Adaptive Kalman filter algorithm study (1) Based on the study of the passive target tracking in modified polar coordinates, the nonlinear dynamic model is devised.
自适应滤波算法(1)研究了极坐标系下的水下目标被动跟踪问题，建立了被动跟踪的动力学模型。
The paper describes an Adaptive Interacting Multiple-model(AIMM) algorithm for use in manoeuvring target tracking.
本文介绍了用于机动目标跟踪的自适应混合多模算法。
This paper proposes one kind of adaptive IMM algorithm based on kinematics parameters used for tracking the high speed and high maneuvering target.
提出一种基于运动学参数的自适应IMM算法，进行高速高机动目标跟踪。
The adaptive grid Interacting Multiple Model algorithm can adjust model parameter adaptively according to the target's mode, and thus realizes target tracking.
自适应网格交互多模算法能够根据目标的运动模式，自适应调整系统各模型参数，从而实现对目标的跟踪。
Concerning the problem to instability and low accuracy of the passive filter on bearings-only target tracking, a modified adaptive Extended Kalman filter algorithm on polar coordinate is presented.
在水下被动目标跟踪系统中，直角坐标系下的扩展卡尔曼滤波器容易发散而导致滤波精度很差。
Maneuvering target tracking algorithm based on adaptive Gauss model and EKF was built for improving the tracking performance in Multi-static systems.
为改善多基地雷达系统对高机动目标的跟踪性能，提出了基于自适应高斯模型和扩展卡尔曼滤波(ekf)的机动目标跟踪算法。
After preprocessing the sampled real-time image sequences, moving target tracking is realized by combining improved Ostu algorithm and adaptive window tracing method.
在对采集的实时序列图像进行滤波处理后，本文通过改进最大类间方差法，并将其和自适应波门跟踪方法结合起来实现运动目标的跟踪。
Concerning the problem of instability and low accuracy of passive filter in underwater target tracking, a modified adaptive extended kalman filter (MAEKF) algorithm is presented.
针对在被动方式下进行水下目标跟踪容易导致滤波发散和收敛精度不高的问题，介绍了一种改进的自适应推广卡尔曼滤波算法。
Concerning the problem of instability and low accuracy of passive filter in underwater target tracking, a modified adaptive extended kalman filter (MAEKF) algorithm is presented.
针对在被动方式下进行水下目标跟踪容易导致滤波发散和收敛精度不高的问题，介绍了一种改进的自适应推广卡尔曼滤波算法。
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