基于路标观测角的星际着陆器自主位姿确定技术

针对利用导航路标进行六自由度状态估计这一非线性、模糊性问题,对星际着陆器自主位姿确定技术进行了研究。为了减小算法的复杂性,提高求解精度,基于欧式变换下角度不变性,提出以导航路标观测视线之间所形成的夹角作为观测量,对像素观测方程中位置、姿态状态进行解耦求解。通过对观测矩阵的讨论,分析了导航路标空间分布对位姿确定精度的影响,给出了导航路标选取的最优观测方案。最后利用蒙特卡罗仿真对所提导航算法进行了验证,并对影响导航精度的各相关因素进行了分析。     

火箭上面级实时自适应抗差估计算法

针对火箭上面级飞行阶段存在的慢旋特性和大推力问题,提出了一种实时自适应抗差估计算法。针对前者,将抗差理论与CKF滤波算法相结合,以提高系统的抗差性;针对后者,采用嵌入机动决策的多模态轨道确定算法,在机动时刻调整状态方差矩阵,以加快观测信息对系统状态的修正作用,减小系统状态变量估计误差。通过对某次火箭上面级的实测数据分析,表明该算法能够有效抑制测量数据质量差的问题,提高系统的跟踪性能,并对外测弹道重建具有一定的应用价值。     

基于自适应扩展卡尔曼滤波的载波跟踪算法

精确的载波相位测量是精密测距中一个很重要的研究点。针对传统扩展卡尔曼滤波(EKF)的固定设计在先验信息不充分和动态变化环境中存在的不足,提出了一种基于自适应扩展卡尔曼滤波(AEKF)的载波跟踪算法。该算法通过实时监测滤波器新息或残差的动态变化,以修正状态噪声方差和观测噪声方差,进而调整滤波器增益,控制状态预测值和观测值在滤波结果中的权重。理论分析和仿真结果表明,本算法充分利用了观测信号的统计特性,克服了传统扩展卡尔曼滤波算法的不足,能够获得更好的载波跟踪性能。     

涡扇发动机变参数鲁棒H_∞滤波器设计

针对存在建模误差及测量噪声干扰条件下的涡扇发动机性能参数估计问题,标准卡尔曼滤波及其改进算法滤波估计误差收敛速度慢,滤波估计精度低,对不确定测量噪声及建模误差较为敏感,为此本文提出了一种变参数鲁棒H_∞滤波器设计方法。该方法采用仿射参数依赖Lyapunov函数设计满足H_∞性能指标要求的鲁棒滤波器,通过引入凸多胞技术,将参数依赖线性矩阵不等式(Linear Matrix Inequality,LMI)中变参数Lyapunov矩阵与系统系数矩阵之间耦合乘积导致的非凸优化问题,转化为常规LMI约束下的凸优化问题进行求解,降低了线性变参数(Linear Parameter Varying,LPV)鲁棒滤波器设计的保守性,得到了全局解。针对涡扇发动机的仿真结果表明:与扩展卡尔曼滤波器对比,采用该方法设计的滤波器具有较快的动态跟踪速度和较高的滤波精度,ΔFn的稳态估计误差不大于0.1%,ΔFn的相对估计误差不大于2.5%,同时对建模误差和测量噪声干扰具有较强的抑制能力。     

X射线脉冲星导航的最优观测周期确定

最优观测周期的确定对脉冲星导航计算具有重要意义。首先推导了航天器处观测脉冲相位估计方差的理论下界，然后给出了观测脉冲相位估计误差与脉冲星观测周期之间的关系式，并以观测脉冲相位估计的均方误差最小为准则，给出了最优观测周期的近似计算公式，最后利用脉冲星PSR B0531+21的实测数据验证了该关系式的正确性。仿真结果表明所给最优观测周期计算公式对脉冲星PSR B0531+21的预测误差为44 s，证明了所给公式的正确性，为导航中脉冲星观测周期的确定提供了理论基础。     

基于MCIS技术的相位差变化率单站无源定位

针对最大似然估计（ML）方法求解测相位差变化率单站无源定位问题计算量大、定位慢的问题,本文提出一种利用蒙特卡洛重要性抽样技术（MCIS）高精度、低复杂度的估计方法。根据Pincus定理推导出ML问题的近似全局解,利用重要性抽样（IS）技术构建符合高斯分布概率密度（PDF）的重要性函数,作为样本选取的依据,通过逆变换采样获得样本集,统计样本均值直接得到辐射源位置估计结果。MCIS方法简单易实现且运算量低,能够克服传统ML估计多维网格搜索耗时较长的缺陷,而且对目标位置初始估计误差有较低的敏感性。实验结果表明,MCIS算法在相同测量噪声水平下,定位精度优于EKF、NLS算法,有效减小了初始化估计误差对算法定位精度的影响,也进一步讨论分析了算法参数和不同观测条件对定位性能的影响。     

卡尔曼/粒子组合滤波在GPS/INS组合导航中的应用研究

在非线性模型和非高斯噪声条件下,粒子滤波在GPS/INS组合导航系统的观测精度较低时能取得较好的滤波结果,但在高观测精度情况下会导致滤波发散。针对这一问题,在分析了基本粒子滤波器算法原理的基础上提出一种卡尔曼/粒子组合滤波方法,将状态向量分为线性部分和非线性部分,分别用卡尔曼滤波和粒子滤波估计,既保证了简化后滤波算法的结果不会变差,又将运算量大大降低,仿真试验表明,组合滤波器能够获得较高的滤波精度,满足实际的导航要求。     

空中交通管制中轨迹预测的相互作用多模型算法的设计

研究了相互作用多模型算法，并就这种算法设计了几种方案，同时选取适当的参数对其进行了仿真设计和计算，给出了水平面内不同方案下的仿真结果，验证了这种算法的有效性，说明了通过模型概率实现模型之间的软转换的可行性。研究得出以下结论：该算法能在平稳飞行时使误差尽量减少，在机动时，对机动进行快速的探测和反应，同时，保证状态估计误差不超过原始的观测数据误差。     

鲁棒EKF在脉冲星导航系统中的应用

针对脉冲星导航系统的滤波问题,传统的扩展卡尔曼滤波(EKF)算法存在不能克服系统模型存在不确定性参数以及乘性噪声等缺陷,提出一种鲁棒EKF算法。首先,分析了状态预测误差方程和估计误差方程,利用统计学原理,得到了状态预测方差矩阵和状态估计方差矩阵计算等式。由于系统模型存在不确定性参数,状态预测协方差矩阵和状态估计协方差矩阵无法计算;因此,利用4个重要矩阵不等式,分析并找到预测方差矩阵和状态估计方差矩阵的上界。最后,利用状态估计误差协方差矩阵上界设计状态增益矩阵,使得状态估计协方差矩阵的迹最小。将该算法对脉冲星导航系统进行仿真,仿真结果验证了所提算法的有效性。     

航天器姿控系统的PD型学习观测器故障重构

针对满足Lipschitz条件的航天器姿态控制系统这一非线性系统中存在的执行器加性故障、空间干扰与测量噪声问题,提出了基于PD型迭代学习观测器的故障重构方法。该方法具有期望的鲁棒性能指标,能够在系统存在空间干扰与测量噪声情况下实现对突变故障与时变故障等故障类型的精确重构。基于线性矩阵不等式技术给出系统化PD型迭代学习观测器的设计方法,并根据Lyapunov稳定性理论对上述设计方法的稳定性条件进行了理论证明,同时利用鲁棒技术抑制空间干扰与测量噪声对执行器故障重构的影响,通过线性矩阵不等式工具箱求解观测器参数矩阵。最后,将该方法应用到航天器姿态控制系统中,仿真结果证明了该方法的有效性。     

Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

Mobile robots are often subject to multiplicative noise in the target tracking tasks, where the multiplicative measurement noise is correlated with additive measurement noise. In this paper,first, a correlation multiplicative measurement noise model is established. It is able to more accurately represent the measurement error caused by the distance sensor dependence state. Then, the estimated performance mismatch problem of Cubature Kalman Filter(CKF) under multiplicative noise is analyzed. An i...     

Optimal sensor selection strategy for discrete-time stateestimators

A novel sensor selection strategy is introduced, which can be implemented on-line in time-varying discrete-time system. We consider a case in which several measurement subsystem are available, each of which may be used to drive a state estimation algorithm. However, due to practical implementation constraints (such as the ability of the on-board computer to process the acquired data), only one of these subsystems can actually by utilized at a measurement update. An algorithm is needed, by which the optimal measurement subsystem to be used is selected at each sensor selection epoch. The approach described is based on using the square root V-Lambda information filter as the underlying state estimation algorithm. This algorithm continuously provides its user with the spectral factors of the estimation error covariance matrix, which are used in this work as the basis for an on-line decision procedure by which the optimal measurement strategy is derived. At each sensor selection epoch, a measurement subsystem is selected, which contributes the largest amount of information along the principal state space direction associated with the largest current estimation error. A numerical example is presented, which demonstrates the performance of the new algorithm. The state estimation problem is solved for a third-order time-varying system equipped with three measurement subsystem, only one of which can be used at a measurement update. It is shown that the optimal measurement strategy algorithm enhances the estimator by substantially reducing the maximal estimation error     

Analysis of Modeling and Bias Errors in Discrete-Time State Estimation

This paper concerns the effects of modeling and bias errors in discrete-time state estimation. The newly derived algorithms include the effect of correlation between plant and measurement noise in the system. The effects of nonzero mean noise terms and bias errors are considered. With plant or measurement matrix errors, divergence can occur. The local or linear sensitivity approach to error analysis, where the sensitivity is defined as a partial derivative with respect to a variable parameter taken about the modeled value, will not show this divergence due to neglect of higher order terms. Approximate algorithms are presented which circumvent the problem inherent in the local sensitivity approach. These make use of a "conditional bias" concept which views system error as a bias, conditioned on knowledge of the state estimates. It is shown that the actual error in optimum estimation is orthogonal to the residue error for suboptimum estimation where the residue error is defined as the difference between the actual estimation error and the optimum estimation error. Two examples, one concerning an integrated navigation system, demonstrate the theoretical results.     

A practical filter error method for aerodynamic parameter estimation of aircraft in turbulence

It is common for aircraft to encounter atmospheric turbulence in flight tests. Turbulence is usually modeled as stochastic process noise in the flight dynamics equations. In this paper, parameter estimation of nonlinear dynamic system with both process and measurement noise was studied, and a practical filter error method was proposed. The linearized Kalman filter of first-order approximation was used for state estimation, in which the filter gain, along with the system parameters and the initial states, constituted the parameter vector to be estimated. The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method, and the sensitivities of observations to unknown parameters were calculated by finite difference approximation. Some practical aspects of the method application were discussed. The proposed filter error method was validated by the flight simulation data of a research aircraft. Then, the method was applied to the flight tests of a subscale aircraft, and the aerodynamic stability and control derivatives were estimated. All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach. It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.     

包含乘性噪声自适应修正的非合作目标相对导航算法

无人机（UAV）态势感知的任务是利用机载传感器对未知环境进行目标识别和引导，针对无人机与非合作目标间中远距离的相对导航问题，提出了一种基于角度和距离量测的相对状态估计算法。在现有滤波算法的基础上，为了提高精度和稳定性，本文利用了列文伯格-马夸尔特（LM）优化的思想对迭代卡尔曼滤波（IEKF）算法进行改进，提出了一种LM-IEKF算法，并推导该算法在迭代过程中的状态更新方程及协方差阵的递推公式。在此基础上，考虑到距离传感器由于信号相关特性而引入的乘性噪声，现有的加性噪声模型难以适应，因此，进一步提出了基于量测噪声自适应修正的Modified LM-IEKF方法，通过在线实时更新噪声阵提高滤波的精度，并设置渐消记忆指数平滑估计结果。算法验证结果表明，与现有的EKF、IEKF算法相比，在仅含加性噪声的情况下，LM-IEKF算法具有更好的性能；在包含乘性噪声的情况下，Modified LM-IEKF可以有效地估计量测噪声，与目前广泛使用的EKF算法相比，在综合相对位置和相对速度精度上分别提高了10%和23%。     

Reduced state estimator for systems with parametric inputs

A reduced state estimator is derived for systems with bounded parameters as inputs. Optimal filter gains are derived for minimizing the total covariance of the estimation error due to measurement noise and parameter uncertainty. It is shown that these filter gains for a two-state system with a Gaussian parameter satisfy the Kalata relation in steady state. Equations are also derived for optimally filtering measurements in arbitrary time order. This reduced state estimator offers novelties over a traditional Kalman filter in its application to the class of problems considered. The total error covariance, which is minimized, makes no use of plant noise. Furthermore, the filter is easier to optimize in high dimensional and multiple sensor applications as well as in processing out-of-sequence measurements.     

Discrete-time min-max tracking

The problem of optimal state estimation of linear discrete-time systems with measured outputs that are corrupted by additive white noise is addressed. Such estimation is often encountered in problems of target tracking where the target dynamics is driven by finite energy signals, whereas the measurement noise is approximated by white noise. The relevant cost function for such tracking problems is the expected value of the standard H/sub /spl infin// performance index, with respect to the measurement noise statistics. The estimator, serving as a tracking filter, tries to minimize the mean-square estimation error, and the exogenous disturbance, which may represent the target maneuvers, tries to maximize this error while being penalized for its energy. The solution, which is obtained by completing the cost function to squares, is shown to satisfy also the matrix version of the maximum principle. The solution is derived in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated discrete-time Kalman filter is recovered. A local iterations scheme which is based on linear matrix inequalities is proposed to solve these equations. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the target position.     

Tracking of crossing targets with imaging sensors

The authors present an algorithm for the tracking of crossing targets using the centroid measurement and the centroid offset measurement of the distributed image formed by the targets. The measurements are obtained by a forward-looking infrared (FLIR) imaging sensor. The joint probabilistic data association merged-measurement coupled filter (JPDAMCF) is used for state estimation which performs filtering in a coupled manner for the targets with common measurements. Two filters are examined: one assuming the displacement noise white and the other one modeling it correctly as autocorrelated. The latter is shown to yield substantially better performance. The proposed algorithm demonstrates the usefulness of the JPDAMCF for tracking crossing targets in combination with the models for the centroid and offset measurements. Even though the centroid offset measurement requires more computations and a more complex model for estimation, it yields significantly better results if the filter accounts for its colored measurement noise     

基于压缩感知的信源个数与信号参数估计算法

信源个数与信号参数估计是盲信号处理的关键环节，对后续信号的侦察处理意义重大。针对当前盲信号信源个数与信源参数估计研究割裂的问题，提出了一种联合估计算法。通过分析信号的稀疏系数在不同测量矩阵相同稀疏字典下位置相同的特点，提高了信源个数和信号参数的估计精度，实现算法的自适应控制；通过数理分析确定了多级搜索策略的最优级次，大大降低了稀疏字典的原子数目。仿真结果表明：算法在一定信噪比下能够实现信源个数和信号参数的有效估计；信源个数和信号参数的估计精度随着压缩比的降低而逐渐提高，随着信噪比的提高而逐步增强；噪声对信源个数和信号参数估计精度的影响很大，尤其是低信噪比下；第2个信源载波频率和调频斜率的估计误差明显高于第1个信源参数的估计误差。