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modelbasedonspectrumprediction
- 文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一,儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based on Gaussian mixture models. Spec
Unsupervised_Adapting_in_Speech_Recognising_using_
- 介绍了一种基于词网的最大似然线性回归无监督自适应算法,并进行了改进。根据解码得到的词网估计变换参数,词网的潜在误识率远小于识别结果,因此可以使参数估计更为准确。传统的一个很大缺点是计算量极大,较难实用,对此本文提出了两个改进技术:1利用后验概率压缩词网;2利用单词的时间信息限制状态统计量的计算范围。实验测定,误识率比传统相对下降了。-Introduced the term network based maximum likelihood linear regression unsupervise
kamansuanfa
- 其基本思想是:采用信号与噪声的状态空间模型,利用前一时刻地估计值和现时刻的观测值来更新对状态变量的估计,求出现时刻的估计值。它适合于实时处理和计算机运算。 -The basic idea is: use of signal and noise state-space model, the estimated value of the previous time and are always observations to update the estimated state variable
KF
- 一种基于运动模型的扩展卡尔曼滤波(EKF)算法,该方法适用于任何能用状态空间模型表示的非线性系统,精度可以逼近最优估计.-an EKF positioning and tracking algorithm based on kinematic model. This method can apply to any state-space model which is the nonlinear system, and the accuracy can approach to best of al
kalman
- 卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全包含噪声的测量(英文:measurement)中,估计动态系统的状态。本程序实现了基于kalman的目标跟踪。-Kalman filter is an efficient recursive filter (autoregressive filter), it can not completely contain from a series of noise measurements (in English: measu
EulerFHN1
- 自适应控制的方法估计动力系统参数,把待估参数作为一个状态变量来处理-parameter estimator adaptive control
AnAdaptiveFourDimensionalKalmnFilteforDopplerFrequ
- 用泰勒级数展开的形式表示高动态的载波相位参数, 给出了对高动态载体和各阶频率参数 估计的四阶加权扩展卡尔漫滤波器(EKF) , 以及实现高动态跟踪滤波器必须的状态转移矩阵和动 态噪声协方差矩阵. 计算机模拟实验分析了对载波相位和各阶频率的跟踪结果.-Taylor series expansion with the form that the carrier phase high-dynamic parameters, given the high-order dynamic freq
KalmFilter
- 卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全包含噪声的测量中,估计动态系统的状态。-Kalman filter is an efficient recursive filter (autoregressive filter), it can not completely contain from a series of noise measurements, the estimated dynamical systems.
63931979MIMO_OFDM
- 在理想的信道状态条件下,对MIMO-OFDM系统进行仿真,包括信道估计,调制,解调,信道编解码等-In an ideal channel state conditions, the MIMO-OFDM system is simulated, including channel estimation, modulation, demodulation, channel coding and decoding
kuozhankaermanlvboqi
- 卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全包含噪声的测量(英文:measurement)中,估计动态系统的状态。-Kalman filter is an efficient recursive filter (autoregressive filter), which can contain from a series of incomplete measurement of noise (in English: measurement) is estimate
ukf
- EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分(忽略高阶项),常常导致在状态的后验分布的估计上产生较大的误差,影响滤波算法的性能,从而影响整个跟踪系统的性能。最近,在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器(Unscented Kalman Filter-UKF)。UKF的思想不同于EKF滤波,它通过设计少量的σ点,由σ点经由非线性函数的传播,计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性,从而比EKF滤波具有更高的估计精
0815109
- 分析并设计kalman滤波器,应用其对航天器得飞行状态量进行实时估计,得到较为准确的状态参量,-Kalman filter analysis and design, application of their spacecraft in real time the amount was estimated flight to get a more accurate state parameter,
JKalman-from-SourceForge.net
- JKalman是一个开源的Java执行卡尔曼滤波。卡尔曼滤波器是一个有效率的计算(递归)工具来估计过程的动态状态中的一种方式,最大限度地减少了错误。 -JKalman is an Open Source Java implementation of Kalman filter. Kalman filter is an efficient computational (recursive) tool to estimate the dynamic state of a process in a
AStar
- A*(A-Star)算法是一种静态路网中求解最短路最有效的方法。公式表示为: f(n)=g(n)+h(n),其中f(n) 是从初始点经由节点n到目标点的估价函数,g(n) 是在状态空间中从初始节点到n节点的实际代价,h(n)是从n到目标节点最佳路径的估计代价。 -A* (A-Star) is a static network algorithm for solving the shortest most effective way. Formula is expressed as: f (n)
nm
- 摘 要: 为缩短数字视频广播卫星标准( DVB-S ) 接收系统中内码信息的估计时间, 提出了两套解决方案. 方案一: 根据维持比译码过程中, 从当前时刻回溯到译码深度以前时刻的各条幸存路径基本重合的原理进行判定 方案二: 利用“二次编码”的特性, 对译码数据进行再编码, 并采用置信度结合有限状态机构成的判定机制, 加快了估计.-Abstract: In order to reduce the Digital Video Broadcast Satellite standard (DVB
ekfukf
- 非线性滤波方法,主要包括EKF(扩展卡尔曼滤波)与UKF(无迹卡尔曼滤波),对于非线性状态、参数估计的学习有很大的帮助-Nonlinear filtering methods, including EKF (EKF) and UKF (unscented Kalman filter) for nonlinear state estimation is very helpful in learning
kalman-lb_zsy
- 由于Kalman 滤波算法将被估计的信号看作在白噪声作用下——个随机线性系统的输出, 并且其输入输出关系是由状态方程和输出方程在时间域内给出的, 因此这种滤波方法-Because of Kalman filtering algorithm will be estimated as the signal in white noise-a random under the action of linear systems, and its output input and output relati
Eural-fermentation-process
- 欧拉法求解酵母发酵过程机理模型,估计4个状态变量-Eural;fermentation process;Mechanism model
Kalmenf-filters
- 文章介绍了卡尔曼滤波的原理,包括状态方程、过程估计、噪声原理等,并通过matlab予以实现验证。-This paper introduces the principle of Kalman filtering, including the equation of state, the process of estimation, noise theory, etc., and through matlab to achieve validation.
yiweilizilvbo
- 一维粒子滤波算法先建立状态模型和量测模型,再构造粒子滤波函数,通过统计分析得到估计后的状态变量,就是该算法的基本原理-One-dimensional particle filter algorithm to first establish a state model and measurement model, and then construct the particle filter function to be estimated by statistical analysis after