基于双视线测量的相对导航方法

对非合作目标自主交会中的中远程相对导航问题进行研究.首先,给出二阶近似的航天器相对轨道动力学方程,并对视线(LOS)导航方法的可观测性进行了分析.然后,提出基于双视线测量的相对导航方案.该导航方案中,系统过程噪声模型采用适用于航天器远距离相对运动的轨道动力学方程;量测量包括目标对两个追踪航天器的视线角和追踪航天器之间的...     

捷联制导旋转弹制导信息提取研究CSCD

以捷联式半主动激光导引头为研究对象,研究其应用在旋转弹上制导信息的提取方法。根据坐标转换关系得到旋转弹惯性系视线角解耦模型,由于导引头和速率陀螺仪具有测量误差特性,直接解耦得到的制导信息会产生较大的误差。基于视线角解耦模型的非线性,采用扩展卡尔曼滤波(EKF)的方法对测量信息进行滤波处理,估计出目标的位置,从而得到捷联式半主动激光导引旋转弹的制导信息。将扩展卡尔曼滤波方法与α-β滤波方法进行对比分析,得到扩展卡尔曼滤波方法对捷联式半主动激光导引旋转弹制导信息的估计精度更高,收敛更快。     

捷联激光制导的视线信号提取方法

提出了一种弹目视线角信号的提取方法,该方法采用UKF解耦,针对捷联激光制导炸弹需要攻击移动目标却不能测量弹目相对距离和速度的情况,建立了弹目相对运动模型,解耦过程不需要相对距离信息,能够更方便地应用于工程实践.经仿真验证,此方法可以较好地估计出弹目视线角以及角速率信息,能够满足捷联激光制导炸弹工程应用的需要.     

基于视线角序列的机动目标视线角速率计算

 依据拦截弹与机动目标间的相对位置关系,采用导引头球面模型,基于当前统计模型,实现了强跟踪状态自适应滤波,计算出了可用于制导的视线角速率。该算法利用导引头球面模型,将测角信息转换成距离信息,进行方差自适应调整和卡尔曼滤波。在只给出视线角序列信息的情况下,根据相对状态滤波结果求出所需的视线角速率。     

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.     

A pure-Cartesian formulation for tracking filters

A pure-Cartesian formulation is presented for angle-only and angle-plus-range tracking filters. Unlike conventional angle-only filters, which use target elevation and bearing as measurements, the filter expresses the sensor measurements in Cartesian coordinates. Consequently, the filter performs equally well for any line-of-sight (LOS) geometry, even when target elevation approaches or is equal to ±90°     

滚转舰炮制导炮弹的空间多约束导引与控制一体化设计

在大口径舰炮制导炮弹打击近岸机动目标的末段,考虑攻击角、控制受限、视线（LOS）角速率测量受限等约束,提出了一种基于块动态面与扩张状态观测器（BDSESO）的多约束空间导引与控制一体化设计（IGC）方法。构建了滚转制导炮弹的空间导引与控制一体化的严块反馈串级模型,运用扩张状态观测器估计视线角速率和目标机动、建模误差、风等系统内外不确定干扰。为了在有限时间内零化视线角跟踪误差与视线角速率,采用自适应指数趋近律设计非奇异终端滑模,通过自适应动态面控制有效镇定串级系统并避免微分膨胀,引入自适应Nussbaum增益函数补偿控制受限的饱和非线性。通过Lyapunov理论证明了视线角跟踪误差、视线角速率的有限时间收敛性与全系统状态的一致最终有界性。半实物仿真实验表明：该方法使制导炮弹在打击具有不同机动形式的目标时,均具备较好的末制导性能。     

Multiple target tracking using products of position measurements

The continued development of the symmetric measurement equation (SME) filter for track maintenance in multiple target tracking (MTT) is considered, focusing on the case in which the SMEs are generated by forming sums of products of the original position measurements. The SME filter is developed for the case of N targets whose motions consist of random perturbations about constant-velocity trajectories. It is assumed that measurements of x-coordinate positions are available, and that the number of measurements is equal to the number of targets. Various analytical properties of the SME filter are studied. It is shown that under a very weak condition, the estimation error equation is locally exponentially stable. The performance of the SME filter is investigated by comparing it with an optimal (minimum-variance) estimator and by generating a computer simulation in the six-target case     

True proportional navigation with maneuvering target

We analytically obtain the capture regions for the true proportional navigation (TPN) missile guidance law against an intelligently maneuvering target. Two versions of TPN are considered. The first is the original TPN which assumes the commanded lateral acceleration to be directly proportional to the line-of-sight (LOS) rate only, the proportionality factor being an arbitrary constant or dependent only on the initial closing velocity. The other, known as RTPN (realistic TPN), assumes the commanded lateral acceleration to be directly proportional to the LOS rate and also the current closing velocity. The target is assumed to maneuver in such a way as to increase the LOS rate and thus directly oppose the proportional navigation (PN) philosophy of annulling the LOS rate. A necessary and sufficient condition for capture is derived for the original TPN, and using it, the exact capture region is obtained. A sufficient condition for capture is derived for RTPN and is used to obtain its capture region partially. Some necessary conditions for capture are also derived for RTPN and are used to obtain an upper bound on its complete capture region. Using these conditions some important results on the existence of capture regions and a comparative study of capturability of TPN laws are also presented     

Coordinate Conversion and Tracking for Very Long Range Radars

The problem of tracking with very long range radars is studied in this paper. First, the measurement conversion from a radar's r-u-v coordinate system to the Cartesian coordinate system is discussed. Although the nonlinearity of this coordinate transformation appears insignificant based on the evaluation of the bias of the converted measurements, it is shown that this nonlinearity can cause significant covariance inconsistency in the conventionally converted measurements (CM1). Since data association depends critically on filter consistency, this issue is very important. Following this, it is shown that a suitably corrected conversion (CM2) eliminates the inconsistency. Then, initialized with the converted measurements (using CM2), four Cartesian filters are evaluated. It is shown that, among these filters, the converted measurement Kalman filter with second order Taylor expansion (CM2KF) is the only one that is consistent for very long range tracking scenarios. Another two approaches, the range-direction-cosine extended Kalman filter (ruvEKF) and the unscented Kalman filter (UKF) are also evaluated and shown to suffer from consistency problems. However, the CM2KF has the disadvantage of reduced accuracy in the range direction. To fix this problem, a consistency-based modification for the standard extended Kalman filter (E1KF) is proposed. This leads to a new filtering approach, designated as measurement covariance adaptive extended Kalman filter (MCAEKF). For very long range tracking scenarios, the MCAEKF is shown to produce consistent filtering results and be able to avoid the loss of accuracy in the range direction. It is also shown that the MCAEKF meets the posterior Carmer-Rao lower bound for the scenarios considered.     

基于新型饱和函数滑模观测器的永磁同步电机控制系统

针对传统反电动势滑模观测器(SMO)抖振造成的永磁同步电机转子位置检测、转速估算不准确的问题,在传统反电动势SMO的基础上提出一种新型饱和函数SMO。构造反电动势状态观测方程,利用Lyapunov稳定性理论论证其收敛性;同时考虑观测器增益系数以及低通截止频率的给定,根据实时转速反馈选取合适的观测系数和低通滤波器的截止频率;在低通滤波器后增加卡尔曼滤波器滤除系统的测量噪声和测量误差,提高观测精度。对比仿真和试验结果,验证了新型饱和函数SMO算法的实用性和有效性。     

着陆小行星的视线测量自主光学相对导航算法及其可观性分析

 以软着陆小行星为工程应用背景,提出了基于视线(LOS)测量的自主光学相对导航算法,并对导航算法的可观性进行了比较深入的分析研究。首先,基于透视投影模型的共线方程,给出了导航测量方程和测量敏感矩阵;接着,通过多个视线矢量的测量,应用高斯最小二乘微分修正(GLSDC)和龙贝格-马尔塔(LM)算法对探测器的位置和姿态参数进行了估计。然后,通过对误差方差阵逆矩阵秩的分析,比较详尽地分析了不同数量视线观测条件下导航算法的可观度和可观性。最后,通过数学仿真对所提出的自主导航算法的可行性进行了验证。     

False alarm effects on estimation in multitarget trackers

An analysis of false alarm effects on tracking filter performance in multitarget track-while-scan radars, using variable correlation gates, is presented. The false alarms considered originate from noise, clutter, and crossing targets. The dimensions of the correlation gates are determined by filter prediction and measurement error variances. Track association is implanted either by means of a distance weighted average of the observations or by the nearest neighbor rule. State estimation is performed by means of a second-order discrete Kalman filter, taking into consideration random target maneuvers. Measurements are made in polar coordinates, while target dynamics are estimated in Cartesian coordinates, resulting in coupled linear filter equations. the effect of false alarms on the observation noise covariance matrix, and hence on state estimation errors, is analyzed. A computer simulation example, implementing radar target tracking with a variable correlation gate in the presence of false alarms, is discussed     

Detection Performance of Hard-Limited Phase-Coded Signals

The performance of a complex phase-coded waveform digital processor with hard-limiting constant false-alarm rate (CFAR) is presented. Processing losses relative to ideal matched-filter performance are computed and verified by hardware measurement. The losses considered include the consequences of hard limiting, envelope algorithm implementation, range cusping, and the associated effects of code length, IF filter bandwidth, and in-phase and quadrature channel phase offset. The range resolution properties of two closely spaced targets are also considered.     

Optimal robust non-fragile Kalman-type recursive filtering with finite-step autocorrelated noises and multiple packet dropouts

In this paper, the optimal robust non-fragile Kalman-type recursive filtering problem is studied for a class of uncertain systems with finite-step autocorrelated measurement noises and multiple packet dropouts. The system state, measurement output and filter parameters are all subject to stochastic uncertainties or multiplicative noises, where the measurement noises are finite-step autocorrelated. When there exist multiple packet dropouts in the system output, the original system is converted into an auxiliary stochastic uncertain system by the augmentation of system states and measurements. The process noises and measurement noises of the auxiliary system are shown to be finite-step autocorrelated and cross-correlated. Then, a robust non-fragile Kalman-type recursive filter is designed that is optimal in the minimum-variance sense. The proposed filter is not only robust against the uncertainties in the system model and measurement model, but also non-fragile against the implementation error with the filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed method.     

Analytical solution of discrete colored noise ECA tracking filter

New analytical solutions of steady-state Kalman gains are presented for a discrete-time tracking filter with correlation in both the measurement noise and the target maneuver. The measurement noise model is a first-order discrete Markov process characterized by a correlation coefficient ρ. The target motion is examined for an exponentially correlated acceleration maneuver type in which the vehicle oscillation such as wind-induced-bending is also considered. The present solution method is based on factorizing the observed spectral density matrix Ψ(z) in frequency domain. The algorithm proposed here gives the Kalman gain matrix directly. For a case when the steady-state error covariance matrix is desired, such gains can be incorporated with the algebraic Riccati equation     

Digital tracking filters with high order correlated measurement noise

The existing algorithms for the design of digital filters with colored measurement noise involve a restriction on the dimension of the measurement error model. Kalman filter equations and state space partition are used to formulate an optimal tracking filter without such restrictions. The input to the new filter are two consecutive measurements, and it is initialized by using the first available measurements and the error model correlation matrix. Several examples illustrate the filter formulation and initialization.     

A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

This paper studies the dynamic estimation problem for multitarget tracking. A novel gating strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density(PHD) filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measurements. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estimation algorithm when sequential Monte Carlo(SMC) implementation of the PHD filter is investigated, where the measurements are used to drive the particle clustering within the space gate.The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.     

利用Singer模型的无陀螺姿态和角速度估计

 给出了一种利用Singer跟踪模型的扩展卡尔曼滤波器 (ExtendedKalmanFilter) ,用于无陀螺姿态和姿态角速度估计。在滤波器中 ,姿态和姿态误差分别由姿态四元数和误差四元数表示 ,而姿态角加速度由一阶Markov过程描述 ,从而避免采用姿态动力学模型。利用数值仿真计算验证了滤波器的性能。在所有的仿真过程 ,滤波器显示出快速收敛能力。稳态估计误差主要由测量更新频率和精度决定。     

基于新息异常检测的改进抗差自适应卡尔曼滤波算法

在工程应用中,量测异常及量测噪声统计特性的时变是引起标准卡尔曼滤波振荡甚至发散的主要原因。经典抗差Sage-Husa自适应滤波方案,对量测中的孤立型异常有所抵抗,并可在线估计量测噪声统计特性改善滤波效果,但当连续型异常值出现时,其滤波效果不佳。针对现有抗差Sage-Husa自适应滤波方案的不足,提出了新的改进滤波方法。在改进算法中,当检测到量测异常时采用模值更大的先验预测方差阵代替原算法中的后验估计方差阵,在估计量测噪声方差时起到放大作用,以降低异常量测权重,提高滤波精度;采用IGG方案构造了新的权函数,可在抑制异常影响的同时调节估计方差阵,以免连续异常时新息持续置零引起的滤波发散;采用标准卡尔曼滤波新息辅助异常检测的双重检测策略,避免了因量测噪声方差阵的调节引起检测阈值变化而导致的漏检率增高。仿真实验表明,与常规抗差自适应滤波算法相比,该方案可更加有效地抑制量测异常值的影响。