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1. (CN103729637) 基于容积卡尔曼滤波的扩展目标概率假设密度滤波方法

专利局 : 中国
申请号: 201310753640.5 申请日: 31.12.2013
公布号: 103729637 公布日: 16.04.2014
授权号: 103729637 授权日: 11.01.2017
公布类型: B
国际专利分类:
G06K 9/32
G PHYSICS
06
计算;推算;计数
K
数据识别;数据表示;记录载体;记录载体的处理
9
用于阅读或识别印刷或书写字符或者用于识别图形,例如,指纹的方法或装置
20
图像捕获
32
图像拾取或图像分布图的对准或中心校正
申请人: XI'AN POLYTECHNIC UNIVERSITY
发明人: MA LILI
WANG NI
CHEN JINGUANG
HU XIMIN
代理人: luo di
优先权数据:
标题: (EN) Extended target probability hypothesis density filtering method based on cubature Kalman filtering
(ZH) 基于容积卡尔曼滤波的扩展目标概率假设密度滤波方法
摘要: front page image
(EN) The invention discloses an extended target probability hypothesis density filtering method based on cubature Kalman filtering. The method comprises the steps of (1) pre-setting the Gaussian mixture form of posterior strength of the moment k-1 and obtaining the mean value and covariance of the ith Gaussian item, (2) conducting one-step prediction on the weight, mean value and covariance of the ith Gaussian item obtained from the first step, and (3) conducting measurement updating on the prediction result obtained from the second step to obtain the estimated value of each Gaussian component (as stated in the specification) of the moment k. According to the extended target probability hypothesis density filtering method based on cubature Kalman filtering, extended target tracking is achieved under a nonlinear system, extended target tracking is achieved when the Jacobian matrix of a nonlinear function does not exist or is hard to solve, and a new realization approach is provided for extended target tracking under nonlinear conditions.
(ZH) 本发明公开的基于容积卡尔曼滤波的扩展目标概率假设密度滤波方法,具体按照以下步骤实施:步骤1、预先设定k‑1时刻后验强度的高斯混合形式,得到第i个高斯项的均值和协方差;步骤2、对步骤1得到的第i个高斯项的权值、均值和协方差进行一步预测:步骤3、根据步骤2得到的预测结果进行量测更新,得到k时刻各高斯分量的估计值。本发明的基于容积卡尔曼滤波的扩展目标概率假设密度滤波方法,解决非线性系统下的扩展目标跟踪问题和非线性函数的雅克比矩阵不存在或难以求解时的扩展目标跟踪问题,为解决非线性条件下的扩展目标跟踪提出了一种新的实现途径。