In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections...In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.展开更多
The finite set statistics provides a mathematically rig- orous single target Bayesian filter （STBF） for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuver...The finite set statistics provides a mathematically rig- orous single target Bayesian filter （STBF） for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded track- ing performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Moti- vated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MM- STBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation （for generic models） and a Gaussian mixture implementation （for linear Gaussian models） are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.展开更多
传统的基于数据关联的同时定位与建图(simultaneous localization and mapping,SLAM)方法易引起观测与目标之间的误匹配,进而导致位姿估计精度下降.结合柱状特征提取方法和随机有限集理论,提出一种基于序贯蒙特卡罗实现的车辆3D激光SLA...传统的基于数据关联的同时定位与建图(simultaneous localization and mapping,SLAM)方法易引起观测与目标之间的误匹配,进而导致位姿估计精度下降.结合柱状特征提取方法和随机有限集理论,提出一种基于序贯蒙特卡罗实现的车辆3D激光SLAM方法.利用M估计抽样一致性算法从分割后的点云中提取稳定的柱状特征,捕获单帧点云中的静态存活特征和新生特征;在Rao-Blackwellized-概率假设密度同时定位与建图(Rao-Blackwellizedprobability hypothesis density-simultaneous localization and mapping,RB-PHD-SLAM)框架中引入两种特征,并运用序贯蒙特卡罗方法完成车辆轨迹概率密度和地图后验强度在帧间的传递,实现对环境特征和车辆位姿的同时估计.模拟数据集和KITTI数据集试验结果显示,与经典的FastSLAM算法相比,本文算法使车辆定位精度提升44.99%,并使环境特征位置估计和环境特征数量估计的平均误差分别降低49.24%和56.22%,显著提升了SLAM的运行精度和鲁棒性,有助于保障智能汽车的运行安全.展开更多
针对在杂波、漏检和非线性情况下,粒子概率假设密度滤波(particle probability hypothesis density filter,P-PHDF)算法估计精度不高、滤波发散及粒子退化等问题,提出了一种基于无迹粒子概率假设密度滤波(unscented particle-PHDF,UP-PH...针对在杂波、漏检和非线性情况下,粒子概率假设密度滤波(particle probability hypothesis density filter,P-PHDF)算法估计精度不高、滤波发散及粒子退化等问题,提出了一种基于无迹粒子概率假设密度滤波(unscented particle-PHDF,UP-PHDF)的序贯融合算法。利用无迹粒子滤波(unscented particle filter,UPF)实现PHDF,由UKF算法得到更好更优的重要性密度函数并从中采样,使粒子的分布更接近多目标概率假设密度分布;另外,为进一步提高滤波算法的性能,实现基于雷达和红外传感器的UP-PHDF序贯融合算法,通过两传感器交替滤波保证目标状态的可观测性。在复杂环境下,仿真结果表明该算法的估计精度和稳定性明显优于单传感器P-PHDF算法。展开更多
基金Project(61101186)supported by the National Natural Science Foundation of China
文摘In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.
基金supported by the National Natural Science Foundation of China (61101181)
文摘The finite set statistics provides a mathematically rig- orous single target Bayesian filter （STBF） for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded track- ing performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Moti- vated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MM- STBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation （for generic models） and a Gaussian mixture implementation （for linear Gaussian models） are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.
文摘传统的基于数据关联的同时定位与建图(simultaneous localization and mapping,SLAM)方法易引起观测与目标之间的误匹配,进而导致位姿估计精度下降.结合柱状特征提取方法和随机有限集理论,提出一种基于序贯蒙特卡罗实现的车辆3D激光SLAM方法.利用M估计抽样一致性算法从分割后的点云中提取稳定的柱状特征,捕获单帧点云中的静态存活特征和新生特征;在Rao-Blackwellized-概率假设密度同时定位与建图(Rao-Blackwellizedprobability hypothesis density-simultaneous localization and mapping,RB-PHD-SLAM)框架中引入两种特征,并运用序贯蒙特卡罗方法完成车辆轨迹概率密度和地图后验强度在帧间的传递,实现对环境特征和车辆位姿的同时估计.模拟数据集和KITTI数据集试验结果显示,与经典的FastSLAM算法相比,本文算法使车辆定位精度提升44.99%,并使环境特征位置估计和环境特征数量估计的平均误差分别降低49.24%和56.22%,显著提升了SLAM的运行精度和鲁棒性,有助于保障智能汽车的运行安全.
文摘针对在杂波、漏检和非线性情况下,粒子概率假设密度滤波(particle probability hypothesis density filter,P-PHDF)算法估计精度不高、滤波发散及粒子退化等问题,提出了一种基于无迹粒子概率假设密度滤波(unscented particle-PHDF,UP-PHDF)的序贯融合算法。利用无迹粒子滤波(unscented particle filter,UPF)实现PHDF,由UKF算法得到更好更优的重要性密度函数并从中采样,使粒子的分布更接近多目标概率假设密度分布;另外,为进一步提高滤波算法的性能,实现基于雷达和红外传感器的UP-PHDF序贯融合算法,通过两传感器交替滤波保证目标状态的可观测性。在复杂环境下,仿真结果表明该算法的估计精度和稳定性明显优于单传感器P-PHDF算法。
