Continuous-time ECV and ECA track filters with colored measurementnoise

Analytical solutions are presented for two continuous-time track filters with correlation in both the measurement errors and the target maneuvers. The measurement error model is a first-order Markov process, characterized by a correlation time (γ) and a position error variance (r). The target motion is described by one of two dynamic models: the exponentially correlated velocity (ECV) model, and the exponentially correlated acceleration (ECA) model. For both models, the steady-state velocity variance exhibits resonant behavior. The analytical solutions include as special cases several previously published results     

Closed-Form Solutions of Target-Tracking Filters with Discrete Measurements

Using two separate algorithms, called Kalman's recursive and Vaughan's nonrecursive, exact closed-form solutions of the steady-state two-state exponentially correlated velocity (ECV) and three-state exponentially correlated acceleration (ECA) target-tracking filters are derived for discrete position measurements. For this particular application, the superiority of Vaughan's nonrecursive over Kalman's recursive algorithm is observed.     

Detection performance in K-distributed and correlatedRayleigh clutters

Performance prediction for a detection system employing noncoherent integration is carried out for a chi-square family of fluctuating targets in K-distributed clutter plus noise. The detection performance for Swerling 11 targets in the K-distributed clutter plus noise is compared with that in exponentially correlated Rayleigh clutter. The results show that the performance prediction based on N pulses integrated in clutter plus noise using the K-distributed clutter model may be approximately equivalent to that using the exponentially correlated Rayleigh-distributed clutter model     

Simple Tracking Filters: Steady-State Filtering and Smoothing Performance

Computed data is presented, in normalized form, showing steady-state filtering and smoothing performance for optimum three-state estimators which assume exponentially correlated acceleration of the tracked object. The data are useful for preliminary design, gain selection, and performance prediction.     

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     

Feedback Realization of a Continuous-Time Optimal Filter

Another derivation of a continuous-time optimal linear filter is presented. Using this result as a point of departure, a feedback version of this continuous filter is postulated and shown to be optimal in the limit as the ``feedback gain' becomes infinite. It is then demonstrated that the need for this infinite feedback gain can frequently be eliminated. This feedback realization of the continuous optimal filter has application to the problem of optimally mixing two or more redundant signals, each contaminated by random noise. The feedback configuration has the advantages of simplicity and the bounding of otherwise troublesome very large errors. A simple example is given of the mixing of two (velocity) references each having exponentially correlated error.     

Simple Tracking Filters: Closed-Form Solutions

A closed-form steady-state solution is presented for a three-state tracking filter with continuous position measurements and exponentially correlated target acceleration. Some other related closed-form solutions are discussed, and some comparative performance data is presented for the discrete measurement case.     

Now You See It, Now You Don't

The signal enhancement properties of a single-loop delayline integrator (DLI) are derived for operation in the presence of correlated noise. The signal is assumed periodic with a period equal to the delay of the DLI and present only for a finite time interval. The noise is assumed additive, zero mean, statistically independent from the signal, and present for an infinite time. The noise also is assumed covariance stationary with a specified auto correlation function. Enhancement is defined as the ratio of the increase in average signal power to the increase in average noise power due to integration. The results are general in that the enhancement is derived in terms of the autocorrelation coefficients of the input noise. Plots of enhancement versus number of signal observations and feedback gain are shown for uncorrelated and exponentially correlated noise. The plots show graphically the optimum choices of feedback gain and number of signal observations for a specified auto correlation function and signal observation time. When strongly correlated noise, characteristic of 10-GHz backscatter from salt flats at low incidence angles, rather than uncorrelated noise is assumed present at the input of the DLI, an example, using DLI parameters typical of modern airborne radars, shows a degradation in the maximum achievable enhancement of 9 dB.     

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     

欠驱动机械臂的光滑时变指数稳定

 针对一般欠驱动机械臂的运动规划和控制问题进行研究。从二阶漂移非完整约束系统的一般控制模型出发,基于李代数扩展理论,提出了二阶非完整约束系统的光滑多项式扩展概念,并进一步提出了一种二阶漂移非完整约束系统控制的指数稳定控制方法。针对欠驱动机械臂中从动关节不多于主动关节的情况,给出了一个指数稳定控制定理,并以平面2R和3R欠驱动机械臂进行了仿真。仿真结果证明了提出的指数稳定控制方法的有效性。     

带有广义不确定性的导弹非线性控制系统设计

 在导弹系统存在广义不确定性情况下,提出了一种基于反演设计技术和RBF神经网络的导弹非线性自适应控制系统设计方法。设计过程中将不确定性对系统的影响合成为一项,然后应用RBF神经网络消除了其对系统的影响,有效地解决了控制系数矩阵未知时控制器设计问题,同时放松了现有文献中对控制系数矩阵不确定性的要求。应用Lypunov稳定性定理推导出神经网络权重矩阵的自适应调节律,并证明了闭环系统的所有信号均有界且指数收敛至系统原点的一个邻域。最后给出的BTT导弹非线性六自由度数字仿真结果验证了该算法的有效性。     

Simplified Solution for a Class of Fading Memory Filters

A fading memory filter is a least squares estimator (LSE) thatapplies an exponentially decaying weight to past measurements.When compared with a standard Kalman filter, its key advantagesare asymptotic stability and reduced sensitivity to modeling errors.This paper derives a simple solution for a class of fading memoryfilters, resulting in a reduction in computational complexity. Steadystate filter solutions are obtained for second-and third-order filtersused in a global positioning system (GPS) receiver for high dynamicvehicles.     

反向交叉眼对单脉冲雷达干扰效果分析及仿真验证

交叉眼干扰被认为是对单脉冲雷达干扰最有效的方式之一。基于雷达方程建立了隔离平台回波下的两点源反向交叉眼干扰模型，推导了交叉眼干扰欺骗角一般性公式，研究了干扰机发射天线间距、干扰平台旋转角和干扰机相对雷达之间距离等参数变化对角度欺骗效果的影响，并依据单脉冲雷达接收机获取角度的信息处理流程，建立了单脉冲雷达接收机仿真模型，对交叉眼数学模型的正确性和局限性进行了分析。研究结果表明：单脉冲雷达越靠近两点源交叉眼干扰机中心线、干扰机两发射天线间距越大、与干扰机距离越近时，角度欺骗效果越好；单脉冲雷达的欺骗角度随着与干扰机距离的接近呈指数式增大；数学模型和仿真模型计算的单脉冲雷达角度误差最大值随干扰机天线与雷达天线中心连线的夹角的增大呈指数化增长。研究可为交叉眼干扰工程设计作参考。     

Precision tracking algorithms for ISAR imaging

The simultaneous imaging and tracking of a moving target is one of the most difficult problems in inverse synthetic aperture radar (ISAR) signal processing. The problem is addressed here using a two-antenna imaging radar. The target range shift is sensed with an exponentially averaged envelope correlation algorithm while the angle change is measured with a differential phase shift algorithm. The three state Kalman filters are used in range and azimuth dimensions to provide both a filtered estimate and a one-sample-ahead prediction for the purpose of target tracking. The filtered range shift provides an accurate information for range bin alignment, and the target angle change provides the angular positions of the synthetic array elements. Therefore, the ISAR imaging simply becomes processing of a circular-arc aperture. The algorithms are verified both by computer simulations and also with experimental data processing     

Performance analysis of linearly combined order statistic CFARdetectors

Linearly combined order statistic (LCOS) constant false-alarm rate (CFAR) detectors are examined for efficient and robust threshold estimation applied to exponentially distributed background observations for improved detection. Two optimization philosophies have been employed to determine the weighting coefficients of the order statistics. The first method optimizes the coefficients to obtain efficient estimates of clutter referred to the censored maximum likelihood (CML) and best linear unbiased (BLU) CFAR detectors. The second optimization involves maximizing the probability of detection under Swerling II targets and is referred to as the most powerful linear (MPL) CFAR detector. The BLU-CFAR detector assumes no knowledge of the target distribution in contrast to the MPL-CFAR detector which requires partial knowledge of the target distribution. The design of these CFAR detectors and the probability of detection performance are mathematically analyzed for background observations having homogeneous and heterogeneous distributions wherein the trade-offs between robustness and detection performance are illustrated     

基于MAP估计和广义高斯分布的SAR图像边缘比率检测方法

 合成孔径雷达(SAR)图像的低信噪比和乘性相干斑噪声给SAR图像的边缘检测带来了极大的困难.通过引入广义高斯(GG)分布作为局部均值功率的先验分布模型,给出了局部均值功率在最大后验概率(MAP)意义下的最优估计,进而提出一种新的SAR图像边缘比率检测算子.利用以梅林变换为基础的对数累积量(MoLC)方法估计GG分布的参数,在此基础上给出一种局部均值功率MAP估计和GG分布参数估计的联合迭代求解方法.利用SAR实测数据对本文提出的边缘检测算子进行仿真验证,并将其与平均比率(RoA)算子和指数加权均值比(ROEWA)算子进行了对比,结果表明该算子可以有效克服相干斑噪声的影响,边缘定位准确且虚假边缘明显减少.     

基于并规则的模糊PID控制器的设计与仿真

当前大多数模糊系统的推理机都是以交规则的形式出现,随着输入的增加,规则的数目将早指数增长,出现了规则组合爆炸的问题。采用并规则设计的一种模糊PID控制器,解决了规则爆炸的问题,同时改善了系统的动态性能和实时性。     

Spacecraft attitude/rate estimation using vector-aided GPSobservations

A sequential filtering algorithm is presented for spacecraft attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the state of the filter, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the robustness and accuracy of the method. Numerical examples are used to demonstrate the performance of the method     

跨声速翼型上激波／边界层干扰的自适应控制计算

在激波区使用自适应壁对跨声速翼型上的激波/边界层干扰进行控制,可改变机翼的气动性能。这种被动控制可通过在翼型的激波区开一凹腔,其上覆盖一弹性橡胶膜柔壁来实现。本文给出用N-S方程数值模拟这一自适应控制翼型的跨声速粘性绕流,提出了一个适用于本特殊情况(物面边界局部地区在求解过程中有变化)的处理办法。并探讨了自适应柔壁对当代跨声速翼型绕流的影响。