一种新的混合高斯模型的学习算法

混合高斯模型是背景对消中一种非常有效的方法.本文提出了一种有效的混合高斯模型的学习算法.与以前的方法不同在于:a.根据最大似然准则,在线的更新模型的参数;b.定义了遗忘因子和学习率因子,并根据它们实际的物理含义,得到了更一般的形式.运用这种算法对模拟视频数据和真实视频处理,结果表明,本文提出的学习算法无论在收敛速率,还是在准确性方面,都要优于以前的方法.     

非线性非高斯模型的高斯和PHD滤波算法(英文)

A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density （GSPHD） filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussian sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaussian mixture probability hypothesis density （GMPHD） filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special case of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.     

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.     

基于GPR模型的自适应平方根容积卡尔曼滤波算法

与传统算法一样,动态系统的参数化模型(含噪声统计特性)未知或不够准确易导致容积卡尔曼滤波(CKF)效果严重下降,甚至滤波结果发散.为此,利用高斯过程回归(GPR)方法对训练数据进行学习,得到动态系统的状态转移GPR模型和量测GPR模型以及噪声统计特性,用以替代或增强原有动态系统模型,并将其融入到平方根容积卡尔曼滤波(SRCKF)中,分别提出了无模型高斯过程SRCKF (MFGP-SRCKF)和模型增强高斯过程SRCKF (MEGP-SRCKF)两种算法.仿真结果表明:这两种新的自适应滤波算法提高了动态系统模型精度,且实时自适应调整噪声的协方差,克服了传统算法滤波性能易受系统模型限制的问题;与MFGP-SRCKF相比,在给定一个不够准确的参数化模型,且有限的训练数据未能遍布估计状态空间的情况下,MEGP-SRCKF具备更高的滤波精度.     

基于故障树与马尔科夫分析模型的发动机系统安全性评估

安全性评估是发动机适航验证的重要方法之一,故障树与马尔科夫分析方法是安全性评估常用的方法,两种方法在安全性评估的不同时机具有各自的特点。针对故障树与马尔科夫在安全性评估中的应用时机进行了深入的研究,分析结果表明：对于失效率为常数的顺序相关的系统,若部件是主动失效部件(即姿伊t较小),应用故障树与马尔科夫分析方法均可准确评估系统的失效概率,采用故障树可以减少运算的复杂性;若部件是潜在失效部件,由于部件暴露时间较长(即姿伊t较大),使用马尔科夫的分析方法可以更精确的评估系统的安全性。     

Adaptive Detection Probabilities for Fluctuating Target Models in Nonhomogeneous Gaussian Noise

Under the assumption that the average noise power may vary from cell to cell, new, more easily computed expressions are given for the probability of detecting a fluctuating target by means of a cell-averaging CFAR test. The generalized chi-square family of fluctuating targets is considered with the Swerling I and III models given as special cases.     

Convolution Particle Filter for Parameter Estimation in General State-Space Models

The state-space modeling of partially observed dynamical systems generally requires estimates of unknown parameters. The dynamic state vector together with the static parameter vector can be considered as an augmented state vector. Classical filtering methods, such as the extended Kalman filter (EKF) and the bootstrap particle filter (PF), fail to estimate the augmented state vector. For these classical filters to handle the augmented state vector, a dynamic noise term should be artificially added to the parameter components or to the deterministic component of the dynamical system. However, this approach degrades the estimation performance of the filters. We propose a variant of the PF based on convolution kernel approximation techniques. This approach is tested on a simulated case study.     

收益管理系统中的几个关键模型

介绍了国内首例自主开发的收益管理系统中的部分运算模型，给出了需求预测、超售、座位优化控制中的一些核心算法。提出了基于C-均值聚类的航班预测模型、基于二项分布的超售计算模型、基于EMSR的多航段座位优化模型、基于期望边际收益的团体置换价模型等．并介绍了上述模型的运行效果。     

混合线性/非线性状态空间模型的边缘Rao-Blackwellized粒子滤波法(英文)

本文提出了边缘 Rao-Blackwellized 粒子滤波器(marginal Rao-Blackwellized particle filter, MRBPF)算法,算法融合了 Rao-Blackwellized 粒子滤波器(Rao-Blackwellized particle filter , RBPF)算法和边缘粒子滤波器(marginal particle filter, MPF)算法。算法中状态被分为线形和非线性两部分,分别用 MPF 和卡尔曼滤波器(Kalman Filter)进行估计。地形辅助导航(terrain aided navigation, TAN)的仿真结果表明,与 RBPF 相比,提出算法的非线性状态估计的误差均方根(root mean square error, RMSE)和误差方差分别降低了约 29%和 96%,独立粒子数提高了约80%,获得了更好的收敛结果。分析表明,现有RBPF是MRBPF的一个特例。     

The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models

In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.     

A Nonparametric Sequential Test for Data with Markov Dependence

A nonparametric sequential probability ratio test has been formed for dependent data. Operating characteristics are provided and their dependence upon the parameters is investigated. An example is given for second-order Markov dependence.     

用于微机械捷联式航姿系统的四元素算法卡尔曼滤波器

介绍了一种卡尔曼滤波器 ,它适用于由微机械惯性传感器构成的捷联式航姿系统。文中阐述了系统构成和原理 ,基于四元素算法公式推导了姿态算法和系统误差模型 ,并设计了实时卡尔曼滤波器。仿真结果表明 ,当没有横向加速度干扰时系统精度优于 0 .0 4度 ,当出现 0 .1g/1Hz的横向交变加速度干扰时 ,精度降为 0 .4 4度。初步测试结果表明系统的静态精度为 :俯仰和横滚 /-0 .2度 ,航向 /- 0 .3度。     

用于非线性跟踪问题的一种新的粒子滤波器

机动目标跟踪系统通常是非线性而且不完全观测的 ,所以问题的关键在于每一时刻的目标机动性都是高度不确定的。提出了一种新的平滑粒子滤波算法 ,该算法在粒子滤波器中加入了对系统模型的概率分布密度的平滑处理 ,从而很好的解决了目标的机动性估计问题。在仿真研究中 ,与辅助粒子滤波器的比较验证了本文算法处理非线性跟踪问题的优越性     

Adaptive Two-Stage Extended Kalman Filter for a Fault-Tolerant INS-GPS Loosely Coupled System

The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has an unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of the unknown bias may be more pronounced on the extended Kalman filter (EKF), which is a nonlinear filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. Recently, the optimal two-stage Kalman filter (TKF) for linear stochastic systems with a constant bias or a random bias has been proposed by several researchers. A TEKF can also be similarly derived as the optimal TKF. In the case of a random bias, the TEKF assumes that the information of a random bi?s is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper proposes an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF. To verify the performance of the proposed ATEKF, the ATEKF is applied to the INS-GPS (inertial navigation system-Global Positioning System) loosely coupled system with an unknown fault bias. The proposed ATEKF tracked/estimated the unknown bias effectively although the information about the random bias was unknown.     

空客A320飞机电子舱冷却系统空气滤更换周期的分析

通过测试A320系列飞机电子舱冷却系统空气滤在不同地区及不同使用时长下的阻力值,对测试结果进行分析,提出了该型空气滤工作寿命的影响因素,并推荐了该型空气滤新的更换周期。     

从系统仿真谈仿真模型的生成

随着计算机科学和系统科学的发展，系统仿真技术愈来愈为人们所关注。其应用范围遍及政治、经济、军事、交通运输、通讯、科学实验、教学及生产管理等社会各部门及领域。而构造仿真模型又是系统仿真中非常重要且必不可少的关键环节。本文对系统仿真模型的生成问题进行了探讨。     

Navigation Accuracy Category-Position Models and Estimate Position Uncertainty Calculations for TIS-B System

Navigation accuracy category-position (NACp) is an important parameter for system accuracy of traffic information service-broadcast (TIS-B), which is determined by estimate position uncertainty (EPU). Centered about the problems that the existing EPU calculation based on noise measurement is low in accuracy and unfit for describing uncorrected biases in target reports, this article analyses the traditional NACp model, and uses the least square estimation (LSE) in EPU calculation. Furthermore, it proposes an extended NACp model, which considers both noise and biases and acquires EPU estimation with the help of approximate multiplex Taylor expression. Analysis and simulation show that the proposed method not only leads to significant improvement of the accuracy of EPU calculation, but is fit for EPU calculation with tracking biases in TIS-B system as well. As such it can find application in practice to depict different kinds of error models in TIS-B system.     

飞行器气动力参数辨识的一种实用滤波误差方法

提出了一种观测噪声相对于过程噪声较小情况下气动参数识别的实用滤波误差方法，方法不要求已知系统过程噪声和观测噪声方差阵，计算量小，具有一定的应用价值。     

基于联合双边滤波的融合图像跟踪算法研究

为了提高跟踪精度,提出一种基于联合双边滤波的融合图像自动跟踪算法.采用帧问差分法并通过形态学处理获得运动目标大致区域,在此基础上采集目标区域及相邻背景区域的代表颜色集合,由此生成草图作为联合双边滤波的引导图,进而生成完整的目标描述,通过边缘检测获得精确的跟踪结果.实验结果表明,方法能够获得清晰准确的跟踪结果,能够较好地避免跟踪中的漂移问题,且算法复杂度低,跟踪精度高.