user’s discretion. The performances of the onboard orbit determination were verified using real GPS data of the CHAMP and KOMPSAT-2 satellites. The results of the orbit determination were compared with the precision orbit ephemeris (POE) of the CHAMP and KOMPSAT-2 satellites.     

天线罩干扰下的雷达/红外复合导引头数据融合

针对雷达/红外复合导引头中存在天线罩折射以及外部干扰问题,在三维模型下,提出一种基于扩展卡尔曼滤波(EKF,Extended Kalman Filter)的多模型算法,对天线罩斜率进行估计,并将估计结果代入EKF,降低观测视线角中的天线罩折射干扰,形成最优局部估计.采用基于环境信息的加权因子法对雷达/红外局部估计结果进行融合,通过环境信息度量传感器测量结果的可信度,忽略不可信的局部估计结果.设计4组算例检验融合算法性能,仿真结果表明:所提算法可以准确估计天线罩斜率,合理并有效使用雷达/红外传感器信息,提高系统估计精度.     

Low C/N_0 Carrier Tracking Loop Based on Optimal Estimation Algorithm in GPS Software Receivers

Suppressing jitter noises in a phase locked loop(PLL) is of great importance in order to keep precise and continuous track of global positioning system(GPS) signals characterized by low carrier-to-noise ratio(C/N0).This article proposes and analyzes an improved Kalman-filter-based PLL to process weak carrier signals in GPS software receivers.After reviewing the optimal-bandwidth-based traditional second-order PLL,a Kalman-filter-based estimation algorithm is implemented for the new PLL by decorrelating the model error noises and the measurement noises.Finally,the efficiency of this new Kalman-filter-based PLL is verified by experimental data.Compared to the traditional second-order PLL,this new PLL is in position to make correct estimation of the carrier phase differences and Doppler shifts with less overshoots and noise disturbances and keeps an effective check on the disturbances out of jitter noises in PLL.The results show that during processing normal signals,this improved PLL reduces the standard deviation from 0.010 69 cycle to 0.007 63 cycle,and for weak signal processing,the phase jitter range and the Doppler shifts can be controlled within ±17° and ±5Hz as against ±25° and ±15 Hz by the traditional PLL.