Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent perfo...Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.展开更多
A new fusion tracking algorithm is presented to track maneuvering target in three-dimensional (3D) space with bearings-only measurements. With the introduction of passive location and interacting multiple model (IMM) ...A new fusion tracking algorithm is presented to track maneuvering target in three-dimensional (3D) space with bearings-only measurements. With the introduction of passive location and interacting multiple model (IMM) algorithm based on multirate model, the high-rate sequence measurements of two sensors are utilized. Simulation results show that the performance of tracking has been improved. The new algorithm removes the barrier of processing high-rate bearings-only measurements.展开更多
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
The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a ta...The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.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
基金supported by the Fundamental Research Funds for the Central Universities(NJ20140010)the Scientific Research Start-up Funding from Jiangsu University of Science and Technology+1 种基金the Scienceand Technology on Electronic Information Control Laboratory Projectthe Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.
文摘A new fusion tracking algorithm is presented to track maneuvering target in three-dimensional (3D) space with bearings-only measurements. With the introduction of passive location and interacting multiple model (IMM) algorithm based on multirate model, the high-rate sequence measurements of two sensors are utilized. Simulation results show that the performance of tracking has been improved. The new algorithm removes the barrier of processing high-rate bearings-only measurements.
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.
基金supported by the National Natural Science Foundation of China(61671181).
文摘The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.
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%.
