皮卫星姿态估计及粗太阳敏感器标定算法研究

粗太阳敏感器是一种由多个光电池片配置组成的模拟式太阳敏感器,它结构简单、资源要求少,在皮卫星平台应用较为广泛。光电池光伏输出特性是粗太阳敏感器测量的基础,使用余弦关系近似等效会引入很大的误差。通过实际测试和深入分析,建立了更高精度的粗太阳敏感器输出特性等效数值模型,并以此为基础,推导了卫星姿态估计及粗太阳敏感器标定的联合在轨实时算法。算法采用多级耦合结构,由1个扩展卡尔曼滤波器(EKF)和6个卡尔曼滤波器组成,同时估计卫星姿态、卫星角速度以及卫星6个面共30个标定参数。仿真表明,和常规的EKF姿态估计算法相比,联合算法的运算量只增加了一半,而估计精度却提高了一个量级。     

On-Line Vehicle Motion Estimation from Visual Terrain Information Part I: Recursive Image Registration

This paper addresses estimation of motion from the optical flow observed by an airborne down looking electro-optical sensor. The paper is in two parts. Part I addresses the development and analysis of a velocity-to-height ratio estimation algorithm and its principal error characteristics. In part II, it is shown how the information provided by the motion estimator can be integrated with additional on-board sensors to provide a complete autonomous navigation system. Part I as presented here is a summary version of the full length paper.? The algorithm implements recursive registration of successive images by using the gradient of a similarity function between them to control the tracking of their relative shift. The shift estimate provides velocity/height information. Substantial saving in memory and computation as compared to conventional full frame registration is achieved by using only a single line in the TV frame. Stochastic mathematical models for the image, terrain and vehicle velocity perturbations are used in the analysis. The choice of the most appropriate similarity function in the registration algorithm is addressed. Performance analysis indicates very small error variances, as illustrated by numerical examples.     

Two-step optimal estimator for three dimensional target tracking

This study presents an adaptation of a novel estimation methodology to the general nonlinear three-dimensional problem of tracking a maneuvering target. The two-step optimal estimator (TSE) suggests an attractive alternative to the standard extended Kalman filter (EKF). A superior performance is accomplished by dividing the estimation problem into two steps: a linear first step and a nonlinear second step. The target tracking performance of the TSE is shown to be better than an EKF implemented in either inertial or modified spherical coordinates. In the passive case, where bearing/elevation angles only are measured, the TSE yields excellent range and target acceleration estimates. In the active case, where range measurement is available as well, a homing missile employing closed-loop optimal guidance based on the TSE state estimates obtains smaller miss distances than with either versions of the EKF.     

Estimation of 3D angular motion using gyroscopes and linear accelerometers

The authors present a novel, real-time angular motion estimation technique using a linear Gaussian estimator, and the outputs of linear accelerometers and gyroscopes, to assess the actual angular velocity of a rigid body in three-dimensional (3D) space. The method obtains the covariances of the random actual 3D angular velocity, the angular velocity measurement, and the measurement noise from the time averages of the outputs of an array of nine linear accelerometers and the outputs of three orthogonal gyroscopes. These statistics are used by the estimator to calculate the angular velocity of the rigid body in 3D space. The multisensor technique performance is evaluated through a computer simulation. Results indicate that the method leads to more accurate angular velocity values than are obtained conventionally.<>     

Nonlinear Estimation Via Kalman Filtering

The extended Kalman-Bucy filtering algorithm is applied to the problem of estimating the system parameters of a single-channel missile attitude control system. In particular, noisy observations of body angular rate, normal acceleration, and control surface deflec tion are used in the algorithm to obtain estimates of body angular rate, angle of attack, control surface deflection, air density, and missile velocity. Computational results obtained for this problem are presented. These results are evaluated to determine the applicapility of this estimator as a part of an adaptive control scheme. In addition, a general approach to the formulation of practical nonlinear estimation problems is suggested.     

On Signal and Noise Level Estimation in a Coherent PCM Channel

Joint maximum likelihood estimators are presented for the signal amplitude and noise power density in a coherent PCM channel with white Gaussian noise and a correlation receiver. The estimates are based upon the correlation coefficient outputs of the receiver. From these estimators, an estimator for the quantity (received signal energy)/bit/,(noise power)/(unit bandwidth) upon which the error probabilities depend, is derived. This estimator is shown to be useful as 1) a point estimator for the signal-to-noise ratio for the higher values of this ratio (about 4 dB or greater), and 2) an easily calculated statistic upon which to base data acceptance or rejection criteria. The acceptance or rejection levels are obtained by the use of confidence interval curves in conjunction with word error probability data.     

Nonlinear Estimation with Radar Observations

The nonlinearities of the radar measurement model equation are examined and their influence upon the accuracy of filtering and smoothing is determined. A simple algorithm by which the effects from these nonlinearities can be significantly reduced is derived. It consists of processing the radar observations in this order: azimuth, elevation, and range, using the azimuth residual to evaluate the elevation residual, and then using this combined result to evaluate the range residual. The accuracy of estimates obtained via the algorithm is compared with and shown to be superior to that of the extended Kalman filter (EKF). Furthermore, use of the algorithm does not increase the computational complexity of estimation beyond that of the EKF.     

基于EKF的天线罩误差斜率多模型估计方法

 提出一种新的滤波器结构,利用基于扩展卡尔曼滤波(EKF)的多模型(MM)算法,对天线罩误差斜率进行估计,降低天线罩误差对雷达自寻的导弹的影响,提高系统性能。在三维坐标下,创建包含导弹运动方程、目标运动方程、弹目相对运动方程的滤波模型。采用EKF算法,对包含天线罩误差的非线性观测方程进行线性化处理;依照多模滤波的思想,对天线罩误差进行离散建模,构建伪观测方程,更新模型概率,得到天线罩误差斜率的估计值;将斜率估计结果代入EKF,得到滤除天线罩误差影响的系统状态量估计结果并形成制导指令。仿真结果表明,所提方法可以有效地估计天线罩斜率,提高系统制导精度。     

The Estimation of the "Center of Gravity" of a Photon Density Profile in Noise

A model for an optical position estimation system is developed employing the photon Poisson process theory. The position estimate is based upon the definition of a center of gravity (CG) of the power density profile of the optical source on the focal plane. An estimator structure is derived using maximum likelihood estimates of the image profile. The resulting estimate of the CG is shown to be unbiased and its variance is obtained. The variance is shown to depend upon the signal energy and noise level as well as upon the distance of the center from the initial counting point. Thus, a composite estimation system is presented which reduces the variance and yet yields a simple structure. Studies on star estimation have yielded position accuracies better than 0.1 seconds of arc for a 2.5 visual magnitude star in a background of equivalent intensity.     

Instantaneous Frequency Estimation Using Adaptive Linear Predictor Weights

A computationally efficient scheme for estimating the digital instantaneous frequencies of narrowband inputs is introduced. The frequency estimates are obtained by searching for minima of the inverse input power spectrum. This spectrum is estimated at each input sample from the weights of an adaptive linear predictor which uses the LMS (least mean square) algorithm to update its weights. The related minima are sought via an iterative search algorithm, referred to as the iterative frequency estimator. This algorithm is computationally more efficient than available methods, and also provides a higher resolution. Simulation results are included; these include tracking of random message sequences in FM signals, and the formant frequency estimation of speech.     

A Kalman Filter Based Tracking Scheme with Input Estimation

Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.     

UKF方法及其在方位跟踪问题中的应用

采用UKF(Unscented Kalman Filter)方法处理了平面内地面站对目标的方位跟踪的估计问题。目标的位置和速度由选定的高斯分布采样点来近似，在每个更新过程中，采样点随着状态方程传播并随着非线性测量方程变换，由此不但得到目标位置和速度的均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方法比传统的扩展卡尔曼滤波(EKF)算法有更高的估计精度，并能有效地克服非线性严重时，方位跟踪问题中很容易出现的滤波发散问题。     

Optimum Position Estimation Using Multiple-Frequency Omega

The maximum-likelihood estimator of the line of position (LOP) resulting from K frequencies received from each of two Omega stations for high SNR is derived. The estimator takes advantage of interfrequency correlation of phase uncertainties by modeling them as resulting from phase velocity uncertainties which are jointly Gaussian with known covariance. The utilization of Omega for search and rescue systems is examined. For this application, the result of increasing the size of the unambiguous lanes of Omega by addition of a fourth frequency to the present three-frequency format is examined. The results of Monte Carlo simulation, performed for several easily implemented fourth frequencies, are presented. The simulation results illustrate the extent to which lane errors can be expected as a function of various fourth frequencies, phase velocity uncertainties, and SNR.     

Velocity Estimation from Position Outputs of a Three-Dimensional Radar

Kalman filtering equations to obtain estimates of velocity from radar position information are defined. In a track-while-scan operation, a three-dimensional radar sensor measures range, bearing, and elevation (r, ?, ?) of an airborne target at uniform sampling intervals of time T. The noisy position measurements are converted to x, y, z coordinates and put through a Kalman filter to obtain x, y, z velocity components. The filtering equations together with steady-state error estimates are given.     

Passive localization of moving emitters using out-of-planemultipath

The purpose of this work is to establish how a moving emitter, such as a jammer, can be localized by a passive receiver through the use of out-of-plane multipath signals reflected by the terrain. This is a novel localization technique that assumes no a priori knowledge of the localization of the multipath sources. The emitter parameters of range, heading, velocity, and altitude are estimated by exploiting the correlation between the direct-path signal and the delayed and Doppler modulated signals. Two basis assumptions about the scattering properties of the terrain lead to different maximum likelihood estimators (MLEs). The Cramer-Rao lower bounds (CRLB) are used to study estimator performance versus emitter velocity for each case. The proposed estimators are successfully demonstrated using field data collected at White Sands Missile Range (WSMR) during the DARPA/Navy Mountaintop program     

Optimum Steady-State Position and Velocity Estimation Using Noisy Sampled Position Data

The Kalman filtering technique is used to obtain analytical expressions for the optimum position and velocity accuracy that can be achieved in a navigation system that measures position at uniform sampling intervals of T seconds through random noise with an rms value of ?x. A one-dimensional dynamic model, with piecewise-constant acceleration assumed, is used in the analysis, in which analytic expressions for position and velocity accuracy (mean square), before and after observations, are obtained. The errors are maximum immediately before position measurements are made. The maximum position error, however, can be bounded by the inherent sensor error by use of a sufficiently high sampling rate, which depends on the sensor accuracy and acceleration level. The steady-state Kalman filter for realizing the optimum estimates consists of a double integrator, the initial conditions of which are reset at each observation.     

The dynamics of chromospheric spicules

We present the observational results on chromospheric spicules obtained at the Sayan observatory 50 cm coronograph. To investigate the evolution of chromospheric spicules, we analysed spicule spectra of strong chromospheric lines measured simultaneously at three altitudes above the solar limb during 5–60 min with a time resolution of 10 to 20 s. The spatial resolution was better than 1, and the spectral resolution was 0.03Å in 6563Å. The appearance of a spicule at a given altitude is preceded by an sharp increase in line-of-sight velocity and/or in line half-width at a lower level. Generally, the evolution has a non-monotonous impulsive character. Changes of line-of-sight velocities and other parameters of the line profile can be represented as the superposition of slow, evolutionary changes and fluctuations with periods of about 80 to 120 s. The amplitude of line-of-sight velocity fluctuations is 2–3 km/sec and tends to increase with height. By studying the phase delays of the fluctuations at different heights, we found that the propagation velocity exceeds 300 km s^–1, and that the disturbances do not necessarily propagate upwards.     

Pursuer identification and time-to-go estimation using passive measurements from an evader

We present an algorithm for identifying the parameters of a proportional navigation guidance missile (pursuer) pursuing an airborne target (evader) using angle-only measurements from the latter. This is done for the purpose of classifying the missile so that appropriate counter-measures can be taken. Mathematical models are constructed for a pursuer with a changing velocity, i.e., a direction change and a speed change. Assuming the pursuer is launched from the ground with fixed thrust, its motion can be described by a four-dimensional parameter vector consisting of its proportional navigation constant and three parameters related to thrusting. Consequently, the problem can be solved as a parameter estimation problem, rather than state estimation and we provide an estimator based on maximum likelihood (ML) to solve it. The parameter estimates obtained can be mapped into the time-to-go until intercept estimation results are presented for different scenarios together with the Cramer-Rao lower bound (CRLB), which quantifies the best achievable estimation accuracy. The accuracy of the time-to-go estimate is also obtained. Simulation results demonstrate that the proposed estimator is efficient by meeting the CRLB.     

Adaptations of a comparison technique for terrain navigation

This paper deals with a terrain navigation technique based on the comparison of terrain height measurements with a reference map. The comparison can be either based on minimum absolute differences, minimum square differences or on cross correlations. This paper will give an introduction to these methods and state some important adaptations. The first adaptation increases the independence of the terrain navigation system by calculating the position hypothesis only based on previous position measurements and the strap-down velocity. The second adaptation uses a detailed search to increase the performance and the reliability of the terrain navigation system. Both adaptations are important steps to a higher integrity of modular terrain navigation systems.     

地形辅助制导系统

本文叙述一种地形辅助制导系统,用来修正惯性系统或其它导航系统的误差,以提高飞行器的制导精度。这种辅助系统含有一个卡尔曼滤波器,它将飞行器所飞过的实际地形信息与预存的地图进行比较和处理,从而估计出导航系统的误差。对于一般的丘陵地形,辅助制导后的精度可达到数十米的范围。它适用于飞机、巡航导弹、弹道导弹和其它武器运载系统,而且所需的设备简单,易于实现。