1) importance density
重要密度
1.
As the algorithm of particle weights, especially the importance density function, often severely affects the performance of the PF, we propose .
鉴于医学图像质量低、噪声大的普遍特点,从状态变量的非线性、非高斯分布前提出发,利用粒子滤波技术解决该类跟踪问题是一种具有挑战性的技术:由于经典粒子滤波器的权值计算,尤其是重要密度函数的构造方法严重影响了粒子滤波器的性能,本文提出了重要改进。
2.
We analyze the degeneracy phenomenon of sequential Monte Carlo particle filters based on bayesian theorem,put focus on the key techniques(good choice of importance density and use of resampling) to reduce its effects.
针对基于贝叶斯原理的序贯蒙特卡罗粒子滤波器出现退化现象的原因,以无敏粒子滤波(UPF)、辅助粒子滤波(ASIR)及采样重要再采样(SIR)等改进的粒子滤波算法为例,对消除该缺陷的关键技术(优化重要密度函数及再采样)进行了分析研究。
2) the importance density function
重要性密度函数
1.
Because the IEKF can acquire a maximum a posteriori(MAP) estimate of the nonlinear system,and the importance density function integrates the latest observation into system state transition density,so the proposal distribution can approximate the posterior distribution reasonably well.
该方法利用迭代扩展卡尔曼滤波的最大后验概率估计产生粒子滤波的重要性密度函数,使重要性密度函数能够融入最新观测信息的同时,更加符合真实状态的后验概率分布。
3) optimal importance density
最优重要密度
1.
Aiming at the constrained bearing only tracking(CBOT) s actual condition,the formula of optimal importance density(OID) used in particle filters is derived.
针对约束仅方位跟踪(CBOT)的实际条件,推导了粒子滤波器中的最优重要密度公式。
4) importance density function
重要性密度函数
1.
The main idea uses the system state transition matrix and the error covariance matrix which are gained from the IEKF and the sequential fusion to construct the importance density function of the particle filter.
针对粒子滤波中得到优化的重要性密度函数比较困难的问题,将迭代扩展卡尔曼滤波和序贯融合与粒子滤波相结合,应于雷达和红外多传感器目标融合跟踪。
2.
The choice of importance density function is very important for the particle filtering.
重要性密度函数的选择对粒子滤波至关重要,围绕重要性密度函数的选择,已提出许多改进粒子滤波算法,典型的有扩展卡尔曼粒子滤波(EPF),不敏卡尔曼粒子滤波(UPF)、辅助粒子滤波(APF)及正则化粒子滤波(RPF)。
3.
The new algorithm uses the quadrature Kalman filter(QKF) to generate the importance density function,and linearizes the nonlinear functions using the statistical linear regression method through a set of Gaussian-Hermite quadrature points.
针对非线性/非高斯系统的状态估计问题,提出一种采用求积分卡尔曼滤波(QKF)算法来产生重要性密度函数的粒子滤波新算法——PF-QKF算法。
5) the prior importance function
先验重要密度函数
1.
It is shown through simulations that the performance of a hybrid importance function is better than that of the prior importance function whether in Gaussian noise or in non-Gaussian noise.
当重要函数分别选取先验重要密度函数和混合重要密度函数时,对于平坦瑞利衰落信道下粒子滤波算法,仿真试验结果表明,无论是在高斯噪声还是非高斯噪声环境中,混合重要密度函数要优于先验重要密度函数。
6) the hybrid importance function
混合重要密度函数
补充资料：非密度制约因素(见密度制约因素)
非密度制约因素(见密度制约因素)
l焦非密度制约因素见生态因素、密度制约后
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条