2018年  7卷  第3期

论文
摘要:
超宽带雷达具备穿透墙体获得建筑物内部结构布局的能力,为建筑物内人员探测定位提供更丰富的信息。传统成像常存在较为严重的旁瓣,而且墙后目标成像位置也会受墙体影响而产生偏移。为提高成像质量,稀疏重构技术被引入穿墙成像领域,但传统方法对弱散射目标的重构概率较低。该文提出结合相干因子(Coherence Factor, CF)加权的稀疏重构方法,在稀疏重构提取支撑集的过程中,利用CF增强成像的结果来提高支撑集原子的正确性,降低稀疏重构过程中强散射目标旁瓣的影响,最终提高场景中弱散射目标的重构概率。同时建立了多层墙体位置校正模型,将场景校正放到稀疏重构之后进行,从而以较低的计算复杂度降低墙体定位误差。实测数据处理结果表明,相比于传统的稀疏成像方法,相同的数抽取比例下,该文提出的方法能够有效提高场景中弱散射目标重构概率,并将建筑物内部墙体定位误差降低至10 cm以内。 超宽带雷达具备穿透墙体获得建筑物内部结构布局的能力,为建筑物内人员探测定位提供更丰富的信息。传统成像常存在较为严重的旁瓣,而且墙后目标成像位置也会受墙体影响而产生偏移。为提高成像质量,稀疏重构技术被引入穿墙成像领域,但传统方法对弱散射目标的重构概率较低。该文提出结合相干因子(Coherence Factor, CF)加权的稀疏重构方法,在稀疏重构提取支撑集的过程中,利用CF增强成像的结果来提高支撑集原子的正确性,降低稀疏重构过程中强散射目标旁瓣的影响,最终提高场景中弱散射目标的重构概率。同时建立了多层墙体位置校正模型,将场景校正放到稀疏重构之后进行,从而以较低的计算复杂度降低墙体定位误差。实测数据处理结果表明,相比于传统的稀疏成像方法,相同的数抽取比例下,该文提出的方法能够有效提高场景中弱散射目标重构概率,并将建筑物内部墙体定位误差降低至10 cm以内。
摘要:
This study proposes a high-resolution radar imaging method combined with the sparse low-rank matrix recovery technique and the deconvolution algorithm based on the matched filtering result. We establish a two-Dimensional (2D) convolution model for the radar signal after the Matched Filter (MF) to maximize the Signal-to-Noise Ratio (SNR) and use the 2D deconvolution algorithm of the Wiener filter to obtain a high resolution. However, representative deconvolution algorithms are confronted with an ill-posed problem, which magnifies the noise while influencing the imaging resolution. Prior to this study, the echo matrix after the MF was proven to be sparse and low rank under the constraint of a sparsely distributed target. The target distribution is smoothed by the influence of the point spread function. Hence, inspired by these points, we further enhance the SNR of the echo matrix based on the sparse and low-rank characteristics to alleviate the ill-posed problem of deconvolution and the poor resolution of the Wiener filter. The performance of the proposed method is demonstrated by the real experimental data. This study proposes a high-resolution radar imaging method combined with the sparse low-rank matrix recovery technique and the deconvolution algorithm based on the matched filtering result. We establish a two-Dimensional (2D) convolution model for the radar signal after the Matched Filter (MF) to maximize the Signal-to-Noise Ratio (SNR) and use the 2D deconvolution algorithm of the Wiener filter to obtain a high resolution. However, representative deconvolution algorithms are confronted with an ill-posed problem, which magnifies the noise while influencing the imaging resolution. Prior to this study, the echo matrix after the MF was proven to be sparse and low rank under the constraint of a sparsely distributed target. The target distribution is smoothed by the influence of the point spread function. Hence, inspired by these points, we further enhance the SNR of the echo matrix based on the sparse and low-rank characteristics to alleviate the ill-posed problem of deconvolution and the poor resolution of the Wiener filter. The performance of the proposed method is demonstrated by the real experimental data.
摘要:
运用探地雷达对复杂地下介质层进行探测时,雷达回波信号易受噪声影响。为了提高探地雷达的探测分辨率和数据解译效果,该文提出基于自动反相校正和峰度值比较的探地雷达回波信号去噪算法。首先,含噪的回波信号与随机噪声拟合得到两路信号,经过独立分量分析算法后得到高峰度值信号和低峰度值噪声,对高峰度值信号进行相位判断并进行自动反相校正,再进行完全总体经验模态算法分解得到多个分解分量。将独立分量分析得出的噪声的峰度值作为阈值,峰度值高于该阈值的分解分量视为信号分量,累加得到重构后的信号,完成去噪处理。所提的去噪算法解决了独立成分分析算法中的信号相位不定性问题,且在进行完全总体经验模态分解算法后无需依靠传统的人工方式进行噪声剔除的步骤。仿真和实测数据的处理结果验证了所提算法的有效性。 运用探地雷达对复杂地下介质层进行探测时,雷达回波信号易受噪声影响。为了提高探地雷达的探测分辨率和数据解译效果,该文提出基于自动反相校正和峰度值比较的探地雷达回波信号去噪算法。首先,含噪的回波信号与随机噪声拟合得到两路信号,经过独立分量分析算法后得到高峰度值信号和低峰度值噪声,对高峰度值信号进行相位判断并进行自动反相校正,再进行完全总体经验模态算法分解得到多个分解分量。将独立分量分析得出的噪声的峰度值作为阈值,峰度值高于该阈值的分解分量视为信号分量,累加得到重构后的信号,完成去噪处理。所提的去噪算法解决了独立成分分析算法中的信号相位不定性问题,且在进行完全总体经验模态分解算法后无需依靠传统的人工方式进行噪声剔除的步骤。仿真和实测数据的处理结果验证了所提算法的有效性。
摘要:
针对相控阵雷达任务调度问题,该文提出了一种基于动态时间窗的任务调度方法。该方法根据目标跟踪滤波中的残差和雷达对目标跟踪波门之间的约束关系以及搜索帧周期的约束,分别实现对跟踪任务和搜索任务的时间窗计算。最后将该方法与传统固定时间窗方法进行对比仿真,仿真结果证明了所提方法的有效性和优越性。 针对相控阵雷达任务调度问题,该文提出了一种基于动态时间窗的任务调度方法。该方法根据目标跟踪滤波中的残差和雷达对目标跟踪波门之间的约束关系以及搜索帧周期的约束,分别实现对跟踪任务和搜索任务的时间窗计算。最后将该方法与传统固定时间窗方法进行对比仿真,仿真结果证明了所提方法的有效性和优越性。
摘要:
外辐射源雷达是一种基于第三方非合作照射源的新体制雷达系统,在微多普勒效应目标分类和识别方面具有独特的优势,而其特点也决定了微多普勒效应参数估计方法需要具有良好的抗噪性能且计算量要小。针对上述问题,该文依据外辐射源雷达直升机旋翼微动信号模型,提出了利用时频域中回波闪烁特征进行直升机旋翼参数估计的新思路。通过对时频图中正负频率轴数据的幅值分别进行累加,提取出回波闪烁参数,同时,依据微动信号内在特性构建字典矩阵,利用正交匹配追踪算法实现了叶片长度、叶片数量、旋翼转速等参数的估计,相比常规Hough变换参数估计方法,该文方法更准确,更迅速。仿真和实测证明了该文方法的有效性。 外辐射源雷达是一种基于第三方非合作照射源的新体制雷达系统,在微多普勒效应目标分类和识别方面具有独特的优势,而其特点也决定了微多普勒效应参数估计方法需要具有良好的抗噪性能且计算量要小。针对上述问题,该文依据外辐射源雷达直升机旋翼微动信号模型,提出了利用时频域中回波闪烁特征进行直升机旋翼参数估计的新思路。通过对时频图中正负频率轴数据的幅值分别进行累加,提取出回波闪烁参数,同时,依据微动信号内在特性构建字典矩阵,利用正交匹配追踪算法实现了叶片长度、叶片数量、旋翼转速等参数的估计,相比常规Hough变换参数估计方法,该文方法更准确,更迅速。仿真和实测证明了该文方法的有效性。
摘要:
Three-Dimensional (3-D) Interferometric Inverse Synthetic Aperture Radar (InISAR) imaging system based on the orthogonal double baseline can achieve the 3-D geometric reconstruction of a target effectively, which is extremely helpful in target classification and identification. However, only sparse aperture measurements are available in the actual imaging process, which might pose some challenges to the traditional InISAR imaging algorithms. In this study, a new method of 3-D InISAR imaging of a ship with sparse aperture is presented. Minimum entropy algorithms are adopted to realize motion compensation and image coregistration of the sparse echoes. A gradient-based technique is used to achieve highly accurate signal reconstruction for the sparse aperture. A two-Dimensional (2-D) ISAR image was achieved with azimuth compression via the parameters-estimation method, and the 3-D reconstruction of a ship was achieved via the interference approach. The obtained simulation results validate the feasibility of the presented approach. Three-Dimensional (3-D) Interferometric Inverse Synthetic Aperture Radar (InISAR) imaging system based on the orthogonal double baseline can achieve the 3-D geometric reconstruction of a target effectively, which is extremely helpful in target classification and identification. However, only sparse aperture measurements are available in the actual imaging process, which might pose some challenges to the traditional InISAR imaging algorithms. In this study, a new method of 3-D InISAR imaging of a ship with sparse aperture is presented. Minimum entropy algorithms are adopted to realize motion compensation and image coregistration of the sparse echoes. A gradient-based technique is used to achieve highly accurate signal reconstruction for the sparse aperture. A two-Dimensional (2-D) ISAR image was achieved with azimuth compression via the parameters-estimation method, and the 3-D reconstruction of a ship was achieved via the interference approach. The obtained simulation results validate the feasibility of the presented approach.
摘要:
阵列干涉合成孔径雷达(Synthetic Aperture Radar, SAR)系统采用距离脉冲压缩、方位合成孔径和高度实孔径的方式,能够获得观测场景的3维SAR图像。在实际系统中多个通道的天线相位中心位置信息通常难以精确获得,如果不进行定标而直接进行成像处理将会造成高度维成像质量降低。针对天线相位中心位置定标问题,该文分析了天线相位中心位置误差对高度维成像造成的影响,提出了一种基于子空间正交原理的相位中心位置定标方法。该方法利用2维SAR单视复图像中的定标点数据,通过特征值分解得到噪声子空间,利用子空间正交原理同时求解多个通道对应的天线相位中心位置。针对阵列干涉SAR系统应用,该文给出了相位中心位置定标处理流程,最后通过仿真和实际数据处理验证了定标方法的有效性。 阵列干涉合成孔径雷达(Synthetic Aperture Radar, SAR)系统采用距离脉冲压缩、方位合成孔径和高度实孔径的方式,能够获得观测场景的3维SAR图像。在实际系统中多个通道的天线相位中心位置信息通常难以精确获得,如果不进行定标而直接进行成像处理将会造成高度维成像质量降低。针对天线相位中心位置定标问题,该文分析了天线相位中心位置误差对高度维成像造成的影响,提出了一种基于子空间正交原理的相位中心位置定标方法。该方法利用2维SAR单视复图像中的定标点数据,通过特征值分解得到噪声子空间,利用子空间正交原理同时求解多个通道对应的天线相位中心位置。针对阵列干涉SAR系统应用,该文给出了相位中心位置定标处理流程,最后通过仿真和实际数据处理验证了定标方法的有效性。
摘要:
作为实现高分辨率宽幅成像的重要技术手段之一,方位多通道合成孔径雷达(Synthetic Aperture Radar, SAR)近年来得到了广泛的研究与发展。在进行多通道数据重建之前,通道之间的传输特性必须校正一致,以避免图像中出现严重的虚假目标。在多通道SAR数据处理中,精确的基带多普勒中心估计对系统的通道失配校正和高分辨率成像具有非常重要的意义。但是单一通道数据的多普勒频谱混叠制约了传统基带多普勒中心估计算法在方位多通道SAR系统中的应用。基于特征分解处理,该文提出一种新的基带多普勒中心估计方法。该方法在推导过程中考虑了波束指向存在斜视的影响,能够实现方位多通道SAR系统基带多普勒中心和通道间相位误差的鲁棒估计。仿真实验和C波段方位向四通道机载SAR实验数据处理分析验证了算法的有效性。 作为实现高分辨率宽幅成像的重要技术手段之一,方位多通道合成孔径雷达(Synthetic Aperture Radar, SAR)近年来得到了广泛的研究与发展。在进行多通道数据重建之前,通道之间的传输特性必须校正一致,以避免图像中出现严重的虚假目标。在多通道SAR数据处理中,精确的基带多普勒中心估计对系统的通道失配校正和高分辨率成像具有非常重要的意义。但是单一通道数据的多普勒频谱混叠制约了传统基带多普勒中心估计算法在方位多通道SAR系统中的应用。基于特征分解处理,该文提出一种新的基带多普勒中心估计方法。该方法在推导过程中考虑了波束指向存在斜视的影响,能够实现方位多通道SAR系统基带多普勒中心和通道间相位误差的鲁棒估计。仿真实验和C波段方位向四通道机载SAR实验数据处理分析验证了算法的有效性。
摘要:
阵列干涉合成孔径雷达(Synthetic Aperture Radar, SAR)通过在交轨向布置多个天线,结合方位向的合成孔径和斜距向的大带宽信号,具备了3维分辨能力,且多个阵元保证了其在高程向的空间采样,能够解决干涉SAR(Interferometric SAR, InSAR)测绘中的叠掩问题,实现观测场景的3维成像。但是获得场景区域的3维点云分布中存在较多杂点,高程向误差较大,所以传统的激光雷达(Light Detection And Ranging, LiDAR)点云滤波方法不适用于阵列干涉SAR点云的滤波处理。针对该问题,该文提出基于空间聚类种子生长算法的阵列干涉SAR点云滤波算法,应用密度和高程双重阈值生成密度-高程图像,通过图像处理手段去除小型杂点,利用空间聚类种子生长算法将植被等从点云数据中去除,完成点云滤波处理。利用国内首次机载阵列干涉SAR实验数据,通过与传统LiDAR滤波方法进行比较,验证了该文算法的有效性,为后续建筑物提取和精细化处理提供保障。 阵列干涉合成孔径雷达(Synthetic Aperture Radar, SAR)通过在交轨向布置多个天线,结合方位向的合成孔径和斜距向的大带宽信号,具备了3维分辨能力,且多个阵元保证了其在高程向的空间采样,能够解决干涉SAR(Interferometric SAR, InSAR)测绘中的叠掩问题,实现观测场景的3维成像。但是获得场景区域的3维点云分布中存在较多杂点,高程向误差较大,所以传统的激光雷达(Light Detection And Ranging, LiDAR)点云滤波方法不适用于阵列干涉SAR点云的滤波处理。针对该问题,该文提出基于空间聚类种子生长算法的阵列干涉SAR点云滤波算法,应用密度和高程双重阈值生成密度-高程图像,通过图像处理手段去除小型杂点,利用空间聚类种子生长算法将植被等从点云数据中去除,完成点云滤波处理。利用国内首次机载阵列干涉SAR实验数据,通过与传统LiDAR滤波方法进行比较,验证了该文算法的有效性,为后续建筑物提取和精细化处理提供保障。
毫米波雷达技术专题
摘要:
In contrast to remote sensing radar, automotive radar focuses on the detection of short-range targets in the 0–1000 m range. Conventional automotive pulsed radar usually uses a monostatic antenna and it requires high peak power for the transmission of the short duration pulses to reliably detect targets at close range with a high resolution. Unfortunately, it is difficult and expensive to generate high-powered pulses on the nanosecond scale. Meanwhile, the existing automotive radars suffer from bottlenecks, i.e., spatial resolution, sidelobe levels, and Inter-Sensor Interference (ISI). To overcome the above challenges, a bistatic antenna to transmit and receive large time-bandwidth product waveforms is firstly proposed in this paper. Secondly, high spatial resolution is implemented using a Digital Beam Forming (DBF) transmitter and the high range resolution is achieved by using the pulse compression technique. Additionally, the radial velocity of the target is calculated by applying pulse Doppler processing. Finally, to deal with the sidelobe effect of impulse response function of point target and the interference arising from neighboring radars, novel Orthogonal Random Phase-Coded (ORPC) radar signals are presented. Using these ORPC signals, the impulse response function of the radar can achieve a peak sidelobe ratio of –45 dB without any loss in the signal-to-noise ratio. Most importantly, interference can be significantly reduced by using the proposed signals. Extensive simulations demonstrate the effectiveness and advantages of the proposed radar. In contrast to remote sensing radar, automotive radar focuses on the detection of short-range targets in the 0–1000 m range. Conventional automotive pulsed radar usually uses a monostatic antenna and it requires high peak power for the transmission of the short duration pulses to reliably detect targets at close range with a high resolution. Unfortunately, it is difficult and expensive to generate high-powered pulses on the nanosecond scale. Meanwhile, the existing automotive radars suffer from bottlenecks, i.e., spatial resolution, sidelobe levels, and Inter-Sensor Interference (ISI). To overcome the above challenges, a bistatic antenna to transmit and receive large time-bandwidth product waveforms is firstly proposed in this paper. Secondly, high spatial resolution is implemented using a Digital Beam Forming (DBF) transmitter and the high range resolution is achieved by using the pulse compression technique. Additionally, the radial velocity of the target is calculated by applying pulse Doppler processing. Finally, to deal with the sidelobe effect of impulse response function of point target and the interference arising from neighboring radars, novel Orthogonal Random Phase-Coded (ORPC) radar signals are presented. Using these ORPC signals, the impulse response function of the radar can achieve a peak sidelobe ratio of –45 dB without any loss in the signal-to-noise ratio. Most importantly, interference can be significantly reduced by using the proposed signals. Extensive simulations demonstrate the effectiveness and advantages of the proposed radar.
摘要:
该文研究工作包括频域稀疏毫米波人体安检成像数据处理和用于快速安检成像的稀疏阵列设计两部分。首先基于柱面扫描成像模型,采用巴克码随机稀疏采样方式减少成像所需数据量;提出一种基于干涉处理和频域压缩感知的3维成像算法,利用干涉处理使人体复图像在频域具备稀疏性,建立频域压缩感知测量模型并重建图像频谱,进而实现稀疏采样下人体安检图像3维重建。实际数据处理结果表明,该方法在数据采集量减少约50%条件下,可获得接近满采样对应的图像分辨率和成像效果,稀疏采样前后的图像相关系数优于0.9。其次基于频域稀疏成像方法、巴克码稀疏采样方式和收发分置工作模式,设计了用于快速安检成像的稀疏阵列布局,在保证人体成像质量前提下,稀疏率高达94.6%。该方法用于实际安检成像系统中可大幅增加安检通过速率、减少辐射单元数量和系统复杂度,在大人流量、高安检要求场所安全检测中具有重要应用价值和市场前景。 该文研究工作包括频域稀疏毫米波人体安检成像数据处理和用于快速安检成像的稀疏阵列设计两部分。首先基于柱面扫描成像模型,采用巴克码随机稀疏采样方式减少成像所需数据量;提出一种基于干涉处理和频域压缩感知的3维成像算法,利用干涉处理使人体复图像在频域具备稀疏性,建立频域压缩感知测量模型并重建图像频谱,进而实现稀疏采样下人体安检图像3维重建。实际数据处理结果表明,该方法在数据采集量减少约50%条件下,可获得接近满采样对应的图像分辨率和成像效果,稀疏采样前后的图像相关系数优于0.9。其次基于频域稀疏成像方法、巴克码稀疏采样方式和收发分置工作模式,设计了用于快速安检成像的稀疏阵列布局,在保证人体成像质量前提下,稀疏率高达94.6%。该方法用于实际安检成像系统中可大幅增加安检通过速率、减少辐射单元数量和系统复杂度,在大人流量、高安检要求场所安全检测中具有重要应用价值和市场前景。
摘要:
结合主动式圆柱扫描毫米波3维成像安检仪的实际应用需求,该文提出一种用于3维场景重建的新方法。该方法采用ω-K算法实现天线阵列方向和距离方向的解耦合与聚焦,再采用后向投影(BP)算法进行距离方向和角度方向的合成孔径处理实现聚焦,进而实现3维场景的重建。3维人体模型仿真和实测数据处理结果表明,该方法具备理论可行性和工程适用性,除此之外,该文方法在CUDA平台下可以实现快速精确3维人体成像,并且能够适应非理想圆柱扫描轨迹3维成像应用。 结合主动式圆柱扫描毫米波3维成像安检仪的实际应用需求,该文提出一种用于3维场景重建的新方法。该方法采用ω-K算法实现天线阵列方向和距离方向的解耦合与聚焦,再采用后向投影(BP)算法进行距离方向和角度方向的合成孔径处理实现聚焦,进而实现3维场景的重建。3维人体模型仿真和实测数据处理结果表明,该方法具备理论可行性和工程适用性,除此之外,该文方法在CUDA平台下可以实现快速精确3维人体成像,并且能够适应非理想圆柱扫描轨迹3维成像应用。