Y2000-62000-737 0009979目标跟踪(含7篇文章)=Target Tracking[会,英]//1998 37th IEEE Conference on Decision and Control.Vol.1 of 4.—737～765(NiG)本部分收录7篇论文。内容包括采用自适应卡尔曼滤波器的机动目标跟踪,杂波中跟...Y2000-62000-737 0009979目标跟踪(含7篇文章)=Target Tracking[会,英]//1998 37th IEEE Conference on Decision and Control.Vol.1 of 4.—737～765(NiG)本部分收录7篇论文。内容包括采用自适应卡尔曼滤波器的机动目标跟踪,杂波中跟踪机动目标的马尔可夫链蒙特卡洛方法,具有导航不确定性的多目标跟踪,跟踪小目标采用随机采样数据的数据融合,参数和状态估计。展开更多
A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. p...A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. position, velocity and acceleration) even the sensor data(i.e. range, azimuth angle and elevation angle ) is color contaminated. The Kalman filter equations are decoupled by proper coordinate transformation and using filter gain rotation algorithm. Monto Carlo simulation is performed for different kinds of target trajectories(with the same measurement noise) and the root mean square values of estimation errors are computed. Results show that there is significant improvement in tracking capability over the methods discussed by other researchers.展开更多
文摘Y2000-62000-737 0009979目标跟踪(含7篇文章)=Target Tracking[会,英]//1998 37th IEEE Conference on Decision and Control.Vol.1 of 4.—737～765(NiG)本部分收录7篇论文。内容包括采用自适应卡尔曼滤波器的机动目标跟踪,杂波中跟踪机动目标的马尔可夫链蒙特卡洛方法,具有导航不确定性的多目标跟踪,跟踪小目标采用随机采样数据的数据融合,参数和状态估计。
文摘A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. position, velocity and acceleration) even the sensor data(i.e. range, azimuth angle and elevation angle ) is color contaminated. The Kalman filter equations are decoupled by proper coordinate transformation and using filter gain rotation algorithm. Monto Carlo simulation is performed for different kinds of target trajectories(with the same measurement noise) and the root mean square values of estimation errors are computed. Results show that there is significant improvement in tracking capability over the methods discussed by other researchers.
