The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the syn...The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.展开更多
To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accura...To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR（CA-CFAR）.To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.展开更多
The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neig...The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neighbor method is used for spatial obstacles clustering from laser radar data.By analyzing the characteristics of obstacles,the types of obstacles are determined by time correlation.Experiments were carried out on the developed unmanned aerial vehicle(UAV),and the experimental results verify the effectiveness of the proposed method.展开更多
针对冰雹检测方法中大量带标签数据不易获取的问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的贝叶斯模型冰雹检测方法,利用大量无标签数据和少量带标签数据,根据概率对冰雹进行检测。该方法首先根据大量无标签数据...针对冰雹检测方法中大量带标签数据不易获取的问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的贝叶斯模型冰雹检测方法,利用大量无标签数据和少量带标签数据,根据概率对冰雹进行检测。该方法首先根据大量无标签数据的密集程度将其初步分成若干集合,然后每个集合采用高斯核密度函数以及最佳固定带宽值,计算得到每个集合的类条件概率,其次利用少量带标签数据以及降水粒子的融化层信息建立类先验概率,最后根据每个集合的最大后验概率匹配降水粒子类型,实现冰雹与其他降水粒子类型的检测结果。通过实测数据对模型的验证结果表明,该方法能够较好的实现冰雹检测。展开更多
基金Project(61105020)supported by the National Natural Science Foundation of ChinaProject(13zxtk08)supported by the Key Research Platform for Research Projects of Southwest University of Science and Technology,China
文摘The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.
基金The National Natural Science Foundation of China under contract No.61471024the National Marine Technology Program for Public Welfare under contract No.201505002-1the Beijing Higher Education Young Elite Teacher Project under contract No.YETP0514
文摘To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR（CA-CFAR）.To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.
基金National Key R&D Program of China(No.2017YFB1201003-020)Science and Technology Project of Gansu Education Department(No.2015B-041)
文摘The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neighbor method is used for spatial obstacles clustering from laser radar data.By analyzing the characteristics of obstacles,the types of obstacles are determined by time correlation.Experiments were carried out on the developed unmanned aerial vehicle(UAV),and the experimental results verify the effectiveness of the proposed method.
文摘针对冰雹检测方法中大量带标签数据不易获取的问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的贝叶斯模型冰雹检测方法,利用大量无标签数据和少量带标签数据,根据概率对冰雹进行检测。该方法首先根据大量无标签数据的密集程度将其初步分成若干集合,然后每个集合采用高斯核密度函数以及最佳固定带宽值,计算得到每个集合的类条件概率,其次利用少量带标签数据以及降水粒子的融化层信息建立类先验概率,最后根据每个集合的最大后验概率匹配降水粒子类型,实现冰雹与其他降水粒子类型的检测结果。通过实测数据对模型的验证结果表明,该方法能够较好的实现冰雹检测。
