基于STF和7thCQKF的状态突变系统跟踪算法

针对传统非线性滤波算法对状态突变的鲁棒性较差,存在跟踪缓慢甚至失效的问题,提出了强跟踪七阶正交容积卡尔曼滤波(ST-7thCQKF)算法.算法将对非线性系统滤波效果良好的七阶正交容积卡尔曼滤波(7thCQKF)与强跟踪滤波(STF)融合,通过在7thCQKF的预测协方差中引入渐消因子调节增益矩阵,提高算法对状态突变系统...     

Performance Improvement of Industrial Drives With Mechanical Elasticity Using Nonlinear Adaptive Kalman Filter

This paper deals with the application of the adaptive control structure for torsional vibration suppression in the drive system with an elastic coupling. The proportional-integral speed controller and gain factors of two additional feedback loops, from the shaft torque and load side speed, are tuned on-line according to the changeable load side inertia. This parameter, as well as other mechanical variables of the drive system (load side speed, torsional and load torques), are estimated with the use of the developed nonlinear extended Kalman filter (NEKF). The initial values of the Kalman filter covariance matrices are set using the genetic algorithm. Then, to ensure the smallest state and parameter estimation errors, the on-line adaptation law for the chosen element of the state covariance matrix of the NEKF is proposed. The described control strategy is tested in an open and a closed-loop control structure. The simulation results are confirmed by laboratory experiments.     

自适应SICKF及在目标跟踪中的应用

针对容积卡尔曼滤波（CKF) 估计精度在系统状态或参数突变时下降的问题,结合均方根嵌入式容积卡尔曼滤波(SICKF)和强跟踪滤波(STF)思想,提出了一种自适应SICKF(ASICKF)方法。在SICKF获得高估计精度的同时引入STF条件,根据系统输出残差获得自适应渐消因子,将其引入系统输出协方差均方根阵和互协方差阵中对滤波增益进行实时修正,强迫系统输出残差序列始终正交,从而使SICKF算法具备强跟踪能力。为验证所提ASICKF算法性能,利用数值仿真将其应用于存在突变情况的目标跟踪问题中。仿真结果表明,ASICKF在系统状态突变时仍能保持较高的估计精度,算法稳定性和适应能力较好。     

Tests of sequential data assimilation for retrieving profile soilmoisture and temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses     

混合卡尔曼滤波在外辐射源雷达目标跟踪中的应用

为了提高无迹卡尔曼滤波(UKF)中误差协方差矩阵的估计精度,该文结合外辐射源雷达目标跟踪模型,提出了一种混合卡尔曼滤波(MKF)算法,首先通过UKF对目标状态进行一次后验估计,然后重新建立一个观测方程,把UKF滤波输出的状态估计值转化为新建观测方程的量测值,并通过线性卡尔曼滤波对状态进行二次最优估计。实验结果表明,与扩展卡尔曼滤波(EKF), UKF相比,MKF明显提高了外辐射源雷达目标跟踪的精度。      

Practical Kalman Filter Software Performance Testing & Validation

Several tests are described which can be used for any Kalman-type filter/smoother computer program. These tests are demonstrated by a case history on a large dimensional Kalman filter/smoother program which implements a 34-state inertial navigation system dynamic error model. The execution of a large dimensional Kalman filter/smoother (KFS) on real measurement data does not represent a software test of the KFS since the right answer (the correct underlying state vector) is unknown; only ``reasonableness checks' are actually possible. Simulated test data were used to exercise the KFS program in a Monte Carlo sense and its outputs evaluated using heuristic plot comparisons as well as rigorous statistical tests. Direct tests on the accuracy of the transition matrix, discrete process noise matrix, and covariance matrix calculations have been derived and demonstrated. Methods for testing properties of the Kalman filter innovations sequence are also covered. The approach and required auxiliary software that generates the test data can be employed to perform suboptimal modeling sensitivity studies and for evaluating analysis methods that depend on KFS estimates.     

Canonical form for deadbeat control

For a time-invariant completely controllable single-input system, a canonical form is established so that the required control sequence is given by the elements of the initial-state vector and so that closed-loop deadbeat control is always possible if the required state variable can be measured.     

一种精确协方差估计的转换量测卡尔曼滤波方法

传统转换量测卡尔曼滤波(CMKF)方法对误差协方差矩阵进行估计时,采用了线性近似的方式。当量测方位误差较大时,无法准确估计出协方差矩阵。针对该问题,提出了一种基于精确协方差估计的CMKF方法,可有效抑制方位误差对协方差估计的影响。仿真结果表明该方法可有效抑制量测方位误差的影响,提升目标跟踪的定位精度。     

Parallel and sequential block Kalman filtering and theirimplementations using systolic arrays

Two sets of block Kalman filtering equations that differ in the manner of generating the initial and updated estimates are derived. Parallel and sequential schemes for generating these estimates are adopted. It is shown that the parallel implementation inherently leads to a block Kalman estimator which provides filtered estimates at the vector (block) level and fixed-lag smoother estimates at the sample level. The sequential implementation scheme, on the other hand, generates the estimates of each sample recursively, leading naturally to a scalar (filter) estimator. These scalar estimates are arranged in a vector form, resulting in a block estimator which solely generates filtered estimates both at the vector and sample levels. Simulation results on a speech signal are presented which indicate the advantages of the sequential block Kalman filter. An algorithm for iterative calculation of Kalman gain and error covariance matrices is given which does not require any matrix inversion operation. The implementation of this algorithm using available systolic array processors is presented. A ring systolic array which can be used to implement the state update part of the block Kalman filter is suggested     

Disturbance observer based fully digital controlled PWM inverterfor CVCF operation

A new approach for digital feedback control of a PWM inverter is proposed, in which an output DB (deadbeat) control is achieved combined with a disturbance observer. In the proposed scheme, the pole placements of the state observer and the disturbance observer are chosen separately. When the two observers employed the same pole placements, the experimental setup had a tendency to become unstable due to the detection error. By selection of the different pole placements, the disturbance observer quickly estimates the disturbance and feedforward disturbance cancellation is achieved. Then the state observer estimates the state variables at the next sampling instant, and the deadbeat controller is applied to the nominal system. This scheme has advantages in robustness in the practical application. From the view point of UPS applications, the advantages and disadvantages are discussed through simulations and experiments. Compared with other digital control laws, the superiority of the proposed control law is verified     

一种基于自适应滤波的雷达目标跟踪算法

针对在复杂环境下基于卡尔曼滤波的雷达目标跟踪中存在的鲁棒性和自适应性较差的问题,研究了一种新的雷达目标自适应鲁棒跟踪算法;通过引入自适应渐消因子,对估计误差协方差和滤波增益矩阵进行在线自适应调整,从而使得滤波算法具备良好的鲁棒性和自适应性,提高雷达目标跟踪的精度。最后,通过仿真对所研究的方法进行了验证。     

一类多速率多传感器系统的状态融合估计算法

基于不同传感器以不同采样率对同一目标状态进行观测的多传感器单模型动态系统,该文提出了一种状态融合估计算法。不同传感器之间采样率之比可以是正有理数。该算法不仅具有好的实时性,而且在线性最小方差意义下是最优的。进一步可以证明:融合多个传感器获得的最高采样率下状态的估计值优于单传感器的估计结果,而减少任何一个传感器的信息所获得的估计值的误差协方差都将增大。仿真结果验证了算法的可行性与有效性。     

基于互相关协方差矩阵的改进多重信号分类高分辨波达方位估计方法

针对经典高分辨波达方位(DOA)估计方法在低信噪比下分辨性能较差的问题,该文提出一种适用于主动探测系统的基于互相关矩阵的改进多重信号分类(MUSIC)高分辨方位估计方法(I-MUSIC)。该方法首先利用主动声呐发射信号已知的特性,将发射信号与阵元接收信号进行互相关,利用互相关序列形成新的空域协方差矩阵,再进行特征分解。理论分析表明,互相关处理在抑制噪声的同时保留了阵元之间的相位信息,可以得到比MUSIC方法更准确的子空间划分,进而提高低信噪比方位估计性能。在此基础上,提出一种基于相关时间门限的改进MUSIC高分辨方位估计(T-MUSIC)方法,通过对互相关序列设置时间门限进一步提高方位估计信噪比。仿真结果表明,与MUSIC方法相比,I-MUSIC与T-MUSIC可以分别使低信噪比时的估计性能提高3 dB和6 dB,相应平均估计误差分别为原方法的77%和53%。在阵元间接收噪声存在相关性时,T-MUSIC与I-MUSIC方法相比可获得8 dB的估计增益,估计性能更优。I-MUSIC与T-MUSIC应用于多目标主动探测,可大幅提高探测系统在低信噪比下的方位估计性能。     

Simplified fast least-squares algorithms for speech predictors

The letter deals with the development of a simplified fast least-squares algorithm which is free of roundoff error accumulation. The simplified algorithm requires 9N MADPR (multiplications and divisions per recursion) rather than 10N MADPR as in the fast least squares or fast Kalman (FLS) case where N is the order of predictor. The superiority of SFLS over FLS and LMS approaches is illustrated by prediction gain performance curves for various speech signals     

On the residual correlation of finite-dimensional discrete Fourier transforms of stationary signals (Corresp.)

The covariance matrix of the Fourier coefficients ofN- sampled stationary random signals is studied. Three theorems are established. 1) If the covariance sequence is summable, the magnitude of every off-diagonal covariance element converges to zero asN rightarrow infty. 2) If the covariance sequence is only square summable, the magnitude of the covariance elements sufficiently far from the diagonal converges to zero asN rightarrow infty. 3) If the covariance sequence is square summable, the weak norm of the matrix containing only the off-diagonal elements converges to zero asN rightarrow infty. The rates of convergence are also determined when the covariance sequence satisfies additional conditions.     

On exact Kalman filtering of polynomial systems

A closed-form state estimator for some polynomial nonlinear systems is derived in this paper. Exploiting full Taylor series expansion we first give exact matrix expressions to compute mean and covariance of any random variable distribution that has been transformed through a polynomial function. An original discrete-time Kalman filtering implementation relying on this exact polynomial transformation is proposed. The important problem of chaotic synchronization of Chebyshev maps is then considered to illustrate the significance of these results. Mean square error between synchronized signals and consistency criteria are chosen as performance measures under various signal-to-noise ratios. Comparisons to the popular extended Kalman filter and to the recent unscented Kalman filter are also conducted to show the pertinence of our filtering formulation.     

倒立摆状态反馈极点配置与LQR控制Matlab实现

为了实现对绝对不稳定的非线性多变量倒立摆系统的控制,采用了状态反馈极点配置和LQR控制2种方法。状态反馈极点配置是将多变量系统的闭环系统极点配置在期望的位置上,从而使系统满足瞬态和稳态性能指标。LQR算法是在一定的性能指标下,利用最少的控制能量,来达到最小的状态误差。通过Matlab软件仿真实验,发现2种控制方法对于倒立摆这种不稳定的系统有一定的控制作用,证明了两种控制方案的可行性和有效性。仿真表明二次型最优控制有较小的振荡和超调量,对系统有更好的控制效果。     

基于空频域信息的改进UKF算法研究

固定单站无源定位系统面临着可观测性弱、初始误差大的问题,为了实现稳定高精度定位,在定位模型中引入角度变化率和多普勒频率变化率信息,并在此基础上提出了一种基于空频域信息的改进不敏卡尔曼滤波(UKF)算法.该算法利用两次观测时刻之间的间隔,根据当前时刻定位结果,通过后向平滑算法平滑估计前一时刻状态向量和协方差矩阵的估计值,...     

单级倒立摆控制系统的稳定性算法设计

为了对一级倒立摆这个非线形、强耦舍、多变量和自然不稳定系统的平衡性进行有效地控制,首先利用lagrange方程对系统进行了数学建模,设计了LQR控制器对其进行稳定性控制,并利用遗传算法优化加权矩阵,得出了比较理想的控制参数,最后利用Matlab对控制结果进行了仿真和分析。实验结果表明,LQR控制方法具有较强的鲁棒性和较好的控制效果。     

基于IMM-RDCKF的机动目标跟踪算法

针对机动目标跟踪问题,提出了一种IMM-RDCKF算法。首先充分利用量测方程中只有部分状态变量是非线性的特点,对于非线性的量测方程采用降维滤波方法,可以在保障跟踪精度条件下减小计算量。其次,对IMM算法中的转移概率矩阵进行实时估计,提高了模型匹配概率。再次,滤波过程中由于误差累积可能导致协方差矩阵失去正定性,对算法进行了优化,确保了滤波过程中协方差矩阵的正定性,提高了算法稳定性。Monte-Carlo仿真结果表明,与CKF算法相比,该算法的跟踪精度有明显的提高,计算效率提高了一倍。