1) Polynomial Predictive Filter-Kalman Filter(PPF-KF)
多项式预测滤波-卡尔曼滤波(PPF-KF)
2) Kalman filter(KF)
卡尔曼滤波(KF)
1.
The simulation results showed that the performance of the proposed algorithm and the standard Kalman filter(KF) algorithm are similar in low dynamic situation,while the systematic biases and the random errors of the proposed algorithm are significantly smaller than the standard KF algorithm in high dynamic situations.
该方法通过增加观测量在状态估计中的权重,大幅降低加速度引起的距离、速度跟踪偏差,从而有效地抑制标准卡尔曼滤波(KF)算法在跟踪高动态目标过程中产生的滤波发散现象。
3) kalman filtering prediction
卡尔曼滤波预测
1.
This thesis researches on mainly two aspects,obstacle-avoiding of mobile robot in real-time and the kalman filtering prediction of the state of obstacles in dynamic uncertain environments.
本文的研究工作主要是针对动态不确定环境下,移动机器人的实时动态避障和对障碍物状态信息的卡尔曼滤波预测两个方面的研究展开的。
4) polynomial FIR predictors
多项式预测滤波
1.
Identification of sensor s dynamic characteristic based on denoising by polynomial FIR predictors;
用多项式预测滤波消噪的传感器动态特性辨识
5) Kalman prediction filter
卡尔曼预测滤波器
6) kalman filtering and estimation
卡尔曼滤波和预测
1.
In this paper,we propose a multiple acoustic source tracking method using steered beamfomer based on kalman filtering and estimation,which integrate voice activity detection,and establish status model and evaluation model for each moving source.
针对传统的可控波束声源跟踪方法运算量大,难以实时处理的缺点,提出了一种基于卡尔曼滤波和预测的可控波束多声源跟踪方案,集成了语音活动检测,为运动声源分别建立状态模型和观测模型。
补充资料：卡尔曼滤波
见波形估计。
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