搜索资源列表
Radar_KalmanIMM6
- 交互多模算法，用于目标多机动假设运动情况下的蒙特卡罗法仿真跟踪滤波器。-interactive multi-mode algorithm for multi-objective maneuver under the assumption that the movement of the Monte Carlo simulation tracking filter.
PureangleofTrackingAlgorithm
- 通过实验仿真分析比较了目标在弱机动和强机动的条件下MGEKF算法和OAUKF算法的跟踪效果结果表明后者能无偏且更加有效地实现目标的跟踪 -The simulation analysis and comparison of the target in the weak mobility and strong mobility conditions MGEKF the tracking algorithm and OAUKF effect results show that the latter
maneuvering-target-TBD
- 我搜集的机动微弱目标检测前跟踪方面的一些经典论文-papers collected for naneuvering weak target track-before-detect
sanwei2
- 比例导引法跟踪做简单正弦机动目标的制导模型-Proportional navigation guidance tracks do simple sinusoidal model maneuvering target
zhidao
- 导弹跟踪正弦机动目标的制导过程，simulink模型-Guided missile tracking process sine maneuvering targets, simulink model
UKF
- 无迹卡尔曼滤波，适用于解决非线性机动目标的跟踪问题-Unscented Kalman filter, suitable for solving nonlinear maneuvering target tracking problem
IMMPF
- 交互式粒子滤波，用于解决不明确知道机动目标运动状态的目标跟踪问题-Interactive particle filtering for solving target tracking problems that do not explicitly know the state of motion of maneuvering targets
particle
- 利用C++语言，实现了粒子滤波算法，该算法可以用于机动目标的跟踪。(The particle filter algorithm is realized by using C++ language, and the algorithm can be used for maneuvering target tracking.)
Kalman
- 针对影响目标跟踪性能的关键因素——机动目标模型及其状态转移方程，探讨了基于Singer统计模型的卡尔曼滤波算法实现。(Aiming at the key factor that affects the performance of target tracking maneuvering target model and its state transition equation, the Calman filtering algorithm based on Singer statistical
ab改进
- 针对传统α － β 滤波算法不够有效跟踪机动目标的问题， 详细分析了其内在原因， 提出一种改进的α － β 滤波算法。该算法不需要假定目标的机动模型， 而是将目标的机动加速度作为滤波状态直接估计出来， 将估计加速度作为输入控制量引入到传统α － β 滤波器的状态估计方程中进行机动目标的跟踪。然后将它与传统α － β 滤波算法进行比较， 证明了新的算法不仅具有传统算法计算量小的优点， 而且还可以对机动目标进行实时跟踪。仿真结果表明， 新算法在综合性能上明显优于传统算法。(In view of th
IMM_f
- 采用交互多模型算法（IMM）对机动目标进行跟踪的实现(Interactive multiple model algorithm (IMM) is applied to track maneuvering target.)