In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
该文针对雷达目标高分辨距离像(High-Resolution Range Profile,HRRP)识别中距离单元回波幅值统计建模所面临的概率密度模型选择问题,提出一种基于半参数化概率密度估计的雷达目标识别方法。半参数化概率密度估计从参数化概率密度估计出...该文针对雷达目标高分辨距离像(High-Resolution Range Profile,HRRP)识别中距离单元回波幅值统计建模所面临的概率密度模型选择问题,提出一种基于半参数化概率密度估计的雷达目标识别方法。半参数化概率密度估计从参数化概率密度估计出发,有效利用了高分辨距离像各距离单元幅值近似服从Gamma分布的经验知识,并且通过非参数化修正因子对Gamma模型进行修正,达到参数化方法和非参数化方法优缺互补的目的。基于5种飞机模型高分辨距离像数据的仿真实验证明了该文方法的有效性。展开更多
雷达高分辨距离像(high-resolution range profile,HRRP)包含了丰富的目标结构信息,在雷达目标识别领域有良好的应用前景。针对传统的HRRP识别方法对噪声环境适应性差的问题,选用具有时移不变性的紧支撑小波自相关作为支持向量机(suppor...雷达高分辨距离像(high-resolution range profile,HRRP)包含了丰富的目标结构信息,在雷达目标识别领域有良好的应用前景。针对传统的HRRP识别方法对噪声环境适应性差的问题,选用具有时移不变性的紧支撑小波自相关作为支持向量机(support vector machine,SVM)分类器的核函数,研究了幂次变换(power transform,PT)参数的选取对识别效果的影响,给出了参数选取经验公式,结合信噪比实时估算自适应地进行数据预处理以增强算法的抗噪性能。仿真表明,所提出的方法与传统的高斯径向基核SVM相比,提高了目标识别率,并且具有较好的噪声稳健性。展开更多
In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this paper.F...In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this paper.For radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more complex.In order to get closer to the real situation,the half space HRRP is simulated as the dataset.The recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image recognition.Finally,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition rate.The recognition rate after fusion has a growth more than 7%compared with a single sensor.After the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
文摘该文针对雷达目标高分辨距离像(High-Resolution Range Profile,HRRP)识别中距离单元回波幅值统计建模所面临的概率密度模型选择问题,提出一种基于半参数化概率密度估计的雷达目标识别方法。半参数化概率密度估计从参数化概率密度估计出发,有效利用了高分辨距离像各距离单元幅值近似服从Gamma分布的经验知识,并且通过非参数化修正因子对Gamma模型进行修正,达到参数化方法和非参数化方法优缺互补的目的。基于5种飞机模型高分辨距离像数据的仿真实验证明了该文方法的有效性。
基金supported by the National Natural Science Foundation of China(61571022,61971022).
文摘In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this paper.For radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more complex.In order to get closer to the real situation,the half space HRRP is simulated as the dataset.The recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image recognition.Finally,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition rate.The recognition rate after fusion has a growth more than 7%compared with a single sensor.After the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.
