针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题,提出一种自适应交互多模型无迹卡尔曼滤波(interacting multiple model unscented Kalman filter,IMM-UKF)算法,使用模型概率后验信息和模型似然函数自适...针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题,提出一种自适应交互多模型无迹卡尔曼滤波(interacting multiple model unscented Kalman filter,IMM-UKF)算法,使用模型概率后验信息和模型似然函数自适应修正马尔可夫转移概率矩阵(transition probability matrix,TPM)。设计模型概率校正方法和模型转移加速方法,两种方法分别作用于模型稳定阶段和模型转移阶段,提高模型概率准确度和模型转移响应速度,减小状态估计误差。最后,通过两种场景下的实验验证所提算法在目标具有复杂运动状态下的性能,并与传统方法进行对比分析,在目标做机动运动时,位置精度和速度精度分别提高了15%和26%,验证了算法的有效性和可行性。展开更多
In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the PO...In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.展开更多
针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM...针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM-PF)算法,获得环境中目标车辆的运动状态;其次,协同车通过车车通信将跟踪到的目标状态发送给主车;最后,利用基于匈牙利算法和快速协方差交叉算法的数据关联和数据融合技术实现多机动目标的协同跟踪.搭建了V2V通信、雷达和定位仿真系统,选定两辆智能车作为主车和协同车,感知并跟踪200 m范围内的7辆目标车,进行了仿真试验.结果表明,与传统的单车跟踪相比,协同跟踪扩大了感知范围,且在不影响跟踪效率的情况下使跟踪误差降低了31.1%.展开更多
文摘针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题,提出一种自适应交互多模型无迹卡尔曼滤波(interacting multiple model unscented Kalman filter,IMM-UKF)算法,使用模型概率后验信息和模型似然函数自适应修正马尔可夫转移概率矩阵(transition probability matrix,TPM)。设计模型概率校正方法和模型转移加速方法,两种方法分别作用于模型稳定阶段和模型转移阶段,提高模型概率准确度和模型转移响应速度,减小状态估计误差。最后,通过两种场景下的实验验证所提算法在目标具有复杂运动状态下的性能,并与传统方法进行对比分析,在目标做机动运动时,位置精度和速度精度分别提高了15%和26%,验证了算法的有效性和可行性。
基金supported by the Aeronautical Science Foundation of China(20135153031 20135553035 2017ZC53033)
文摘In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.
文摘针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM-PF)算法,获得环境中目标车辆的运动状态;其次,协同车通过车车通信将跟踪到的目标状态发送给主车;最后,利用基于匈牙利算法和快速协方差交叉算法的数据关联和数据融合技术实现多机动目标的协同跟踪.搭建了V2V通信、雷达和定位仿真系统,选定两辆智能车作为主车和协同车,感知并跟踪200 m范围内的7辆目标车,进行了仿真试验.结果表明,与传统的单车跟踪相比,协同跟踪扩大了感知范围,且在不影响跟踪效率的情况下使跟踪误差降低了31.1%.
