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1.
研究了势平衡多目标多伯努利(cardinality balanced multi-target multi-Bernoulli,CBMeMBer)滤波器高斯混合(Gaussian mixture,GM)实现的收敛性问题.证明在线性高斯条件下,若GM-CBMeMBer滤波器的高斯项足够多,则它一致收敛于真实的CBMeMBer滤波器.并且证明在弱非线性条件下,GM-CBMeMBer滤波器的扩展卡尔曼(extended Kalman,EK)滤波近似实现—EK-GM-CBMeMBer滤波器,若每个高斯项的协方差足够小,也一致收敛于真实的CBMeMBer滤波器,本文的研究目的是从理论上给出CBMeMBer滤波器GM实现的收敛结果,以完善CBMeMBer滤波器对多目标跟踪的理论研究.  相似文献   

2.
多模型GM-CBMeMBer滤波器及航迹形成   总被引:1,自引:0,他引:1  
连峰  韩崇昭  李晨 《自动化学报》2014,40(2):336-347
提出了一种可适用于杂波环境下对多个机动目标进行跟踪并能形成多目标航迹的多模型势平衡多目标多伯努利(Cardinality balanced multi-target multi-Bernoulli,CBMeMBer)滤波器. 随后,在多机动目标时间演化模型和观测模型均为线性高斯的假设条件下利用高斯混合(Gaussian mixture,GM)技术获得了该滤波器解析的递推形式——-多模型 GM-CBMeMBer 滤波器,并简要给出了它在非线性条件下的扩展卡尔曼(Extended Kalman,EK)滤波近似. 仿真实验结果表明所建议的多模型 GM-CBMeMBer 滤波器能有效地对多个机动目标进行跟踪而单模型 GM-CBMeMBer 滤波器则会产生明显的航迹丢失和虚假航迹,并且对于信噪比较低的仿真场景,它的性能优于多模型高斯混合概率假设密度(GM probability hypothesis density,GM-PHD)滤波器,接近于多模型高斯混合势概率假设密度(GM cardinalized PHD,GM-CPHD)滤波器.  相似文献   

3.
孟敏  李修贤 《控制理论与应用》2022,39(10):1969-1977
原始-对偶梯度算法广泛应用于求解带约束的凸优化问题, 大部分文献仅证明了该算法的收敛性, 而没有分析其收敛速度. 因此, 本文研究了求解带有不等式约束凸优化的一类离散算法, 即增广原始-对偶梯度算法 (Aug-PDG), 证明了Aug-PDG 算法在一些较弱的假设条件下可以半全局线性收敛到最优解, 并明确给出了算法中步长的上界. 最后, 数值算例证实了所得理论结果的有效性.  相似文献   

4.
为分析四元数卡尔曼滤波组合导航算法在飞行器姿态估计中的性能,在建立四元数卡尔曼滤波观测方程、状态方程和方差计算模型的基础上,分别设计了陀螺/加速度计/磁强计组合导航仿真算例和陀螺/加速度计初始对准实验,比较了四元数卡尔曼滤波组合导航算法相较于传统扩展卡尔曼滤波组合导航算法在计算量、收敛性、收敛速度、收敛精度方面的性能.分析结果表明该滤波器无须扩展卡尔曼滤波器的线性化过程,计算量小,算法实现简单;收敛性和收敛速度均优于扩展卡尔曼滤波器.收敛精度较扩展卡尔曼滤波器高出约两个数量级,但收敛过程中存在一个比扩展卡尔曼滤波器精度低的时间区间.  相似文献   

5.
对于一类具有动态和静态关联项的大系统,利用MT-滤波器和反推设计方法设计了一种鲁棒分散自适应输出反馈控制器.在各子系统为最小相位、静态关联项满足Lipschitz条件、动态关联项稳定且严格正则的假设下,证明了闭环系统的所有信号全局一致有界,且除了参数估计外的其他信号皆以指数速率收敛到零,该结果好于其他相关文献中的结果,仿真结果进一步验证了该方法的有效性。  相似文献   

6.
狄昂照 《自动化学报》1985,11(3):251-257
本文研究了带限制的随机递推算法,并在一定条件下证明了这种算法的几乎处处收敛 性、均方收敛性以及平均收敛性.  相似文献   

7.
张光华  韩崇昭  连峰  曾令豪 《自动化学报》2017,43(12):2100-2108
由于在实际应用中目标模型不一定满足隐马尔科夫模型(Hidden Markov model,HMM)隐含的马尔科夫假设和独立性假设条件,一种更为一般化的Pairwise马尔科夫模型(Pairwise Markov model,PMM)被提出.它放宽了HMM的结构性限制,可以有效地处理更为复杂的目标跟踪场景.本文针对杂波环境下的多目标跟踪问题,提出一种在PMM框架下的势均衡多目标多伯努利(Cardinality balanced multi-target multi-Bernoulli,CBMeMBer)滤波器,并给出它在线性高斯PMM条件下的高斯混合(Gaussian mixture,GM)实现.最后,采用一种满足HMM局部物理特性的线性高斯PMM,将本文所提算法与概率假设密度(Probability hypothesis density,PHD)滤波器进行比较.实验结果表明本文所提算法的跟踪性能优于PHD滤波器.  相似文献   

8.
针对多扩展目标跟踪中的传感器控制问题, 本文基于有限集统计(FISST)理论与随机超曲面模型(RHM), 利 用多伯努利(MBer)滤波器提出有效的传感器控制策略. 首先, 文中给出多扩展目标跟踪中基于信息论联合目标形状 估计优化和目标运动状态估计优化的传感器控制方法的求解思路. 其次, 给出RHM容积卡尔曼高斯混合(GM)势均 衡多扩展目标多伯努利滤波算法的具体实现过程. 然后, 结合GM密度间的柯西施瓦兹(Cauchy-Schwarz)散度提出 相应的传感器控制决策方法. 此外, 详细推导了扩展目标势的后验期望(PENET)的GM实现, 并提出以GM–PENET 为评价函数的传感器控制方法. 最后, 通过构造随机星凸形多扩展目标的跟踪优化仿真实验验证了本文所提传感 器控制方法的有效性.  相似文献   

9.
一类基因表达式程序设计的若干收敛定理及其推广   总被引:1,自引:0,他引:1  
基因表达式编程算法(或称基因表达式程序设计)的基因型/表现型双实体为之带来许多不同于传统演化算法的优势,但建立其Markov模型时,我们须在两者之间作出权衡.为简化遗传算子概率结构的分析,本文以基因型空间为搜索空间,研究一类GEP在宽松条件下的收敛性.首先,针对由基因型-表现型映射所致的多峰适应值函数,重构带精英保留策略的GEP的Markov链模型转移矩阵.然后,通过建立依概率收敛速度的精确表达式、估计其上界,证明了算法依均值收敛、几乎必然收敛甚至完全收敛至全局最优值.与之前的严格假设下的若干结论相比,本文的模型更匹配算法的特性,收敛性结论更强且最优状态子集更小.另外,上述精确表达式也可以推广至自适应演化算法.  相似文献   

10.
在迭代学习控制理论的收敛性分析中,常见的初始条件是迭代初值与期望初值一致,或者迭代初值固定,给出了一类含控制时滞非线性时变系统在任意初值条件下采用开环PD型迭代学习控制算法时的收敛条件.迭代学习采用控制输入与初值同时学习的算法,其中控制输入利用了给定超前法,该算法解决了控制时滞和初值问题.运用算子理论证明了收敛条件,给出了间歇非线性控制时滞过程仿真实例,研究结果说明了该算法的有效性.  相似文献   

11.
For a linear time-invariant system model, this paper analyzes the convergence of parameter estimations as the length of the input–output data tends to infinity through the prediction error method. It is known that the sequence of the criterion functions converges uniformly in the parameter with probability one as the data length tends to infinity. The parameter estimation is represented by a set in general, instead of by a single point, on which the criterion function takes its minimum. Thus a mathematical feature of the convergence problem of parameter estimation is in that we are needed, from the convergence of a sequence of functions, to infer the convergence of the sequence of their sets of minimizing arguments. The Hausdorff metric is suggested to measure the distance between sets and then is used to discuss the convergence problem here. According to the Hausdorff metric, the convergence of parameter estimation is not guaranteed in general. A condition guaranteeing such convergence is given.  相似文献   

12.
In this paper, an extended risk-sensitive filter (ERSF) is used to estimate the motion parameters of an object recursively from a sequence of monocular images. The effect of varying the risk factor &thetas; on the estimation error is examined. The performance of the filter is compared with the extended Kalman filter (EKF) and the theoretical Cramer-Rao lower bound. When the risk factor &thetas; and the uncertainty in the measurement noise are large, the initial estimation error of the ERSF is less than that of the corresponding EKF The ERSF is also found to converge to the steady state value of the error faster than the EKF. In situations when the uncertainty in the initial estimate is large and the EKF diverges, the ERSF converges with small errors. In confirmation with the theory, as &thetas; tends to zero, the behavior of the ERSF is the same as that of the EKF  相似文献   

13.
A recursive algorithm for estimating the constant but unknown parameters of a controlled ARMA process is presented. The algorithm is a recursive version of an off-line algorithm using three stages of standard least-squares. In the first stage the parameters of a controlled AR model of degree p are estimated. The residuals used in this stage are employed in the second stage to estimate the parameters of a controlled ARMA process. The first two stages constitute a recursive version of Durbin's algorithm. The model obtained in the second stage is used to filter the input, output and residuals and these filtered variables are used in the final stage to obtain improved estimates of the controlled ARMA process. It is shown that the estimate is (globally) p-consistent, i.e. that the estimate converges a.s. as the number of data tends to infinity, to a vector which, in turn, converges to the true parameter vector as the degree p of the AR model tends to infinity.  相似文献   

14.
基于卷积神经网络(CNN)的入侵检测方法在实际应用中模型训练时间过长、超参数较多、数据需求量大。为降低计算复杂度,提高入侵检测效率,提出一种基于集成深度森林(EDF)的检测方法。在分析CNN的隐藏层结构和集成学习的Bagging集成策略的基础上构造随机森林(RF)层,对每层中RF输入随机选择的特征进行训练,拼接输出的类向量和特征向量并向下层传递迭代,持续训练直至模型收敛。在NSL-KDD数据集上的实验结果表明,与CNN算法相比,EDF算法在保证分类准确率的同时,其收敛速度可提升50%以上,证明了EDF算法的高效性和可行性。  相似文献   

15.
改进的最小均方自适应滤波算法   总被引:1,自引:0,他引:1  
汪成曦  刘以安  张强 《计算机应用》2012,32(7):2078-2081
针对传统的固定步长最小均方(LMS)算法应用于雷达杂波自适应滤波器系统存在收敛速度与收敛精确度相矛盾的问题,提出一种新的变步长LMS自适应滤波算法。在其基础步长迭代公式中,通过组合自相关误差与前一步长因子来实时更新迭代下一步长因子的方法,达到具有较快的收敛速度和较小的失调,并且不受已经存在的不相关噪声的干扰的效果。仿真结果表明,所提方法的实验效果与传统固定步长LMS算法及已有算法相比,在收敛速率、收敛精度、抑制噪声方面都有很大的改善,证明所提算法是有效、可行的,且与理论分析一致。  相似文献   

16.
Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.  相似文献   

17.
An ordinary differential equation technique is developed via averaging theory and weak convergence theory to analyze the asymptotic behavior of continuous-time recursive stochastic parameter estimators. This technique is an extension of L. Ljung's (1977) work in discrete time. Using this technique, the following results are obtained for various continuous-time parameter estimators. The recursive prediction error method, with probability one, converges to a minimum of the likelihood function. The same is true of the gradient method. The extended Kalman filter fails, with probability one, to converge to the true values of the parameters in a system whose state noise covariance is unknown. An example of the extended least squares algorithm is analyzed in detail. Analytic bounds are obtained for the asymptotic rate of convergence of all three estimators applied to this example  相似文献   

18.
本文讨论带遗忘因子的最小二乘法估计传递函数的误差硬界及其渐近性质.在噪声有界 等一定假定下,当样本个数趋向无限时,误差硬界收敛于复平面内的一个圆.  相似文献   

19.
In this paper, we propose an adaptive fuzzy dynamic surface control (DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity" problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated, which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.   相似文献   

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