Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,ca...Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.展开更多
In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference alg...In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.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
基金supported by the National Natural Science Foundation of China (No.61971412)。
文摘Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.
基金supported by ZTE Industry-Academia-Research Cooperation Funds
文摘In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.
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.
