The aim of modulation classification （MC） is to identify the modulation type of a commtmication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-...The aim of modulation classification （MC） is to identify the modulation type of a commtmication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their recognition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis （PCA） with different transformation inputs, can achieve satisfactory accuracy even at low SNR （as low as 2 dB）. Due to the properties of spectrogram, the statistical pattern recognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.展开更多
为充分利用多时相极化合成孔径雷达(SAR)数据的时间相干性和散射特征,提出一个多时相极化SAR分类方法,该方法基于完整的极化协方差矩阵,能够在张量空间保持协方差矩阵的复数矩阵结构,实现时间维度的独立表示,可同时适用于全极化和简缩极...为充分利用多时相极化合成孔径雷达(SAR)数据的时间相干性和散射特征,提出一个多时相极化SAR分类方法,该方法基于完整的极化协方差矩阵,能够在张量空间保持协方差矩阵的复数矩阵结构,实现时间维度的独立表示,可同时适用于全极化和简缩极化SAR。该方法采用目标级的分类策略,首先,通过简单线性迭代聚类方法实现多时相极化SAR的超像素联合分割;随后,将目标的极化协方差矩阵表示为张量的形式,利用张量域的多线性主成分分析方法,实现多时相极化协方差矩阵的特征降维;最后,用决策树方法实现农作物分类。获取4景RADARSAT-2 Fine Quad模式全极化SAR图像,对天津市武清区农作物种植区开展作物分类实验,相较于其他文献提出的方法,本文方法取得了最高的总体分类精度。进一步,将该方法推广至π/4模式和CTLR模式的简缩极化SAR,并将其农作物分类精度与全极化SAR进行对比,以研究不同极化SAR数据对作物的识别能力。实验结果表明,简缩极化SAR可以取得与全极化SAR相当的总体分类精度,但全极化SAR在水稻、荷花等小样本地物上表现更优。展开更多
To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target...To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.展开更多
高光谱图像以其高分辨率的空间和光谱信息在军事、航空航天及民用等遥感领域均有重要应用,具有重要的研究意义。深度学习具有学习能力强、覆盖范围广及可移植性强的优势,成为目前高精度高光谱图像分类技术研究的热点。其中卷积神经网络(...高光谱图像以其高分辨率的空间和光谱信息在军事、航空航天及民用等遥感领域均有重要应用,具有重要的研究意义。深度学习具有学习能力强、覆盖范围广及可移植性强的优势,成为目前高精度高光谱图像分类技术研究的热点。其中卷积神经网络(CNN)因强大的特征提取能力广泛应用于高光谱图像分类方法研究中,取得了有效的研究成果,但该类方法通常单独基于2D-CNN或3D-CNN进行,针对高光谱图像的单一特征,一是不能充分利用高光谱数据本身完整的特征信息;二是虽然相应提取网络局部特征优化性好,但是整体泛化能力不足,在深度挖掘HSI的空间和光谱信息方面存在局限性。鉴于此,提出了基于注意力机制的混合卷积神经网络模型(HybridSN_AM),使用主成分分析法对高光谱图像进行降维,采用卷积神经网络作为分类模型的主体,通过注意力机制筛选出更有区分度的特征,使模型能够提取到更精确、更核心的空间-光谱信息,实现高光谱图像的高精度分类。对Indian Pines(IP)、University of Pavia(UP)和Salinas(SA)三个数据集进行了应用实验,结果表明,基于该模型的目标图像总体分类精度、平均分类精度和Kappa系数均高于98.14%、97.17%、97.87%。与常规HybridSN模型对比表明,HybridSN_AM模型在三个数据集上的分类精度分别提升了0.89%、0.07%和0.73%。有效解决了高光谱图像空间-光谱特征提取与融合的难题,提高HSI分类的精度,具有较强的泛化能力,充分验证了注意力机制结合混合卷积神经网络在高光谱图像分类中的有效性和可行性,对高光谱图像分类技术的发展及应用具有重要的科学价值。展开更多
文摘The aim of modulation classification （MC） is to identify the modulation type of a commtmication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their recognition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis （PCA） with different transformation inputs, can achieve satisfactory accuracy even at low SNR （as low as 2 dB）. Due to the properties of spectrogram, the statistical pattern recognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.
文摘为充分利用多时相极化合成孔径雷达(SAR)数据的时间相干性和散射特征,提出一个多时相极化SAR分类方法,该方法基于完整的极化协方差矩阵,能够在张量空间保持协方差矩阵的复数矩阵结构,实现时间维度的独立表示,可同时适用于全极化和简缩极化SAR。该方法采用目标级的分类策略,首先,通过简单线性迭代聚类方法实现多时相极化SAR的超像素联合分割;随后,将目标的极化协方差矩阵表示为张量的形式,利用张量域的多线性主成分分析方法,实现多时相极化协方差矩阵的特征降维;最后,用决策树方法实现农作物分类。获取4景RADARSAT-2 Fine Quad模式全极化SAR图像,对天津市武清区农作物种植区开展作物分类实验,相较于其他文献提出的方法,本文方法取得了最高的总体分类精度。进一步,将该方法推广至π/4模式和CTLR模式的简缩极化SAR,并将其农作物分类精度与全极化SAR进行对比,以研究不同极化SAR数据对作物的识别能力。实验结果表明,简缩极化SAR可以取得与全极化SAR相当的总体分类精度,但全极化SAR在水稻、荷花等小样本地物上表现更优。
基金Defense Industrial Technology Development Program (JCKY2020204B016)National Natural Science Foundation of China (92471206)。
文摘To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.
文摘高光谱图像以其高分辨率的空间和光谱信息在军事、航空航天及民用等遥感领域均有重要应用,具有重要的研究意义。深度学习具有学习能力强、覆盖范围广及可移植性强的优势,成为目前高精度高光谱图像分类技术研究的热点。其中卷积神经网络(CNN)因强大的特征提取能力广泛应用于高光谱图像分类方法研究中,取得了有效的研究成果,但该类方法通常单独基于2D-CNN或3D-CNN进行,针对高光谱图像的单一特征,一是不能充分利用高光谱数据本身完整的特征信息;二是虽然相应提取网络局部特征优化性好,但是整体泛化能力不足,在深度挖掘HSI的空间和光谱信息方面存在局限性。鉴于此,提出了基于注意力机制的混合卷积神经网络模型(HybridSN_AM),使用主成分分析法对高光谱图像进行降维,采用卷积神经网络作为分类模型的主体,通过注意力机制筛选出更有区分度的特征,使模型能够提取到更精确、更核心的空间-光谱信息,实现高光谱图像的高精度分类。对Indian Pines(IP)、University of Pavia(UP)和Salinas(SA)三个数据集进行了应用实验,结果表明,基于该模型的目标图像总体分类精度、平均分类精度和Kappa系数均高于98.14%、97.17%、97.87%。与常规HybridSN模型对比表明,HybridSN_AM模型在三个数据集上的分类精度分别提升了0.89%、0.07%和0.73%。有效解决了高光谱图像空间-光谱特征提取与融合的难题,提高HSI分类的精度,具有较强的泛化能力,充分验证了注意力机制结合混合卷积神经网络在高光谱图像分类中的有效性和可行性,对高光谱图像分类技术的发展及应用具有重要的科学价值。
