1) MGEKF
修正增益的扩展卡尔曼滤波
1.
By establishing maneuvering target model and measurement formula,the location of maneuvering target is practicable with the MGEKF algorithms.
在建立目标机动模型与测量方程的基础上,运用修正增益的扩展卡尔曼滤波(MGEKF)算法,实现对机动目标进行定位与跟踪。
2) AMGEKF(Adaptive Modified Gain Extended Kalman Filter)
自适应修正增益的扩展卡尔曼滤波
3) MGEKF
修正增益扩展卡尔曼滤波
1.
The Modified Gain EKF(MGEKF)algorithm for passive localization of maneuvering target by single station is discussed.
在建立目标机动模型与测量方程的基础上,运用修正增益扩展卡尔曼滤波(MGEKF)算法,实现对机动目标进行定位与跟踪。
2.
The paper presents a single observer passive location algorithm which is based on azimuth and elevation angle and TDOA measurement and adds the changing rates of azimuth angle,meanwhile,introduces a filtering algorithm which is good for nonlinear system,modified gain extended Kalman filter(MGEKF) algorithm.
同时引入一种对非线性系统较好的滤波算法——修正增益扩展卡尔曼滤波(MGEKF)算法,与推广卡尔曼滤波器(EKF)相比,MGEKF能更好地解决量测模型非线性问题,滤波性能更好。
3.
The modified gain EKF(MGEKF)algorithm for passive localization of maneuvering target by single station is discussed.
在建立目标机动模型与测量方程的基础上,运用修正增益扩展卡尔曼滤波(MGEKF)算法,实现对机动目标进行定位与跟踪,讨论了其定位原理与算法,计算机仿真验证了该方法的正确性与有效性。
4) MGEKF
修正增益卡尔曼滤波
5) MEKF
修正扩展卡尔曼滤波
1.
Modified extend Kalman filter(MEKF) is simple yet very effective in accounting for the measurement of non-linearity.
针对基于扩展卡尔曼滤波的融合算法存在滤波精度不高的问题,将修正扩展卡尔曼滤波算法与集中式序贯融合算法相结合,用于毫米波雷达和红外传感器目标融合跟踪。
2.
Since the traditional Kalman Filter fusion has relative low filtering accuracy,Modified Extended Kalman Filter(MEKF) was used for fusion of measurements of radars for estimating the target state.
传统扩展卡尔曼滤波融合算法滤波精度不高,因此先利用雷达传感器的量测,采用修正扩展卡尔曼滤波算法对目标状态进行估计,再把估计值作为红外传感器的预测值进行序贯融合。
6) time varying sampling MGEKF
时变采样间隔的修正增益推广卡尔曼滤波
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