Bayesian filter based on grid filtration and its application to Multi-UAV tracking
Bayesian filter based on grid filtration and its application to Multi-UAV tracking
复制标题
基于网格滤波的贝叶斯滤波器及其在多无人机跟踪中的应用
DOI：
10.1016/j.sigpro.2021.108305
复制
发表时间：
2022-01
影响因子：
4.4
通讯作者：
Yanbo Zhu
中科院分区：
文献类型：
--
作者：
Xingzi Qiang;Rui Xue;Yanbo Zhu
提出了一种基于贝叶斯推理的网格过滤滤波（GFF）方法。首先，根据置信参数α选择当前状态的高概率区域，并在该区域内均匀获取样本；其次，将这些样本视为离散化的“潜在状态”，并根据贝叶斯推理计算其后验权值。第三，采用网格过滤方法选择权重较高的“潜在状态”，用这些选择的“潜在状态”及其归一化权重表示后验分布，从而对状态进行估计。最后在一个典型的二维线性非高斯滤波场景中验证了GFF算法的可行性。结果表明，GFF算法的精度略高于粒子滤波（PF）算法，计算速度约为10000个粒子的PF算法的45倍。最后，我们在一个协同目标跟踪场景中验证了GFF算法。结果表明，GFF估计的精度略优于扩展卡尔曼滤波和无气味卡尔曼滤波，而速度和加速度的估计精度优势更为明显。
A filtering method called Grid Filtration Filter (GFF) is proposed based on Bayesian inference. First, we select the high-probability region of the current state according to the confidence parameter α, and obtain samples uniformly in this region. Second, these samples are regarded as discretized “potential states” and their posterior weights are calculated based on the Bayesian inference. Third, the grid filtration method is used to choose these “potential states” with high weights, and these selected “potential states” and their normalized weights are used to represent the posterior distribution, and thus estimate the state. we finally verify the feasibility of the GFF algorithm in a typical two-dimensional linear non-Gaussian filtering scenario. Results show that the GFF has a slightly better accuracy than the particle filter (PF) algorithm, and the calculation speed is better by a factor of approximately 45 compared with the PF with 10,000 particles. We finally validate the GFF algorithm in a collaborative target tracking scenario. Results show that the accuracy of the GFF estimation is slightly better than that of the extended Kalman filtering and that of the unscented Kalman filtering, while the advantage of the estimation accuracy of velocity and acceleration is more obvious.
登录
查看更多内容
DOI：
10.1109/9780470544334.ch9
发表时间：
2001
期刊：
Comput. Electron. Agric.
影响因子：
--
作者：
T. Başar
通讯作者：
T. Başar
影响因子：
5.4
作者：
Aditya Vempaty;L. Varshney;Gregory J. Koop;A. Criss;P. Varshney
通讯作者：
Aditya Vempaty;L. Varshney;Gregory J. Koop;A. Criss;P. Varshney
DOI：
10.1007/978-1-4757-3437-9
发表时间：
2001
期刊：
--
影响因子：
--
作者：
A. Doucet;Nando de Freitas;N. Gordon
通讯作者：
A. Doucet;Nando de Freitas;N. Gordon
DOI：
10.1155/s1110865704405149
发表时间：
2004-11-01
期刊：
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
影响因子：
--
作者：
Bolic, M;Djuric, PM;Hong, SJ
通讯作者：
Hong, SJ
影响因子：
20.6
作者：
Julier, SJ;Uhlmann, JK
通讯作者：
Uhlmann, JK