1) 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。
2) Modified covariance extended Kalman filter
修正协方差扩展卡尔曼滤波
3) MVEKF(Modified coVariance Extended Kalman Filter)
MVEKF(修正协方差扩展卡尔曼滤波)
4) 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。
5) AMGEKF(Adaptive Modified Gain Extended Kalman Filter)
自适应修正增益的扩展卡尔曼滤波
6) 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。
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