一种新型非线性滤波理论及其应用

研究一种新型的非线性滤波理论,即Unscented卡尔曼滤波（UKF）,同时为了获得更高的计算效率和确保协方差阵的非负定性,研究了平方根UKF。将UKF和平方根UKF应用到星载GPS卫星定轨中,实际算例表明UKF和平方根UKF的性能要优于常用的推广卡尔曼滤波的性能。     

基于增强型GPS的自适应UKF实时星间相对定位方法

高精度的星间实时相对定位是卫星编队飞行的一项关键技术。本文以双星编队为例,提出一种基于GPS双频P码、双频载波相位以及星间距离观测信息的自适应UKF、实时相对定位方法。仿真结果表明:相比传统的EKF方法,该算法能有效地提高滤波稳定性及相对定位精度,而且,星间测距信息的引入能大大减少整周模糊度判定所需的历元数。     

Robust adaptive unscented Kalman filter for bearings-only tracking in three dimensional case

This paper proposes an improved version of Unscented Kalman Filter (UKF), namely Robust Adaptive UKF (RAUKF), with a special focus on Bearings-Only Target Tracking for three-dimensional case (3DBOT). The automatic tuning of the noise covariance matrices and the robust estimation of the target states form a critical point for the performance of the Kalman-type filtering algorithms, especially in the variable environmental conditions exposed in underwater. The key idea of the proposed filter is to combine robust aspects of UKF and adaption of the process and measurement noise covariance matrices with low computational complexity. The main contribution of this paper is to adjust these matrices by means of the steepest descent algorithm, and the H_∞ technique is embedded to achieve superior performance in terms of accuracy and robustness against initial conditions and model uncertainties. Different experiments are performed to evaluate the performance of the proposed algorithm in the 3DBOT problem with a single moving observer. Simulations demonstrate that the proposed filter produce more accurate results with satisfactory computational burden in comparison with other methods.     

UKF在惯性/重力组合导航中的应用

将UKF(Unscented Kalman Filter)方法用于惯性/重力组合导航系统.UKF方法设计了少量的呈高斯分布的σ点,在每个更新过程中,σ点随着非线性状态方程和测量方程传播,从而获得滤波值及较高的计算精度,而且避免了对非线性方程的线性化过程.仿真结果表明:UKF方法比传统卡尔曼滤波及其改进的滤波模型都有更高的估计精度,并能有效的克服非线性严重时出现的滤波发散问题.     

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UKF滤波算法在数据同化中的应用

介绍了一种新的数据同化算法（UKF,Unscented Kalman Filter）,该算法不需要计算伴随矩阵,就能够解决模式的非线性问题。以Lorenz系统为例,进行了数据同化的数值试验。结果表明：基于UKF的同化方案与背景场的初始值无关,它能有效地抑制状态变量误差的增长,同化结果精度高。     

动态非线性滤波模型非线性强度的曲率度量及其应用

基于微分几何的两种曲率——参数影响曲率和固有曲率,给出了定量描述非线性滤波问题的非线性强度的方法,分别采用扩展Kalman滤波方法和Unscented Kalman滤波方法进行了模拟实验。结果验证了这些曲率确实能够度量非线性滤波问题的非线性强度,且能够评估非线性滤波算法的状态估计性能。     

Unscented Kalman Filter trained neural networks based rudder roll stabilization system for ship in waves

The large roll motion of ships sailing in the seaway is undesirable because it may lead to the seasickness of crew and unsafety of vessels and cargoes, thus it needs to be reduced. The aim of this study is to design a rudder roll stabilization system based on Radial Basis Function Neural Network (RBFNN) control algorithm for ship advancing in the seaway only through rudder actions. In the proposed stabilization system, the course keeping controller and the roll damping controller were accomplished by utilizing modified Unscented Kalman Filter (UKF) training algorithm, and implemented in parallel to maintain the orientation and reduce roll motion simultaneously. The nonlinear mathematical model, which includes manoeuvring characteristics and wave disturbances, was adopted to analyse ship’s responses. Various sailing states and the external wave disturbances were considered to validate the performance and robustness of the proposed roll stabilizer. The results indicate that the designed control system performs better than the Back Propagation (BP) neural networks based control system and conventional Proportional-Derivative (PD) based control system in terms of reducing roll motion for ship in waves.     

基于UKF的GPS非线性动态滤波算法

介绍了一种Unscented卡尔曼滤波算法，它通过确定性采样获得一组采样点，可获得更多的观测假设，对系统状态统计特性的估计更加准确，同时该算法无需对系统方程进行线性化，避免了传统的EKF算法由于线性化引入的误差。本文将UKF算法用于GPS非线性动态滤波技术中，建立了仿真模型并定义了仿真条件，与EKF算法的仿真结果相比，在系统状态统计特性未知的情况下，UKF算法对系统状态的估计更准确，定位精度更高。     

UKF在惯性/重力组合导航中的应用

将UKF(Unscented Kalman Filter)方法用于惯性/重力组合导航系统。UKF方法设计了少量的呈高斯分布的σ点,在每个更新过程中,σ点随着非线性状态方程和测量方程传播,从而获得滤波值及较高的计算精度,而且避免了对非线性方程的线性化过程。仿真结果表明:UKF方法比传统卡尔曼滤波及其改进的滤波模型都有更高的估计精度,并能有效的克服非线性严重时出现的滤波发散问题。