A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of tar...A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of target motions are modeled by using jump Markovian system(JMS),is presented in this paper.The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model.In update,we derive a tractable GLMB density,which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence(KLD),to enable the next recursive cycle.The relevant simulation results prove that the proposed multiple-model GLMB-TBD(MM-GLMB-TBD)algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background.Additionally,the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD(DP-TBD)algorithm.Meanwhile,the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD(JMSMeMBer-TBD)filter in estimation error with the basically same computational cost.Finally,the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.展开更多
针对图像序列中多目标检测和跟踪算法结构复杂、计算量大、性能降低等问题,提出一种基于代价参考粒子滤波器组的多目标检测前跟踪(Cost-reference particle filter bank based multi-target track-before-detect, CRPFB-MTBD)算法,将多...针对图像序列中多目标检测和跟踪算法结构复杂、计算量大、性能降低等问题,提出一种基于代价参考粒子滤波器组的多目标检测前跟踪(Cost-reference particle filter bank based multi-target track-before-detect, CRPFB-MTBD)算法,将多目标跟踪问题转换为序贯地检测和跟踪多个单目标的问题.首先,采用代价参考粒子滤波器组序贯地估计所有可能单目标状态序列;其次,基于所有可能单目标状态序列的欧氏距离和累积代价确定目标数量;最后,根据累积代价判断每个目标出现和消失的具体时刻.仿真实验验证了CRPFB-MTBD的优良性能,与基于传统粒子滤波的多目标检测前跟踪算法(Particle filter based multi-target track-before-detect, PF-MTBD)、基于概率假设密度的检测前跟踪算法(Probability hypothesis density based track-before-detect, PHD-TBD)和基于伯努利滤波的检测前跟踪算法(Bernoulli based track-before-detect, Bernoulli-TBD)相比, CRPFB-MTBD的目标状态序列和数量估计结果最佳,且平均单次运行时间极短.展开更多
针对密集杂波背景中雷达微弱海面目标检测问题,提出一种基于修正Hough变换的检测前跟踪(Track Before Detect,TBD)新方法.在传统两级检测器的基础上增加点迹筛选环节,提出一种基于单帧观测数据的修正单帧Hough变换(Modified Single Houg...针对密集杂波背景中雷达微弱海面目标检测问题,提出一种基于修正Hough变换的检测前跟踪(Track Before Detect,TBD)新方法.在传统两级检测器的基础上增加点迹筛选环节,提出一种基于单帧观测数据的修正单帧Hough变换(Modified Single Hough Transform,MSHT)算法,在MSHT空间引入连续多帧共线和速度约束条件,实现对密集杂波点迹的有效抑制;针对海面多目标同时检测需要,改进传统批处理Hough变换算法,使观测空间原点自适应筛选后点迹数据,得到数据匹配Hough变换算法(Data-Matched Hough Transform,DMHT),以提升参数空间多目标分辨与检测能力.基于游程分布理论推导得到新检测器检测性能解析表达式.仿真和实测数据处理结果验证了本文方法的有效性,表明本文方法在密集杂波背景下具有良好检测性能.展开更多
Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multi...Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.展开更多
基金supported by the Fund for Foreign Scholars in University Research and Teaching Programs(B18039)Shaanxi Youth Fund(202J-JC-QN-0668).
文摘A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of target motions are modeled by using jump Markovian system(JMS),is presented in this paper.The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model.In update,we derive a tractable GLMB density,which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence(KLD),to enable the next recursive cycle.The relevant simulation results prove that the proposed multiple-model GLMB-TBD(MM-GLMB-TBD)algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background.Additionally,the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD(DP-TBD)algorithm.Meanwhile,the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD(JMSMeMBer-TBD)filter in estimation error with the basically same computational cost.Finally,the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.
文摘针对图像序列中多目标检测和跟踪算法结构复杂、计算量大、性能降低等问题,提出一种基于代价参考粒子滤波器组的多目标检测前跟踪(Cost-reference particle filter bank based multi-target track-before-detect, CRPFB-MTBD)算法,将多目标跟踪问题转换为序贯地检测和跟踪多个单目标的问题.首先,采用代价参考粒子滤波器组序贯地估计所有可能单目标状态序列;其次,基于所有可能单目标状态序列的欧氏距离和累积代价确定目标数量;最后,根据累积代价判断每个目标出现和消失的具体时刻.仿真实验验证了CRPFB-MTBD的优良性能,与基于传统粒子滤波的多目标检测前跟踪算法(Particle filter based multi-target track-before-detect, PF-MTBD)、基于概率假设密度的检测前跟踪算法(Probability hypothesis density based track-before-detect, PHD-TBD)和基于伯努利滤波的检测前跟踪算法(Bernoulli based track-before-detect, Bernoulli-TBD)相比, CRPFB-MTBD的目标状态序列和数量估计结果最佳,且平均单次运行时间极短.
文摘针对密集杂波背景中雷达微弱海面目标检测问题,提出一种基于修正Hough变换的检测前跟踪(Track Before Detect,TBD)新方法.在传统两级检测器的基础上增加点迹筛选环节,提出一种基于单帧观测数据的修正单帧Hough变换(Modified Single Hough Transform,MSHT)算法,在MSHT空间引入连续多帧共线和速度约束条件,实现对密集杂波点迹的有效抑制;针对海面多目标同时检测需要,改进传统批处理Hough变换算法,使观测空间原点自适应筛选后点迹数据,得到数据匹配Hough变换算法(Data-Matched Hough Transform,DMHT),以提升参数空间多目标分辨与检测能力.基于游程分布理论推导得到新检测器检测性能解析表达式.仿真和实测数据处理结果验证了本文方法的有效性,表明本文方法在密集杂波背景下具有良好检测性能.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.
