UPF based autonomous navigation scheme for deep space probe

The autonomous ＂celestial navigation scheme＂ for deep space probe departing from the earth and the autonomous ＂optical navigation scheme＂ for encountering object celestial body are presented. Then, aiming at the conditions that large initial estimation errors and non-Gaussian distribution of state or measurement errors may exist in orbit determination process of the two phases, UPF （unscented particle filter） is introduced into the navigation schemes. By tackling nonlinear and non-Gaussian problems, UPF overcomes the accuracy influence brought by the traditional EKF （extended Kalman filter）, UKF （unscented Kalman filter）, and PF （particle filter） schemes in approximate treatment to nonlinear and non-Gaussian state model and measurement model. The numerical simulations demonstrate the feasibility and higher accuracy of the UPF navigation scheme.     

基于“当前”统计模型的高动态GPS信号跟踪算法

提出了一种新的高动态GPS信号跟踪算法,基于＂当前＂统计模型对载波瞬时相位过程、瞬时频率及瞬时频率变化率过程进行建模;在此基础上,结合系统状态方程为线性而量测方程为非线性的特性,采用简化UKF算法对信号参数进行估计,同时采用EKF和标准UKF滤波算法在同等条件下对信号参数进行估计比对。仿真结果表明对信号参数所建模型能真实反映信号的过程,验证了所建模型的正确性的;简化UKF参数估计结果不仅精度与标准UKF相当且具有较高的运算效率,能更好满足高动态下的实时性需求。

Abstract：

A new high dynamic GPS signal tracking algorithm was proposed.Based on current statistical model,a new model for the GPS signal transitional process was established.Since the system involved a linear state equation and a nonlinear measurement equation,a simple Unscented Kalman Filter （UKF） algorithm was applied to estimation the signal parameters.For comparison purpose,an Extended Kalman Filter （EKF） algorithm and a standard UKF algorithm were also applied to the system to illustrate the effectiveness of the proposed filtering algorithm.Simulation results show that the new model can precisely represent the real signal process,and the simple UKF algorithm achieves high precision comparable to standard UKF with higher operation efficiency.Therefore,the new model and the proposed algorithm can satisfy real time requirement in high dynamic environment.     

一种融合 UKF 和EKF 的粒子滤波状态估计算法

在扩展卡尔曼滤波算法(Extended Kalman Filter,EKF)和不敏卡尔曼滤波算法(Unscented Kalman Filter,UKF)的基础上,提出一种基于融合的粒子滤波算法(Fusion based particle filter, FPF)。该算法首先利用EKF 与UKF 分别预测粒子状态,然后通过融合算法得到粒子的重要性建议分布,实现粒子状态更新。因为充分利用了量测信息,因而能有效提高状态估计精度。仿真中通过实例将该算法与已有的粒子滤波算法(Particle filter algorithm,PF)进行比较,结果表明该算法各方面性能都有较大改进。     

Modified unscented particle filter for nonlinear Bayesian tracking

A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.     

一种新的自适应机动目标跟踪算法

在＂当前＂统计（CS）模型基础上,提出了一种新的机动目标自适应滤波算法,当前统计模型-修正强跟踪滤波（CS-MSTF）算法。新算法在保留＂当前＂统计模型及强跟踪滤波器（STF）对一般机动目标跟踪精度高的优点的同时,作出以下改进：针对强跟踪滤波器在机动部分获得完美性能的同时,非机动部分的精度却不理想的缺陷,对预测误差协方差及渐消因子的计算作出修正,同时改进机动部分和非机动部分的精度;将目前常用的估计误差协方差的计算公式采用更加可靠的Joseph公式,增强了数值的稳定性和算法的鲁棒性。蒙特卡罗仿真表明,新算法的性能优于当前统计模型-强跟踪滤波（CS-STF）算法,能够进行有效估计。

Abstract：

Based on the ＂current＂ statistical model,a new adaptive maneuvering target tracking algorithm,CS-MSTF,was proposed. The new algorithm,keeping the merits of high tracking precision that the ＂current ＂ statistical model and strong tracking filter（STF） have in tracking maneuvering target has made the modifications as such：First,STF has the defect that it achieves the perfert performance in maneuvering segment at a cost of the precision in non-naneuvering segment,so the new algorithm modifies the prediction error covariance matrix and the fading factor to improve the tracking precision both of the maneuvering segment and non-maneuvering segment; The estimation error covariance matrix was calculated using the Joseph form,which is more stable and robust in numerical. The Monte-Carlo simulation shows that the CS-MSTF algorithm has a more excellent performance than CS-STF and can esitmate efficiently.     

复杂背景下基于改进粒子滤波的红外人体跟踪

为解决复杂背景下红外图像序列中的人体跟踪问题,提出了一种改进的粒子滤波跟踪方法。根据红外图像中人体目标的特点,首先建立人体的灰度直方图来提取其灰度特征,同时采用一种新的基于帧间差分和灰度概率分布图的方法提取其运动特征。然后将上述两种特征融合到粒子滤波框架中,用于粒子权值的计算,最终实现红外序列中人体的稳健跟踪。实验结果表明,和传统粒子滤波算法相比,该方法大大提高了复杂背景下红外人体跟踪的准确性和有效性,跟踪结果令人满意。

Abstract：

An improved particle filter tracking algorithm was proposed to solve the problem of human tracking in infrared image sequences under complex background.According to the characters of the human in the infrared images,the algorithm firstly constructed the gray histogram of the human to extract the gray feature,and at the same time utilized a new method based on the inter-frame difference and gray probability distribution image to get the motion feature.Then,the above-mentioned two features were fused into the particle filter frame to calculate the particle weights.Finally the robust tracking of human in infrared image sequences was achieved.The experimental results show that compared with the traditional particle filter algorithm,the presented method greatly improves the accuracy and effectiveness of the infrared human tracking under complex background,and the tracking results are satisfactory.     

Unscented Kalman filter for SINS alignment

In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment. Simulation results show the superior performance of this approach when compared with classical suboptimal techniques such as extended Kalman filter in eases of large initial misalignment. The UKF has good performance in case of small initial misalignment.     

Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter （UKF） via predicted variance Pk｜k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.     

IUKF neural network modeling for FOG temperature drift

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.     

UKF-based attitude determination method for gyroless satellite

UKF (unscented Kalman filtering) is a new filtering method suitable to nonlinear systems. The method need not linearize nonlinear systems at the prediction stage of filtering, which is indispensable in EKF ( extended Kalman filtering) . As a result, the linearization error is avoided, and the filtering accuracy is greatly improved. UKF is applied to the attitude determination for gyroless satellite. Simulations are made to compare the new filter with the traditional EKF. The results indicate that under same conditions, compared with EKF, UKF has faster convergence speed, higher filtering accuracy and more stable estimation performance.     

SR-UPF在探测器自主光学导航中的应用研究(英文)

提出了一种用于探测器在巡航段的自主光学导航方案,该方案利用光学导航相机以及星敏感器,通过测量星光信息以及天体边缘的信息,得出了探测器的相对位置.在此基础上针对导航系统状态方程和观测方程的非线性问题,提出了SR-UPF(Square-Root Unscented Particle Filter)算法,该方法将平方根UKF滤波和粒子滤波有机结合起来,可更好地提高自主导航系统的准确度和可靠性.通过数学仿真表明改进的算法与原UPF算法相比,收敛速度更快,滤波精度更高.     

UPF算法及其在目标跟踪问题中的应用

针对传统粒子滤波(PF)算法的缺陷,提出了一种改进的粒子滤波(UPF)算法。该算法以UKF方法生成替代分布并从中采样,解决了传统PF算法中以转换先验密度函数作为替代分布所引发的各种问题。对UPF算法进行了深入的分析研究,并给出了一个纯方位目标跟踪问题的仿真算例。理论分析与仿真结果均表明,改进算法提高了滤波的稳定性和精确性,具有较高的实用价值和广泛的应用前景。     

一种AUKF滤波算法及其在电力频率测量中的应用

为了提高无迹卡尔曼滤波器（UKF）在非线性随机系统中的抗干扰性和状态跟踪性能,提出了一种自适应无迹卡尔曼滤波算法（AUKF）。该算法通过计算状态变量微分累积值判断状态变量是否发生突变,并在突变时刻重新设置协方差矩阵,使状态变量估计值迅速收敛;同时估计观测噪声,通过调整噪声方差以减小观测噪声变化对测量精度的影响。将AUKF算法应用于电力系统频率测量,通过数值仿真得到电力频率稳定、波动和突变时的频率测量值。仿真结果表明该算法具有较好的测量效果,在保证频率测量精度和稳定性的同时,减小了频率测量滞后时间和超调。     

针对高速自治水下航行器的UKF主动目标跟踪算法

研究了基于高速自治水下航行器平台下的主动单目标跟踪,基于Unscented Kalman Filter(UKF)建立跟踪滤波器,在强观测噪声、大采样时间间隔情况下完成对目标各运动状态参量的准确估计。将此跟踪滤波器与基于Extended Kalman Filter(EKF)的跟踪滤波器进行了对比。计算机仿真结果表明采用EKF滤波器,目标的速度估计值可以收敛向真值,而距离估计值无法获得收敛;采用UKF滤波器,目标的速度和距离估计值都能获得收敛,且其对目标的速度估计较EKF准确。     

基于unscented粒子滤波的红外弱小目标跟踪

为有效解决非线性环境中的红外弱小目标跟踪问题,提出基于unscented粒子滤波的目标跟踪算法。状态转移先验概率中未考虑当前测量对状态估计的作用,为克服传统粒子滤波算法采用状态转移先验概率作为粒子滤波建议分布的缺点,采用UKF生成粒子滤波的建议分布(UPF),并从中抽样粒子。由于考虑到当前观测值在状态后验估计中产生的影响,改善了目标状态估计的性能,且实验所需粒子数目大大少于传统粒子滤波算法所需粒子数目。用实际红外图像对所提算法做了仿真实验,结果表明,用该方法得到的状态估计结果优于用传统粒子滤波算法和用扩展卡尔曼滤波作为建议分布的粒子滤波算法获得的结果。     

基于一个移动锚节点的无线传感器网络节点定位算法

节点定位技术是无线传感器网络应用的重要支撑技术之一,对此提出了一种基于移动锚节点的两步定位算法。该算法利用一个移动锚节点遍历整个网络,并周期性的广播包含自身当前位置的信息。把传感器节点的自身定位过程用基于无迹状态滤波(UKF)的目标跟踪方法实现。由于所用的目标状态模型和量测模型有一定的不确定性,所以先选取不共线3个锚节点信息,利用三边定位法提高滤波的初始位置精度,从而改善定位效果。最后仿真并分析比较了锚节点在多种移动轨迹情况下节点定位误差,结果表明本文所述两步定位法改善了对锚节点移动轨迹的特殊要求的限制,更适合实际情况,并取得理想的定位精度。     

光电跟踪系统的一类非线性滤波器

传统的光电跟踪系统通过测量目标的斜距离、俯仰角和偏航角来获取目标运动参数。针对附加角速度测量的一类新的光电跟踪系统,充分考虑传感器测量噪声的相关特性,基于非线性估计理论设计了目标跟踪滤波器。首先利用嵌套条件方法推导了转换测量误差前两阶矩的一致性估计,然后采用序贯采样卡尔曼滤波器（SUKF）对解耦后的转换测量进行序贯处理,最后从理论上分析了跟踪滤波器的稳定性并给出了跟踪系统的Cramer-Rao下界（CRLB）。Monte-Carlo仿真表明：滤波器估计误差均方差（RMSE）已逼近非线性跟踪系统的CRLB。

Abstract：

Generally tradition optic-electric tracking systems can obtain target motion parameters by measuring distance,elevation angle and yaw angle of targets.A target tracking filter taking correlated measurement noises into account was designed for optic-electric tracking systems with angle velocity measurements based on nonlinear estimation theory.Firstly,the consistent first two moments estimate of the converted measurement errors were derived by nested conditioning method;then the converted measurements were sequentially processed according to Sequential Unscented Kalman Filter （SUKF）;Finally the stability of the filter was analyzed theoretically and the Cramer-Rao Low Bound （CRLB） of the nonlinear tracking systems was proposed.Monte-Carlo simulation results show that the Root Mean Square of Estimate Error （RMSE） of the filter is closed to the CRLB of the nonlinear tracking systems.     

基于小波-UKF的自主光学导航方法研究

利用光学导航相机以及星敏感器,通过测量星光信息以及天体边缘的信息,进行了自主光学导航方案的设计.利用小波分析对观测信息进行预处理,滤除了它所包含的高频噪声,为UKF（Unscented Kalman Filter）滤波过程提了更为平稳的观测信息,并与UKF滤波进行比较,通过仿真验证了该算法的优越性.