基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density,CPHD)检测前跟踪(Track before detect,TBD)算法能有效解决未知目标数的弱小目标检测跟踪.文章深入研究了CPHD算法,从标准CPHD滤波的粒子权重更新出发,结合检测前...基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density,CPHD)检测前跟踪(Track before detect,TBD)算法能有效解决未知目标数的弱小目标检测跟踪.文章深入研究了CPHD算法,从标准CPHD滤波的粒子权重更新出发,结合检测前跟踪的实际,合理地推导出CPHD-TBD算法的粒子权重更新表达式;分析了CPHD滤波目标势分布的物理意义,实现了目标势分布更新计算在检测前跟踪的应用.将CPHD滤波和TBD进行有效结合,提出了基于势概率假设密度滤波的检测前跟踪算法,并给出其详细实现步骤.仿真实验证明提出的CPHD-TBD算法与现有概率假设密度检测前跟踪(PHD-TBD)算法相比,能更详细地传递目标分布信息,从本质上改变了PHD-TBD对目标数估计的方式,能更准确稳定估计目标数,实现了对目标的发现和状态准确估计,性能明显更优.展开更多
多目标检测与估计是多普勒雷达的基本任务。当信噪比较低时,为确保检测到目标需降低门限而产生了大量虚警,基于数据的多假设跟踪(Multi-Hypothesis Tracking,MHT)和联合概率数据关联(Joint Probabilistic Data Association,JPDA)方法因...多目标检测与估计是多普勒雷达的基本任务。当信噪比较低时,为确保检测到目标需降低门限而产生了大量虚警,基于数据的多假设跟踪(Multi-Hypothesis Tracking,MHT)和联合概率数据关联(Joint Probabilistic Data Association,JPDA)方法因计算复杂度过高而失效,基于原始信号的随机有限集(Random Finite Set,RFS)滤波器可有效解决该问题。多普勒雷达回波信号以叠加的方式受到多个目标影响,其多目标检测与估计问题属于叠加式传感器的典型应用。本文在叠加式多伯努利(Multi-Bernoulli,MBR)滤波器基础上利用具有准确势估计的独立同分布群(Independent and Identically Distributed Cluster,IIDC)RFS对新生目标建模,并采用辅助粒子滤波器(Auxiliary Particle Filter,APF)实现了多目标联合检测与状态估计。仿真结果表明,混合MBR和集势概率假设密度(Cardinalized Probability Hypothesis Density,CPHD)滤波器对多普勒雷达多目标的检测估计性能优于MBR滤波器,且减小了计算复杂度。展开更多
CPHD(Cardinalized Probability Hypothesis Density)滤波是一种杂波环境下可变目标数的多目标跟踪算法,该文针对算法中存在的目标漏检问题提出一种改进算法,该算法在高斯混合框架下实现贝叶斯递归,通过对各个高斯分量进行标记,对目标...CPHD(Cardinalized Probability Hypothesis Density)滤波是一种杂波环境下可变目标数的多目标跟踪算法,该文针对算法中存在的目标漏检问题提出一种改进算法,该算法在高斯混合框架下实现贝叶斯递归,通过对各个高斯分量进行标记,对目标进行航迹关联,在此基础上对修剪合并后各个高斯分量的权值进行两次分配。首先对超过检测门限的高斯分量权值进行分配,有效解决了目标漏检问题,然后基于一个目标只可能产生一个观测的事实进行第2次分配,改善了目标发生交叉时的算法性能。实验结果表明,所提方法在多目标状态估计和航迹维持方面均优于普通的CPHD算法。展开更多
In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hy...In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.展开更多
In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with ...In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.展开更多
文摘基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density,CPHD)检测前跟踪(Track before detect,TBD)算法能有效解决未知目标数的弱小目标检测跟踪.文章深入研究了CPHD算法,从标准CPHD滤波的粒子权重更新出发,结合检测前跟踪的实际,合理地推导出CPHD-TBD算法的粒子权重更新表达式;分析了CPHD滤波目标势分布的物理意义,实现了目标势分布更新计算在检测前跟踪的应用.将CPHD滤波和TBD进行有效结合,提出了基于势概率假设密度滤波的检测前跟踪算法,并给出其详细实现步骤.仿真实验证明提出的CPHD-TBD算法与现有概率假设密度检测前跟踪(PHD-TBD)算法相比,能更详细地传递目标分布信息,从本质上改变了PHD-TBD对目标数估计的方式,能更准确稳定估计目标数,实现了对目标的发现和状态准确估计,性能明显更优.
文摘CPHD(Cardinalized Probability Hypothesis Density)滤波是一种杂波环境下可变目标数的多目标跟踪算法,该文针对算法中存在的目标漏检问题提出一种改进算法,该算法在高斯混合框架下实现贝叶斯递归,通过对各个高斯分量进行标记,对目标进行航迹关联,在此基础上对修剪合并后各个高斯分量的权值进行两次分配。首先对超过检测门限的高斯分量权值进行分配,有效解决了目标漏检问题,然后基于一个目标只可能产生一个观测的事实进行第2次分配,改善了目标发生交叉时的算法性能。实验结果表明,所提方法在多目标状态估计和航迹维持方面均优于普通的CPHD算法。
基金supported by the National GNSS Research Center Program of the Defense Acquisition Program Administration and Agency for Defense Developmentthe Ministry of Science and ICT of the Republic of Korea through the Space Core Technology Development Program (No. NRF2018M1A3A3A02065722)
文摘In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.
文摘In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.
