针对卫星导航应用中线性调频(linear frequency modulated,LFM)干扰统计特征时变引起的抗干扰性能下降问题,提出了一种基于数据空时频三维特征分组的空频自适应处理(space-frequency adaptive processing,SFAP)算法。首先通过时频分析...针对卫星导航应用中线性调频(linear frequency modulated,LFM)干扰统计特征时变引起的抗干扰性能下降问题,提出了一种基于数据空时频三维特征分组的空频自适应处理(space-frequency adaptive processing,SFAP)算法。首先通过时频分析方法获取采样数据的时域、频域联合分布,并利用空间相关系数分析相同频率干扰在不同时间的空间相关性,然后对SFAP的采样数据进行分组,将不同时间具有相同频率和到达角参数的采样点分到相同组,最后利用分组后的数据进行协方差矩阵估计、权值计算和自适应滤波,提高了干扰特征值、增加了零陷深度、提升了抗干扰能力。仿真结果表明,所提算法可有效提升卫星导航接收机对LFM干扰的抑制能力,且对存在单个和多个LFM干扰的场景均能适用。展开更多
Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach t...Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.展开更多
文摘针对卫星导航应用中线性调频(linear frequency modulated,LFM)干扰统计特征时变引起的抗干扰性能下降问题,提出了一种基于数据空时频三维特征分组的空频自适应处理(space-frequency adaptive processing,SFAP)算法。首先通过时频分析方法获取采样数据的时域、频域联合分布,并利用空间相关系数分析相同频率干扰在不同时间的空间相关性,然后对SFAP的采样数据进行分组,将不同时间具有相同频率和到达角参数的采样点分到相同组,最后利用分组后的数据进行协方差矩阵估计、权值计算和自适应滤波,提高了干扰特征值、增加了零陷深度、提升了抗干扰能力。仿真结果表明,所提算法可有效提升卫星导航接收机对LFM干扰的抑制能力,且对存在单个和多个LFM干扰的场景均能适用。
基金supported by Fund of National Science & Technology monumental projects under Grants No.61105015,NO.61401239,NO.2012-364-641-209
文摘Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.
