Obtaining good measurement performance with meter wave radar has always been a difficult problem. Especially in low-elevation areas, the multipath effect seriously affects the measurement accuracy of meter wave radar. The generalized multiple signal classification (MUSIC) algorithm is a well-known measurement method that dose not require decorrelation processing. The polarization-sensitive array (PSA) has the advantage of polarization diversity, and the polarization smoothing generalized MUSIC algorithm demonstrates good angle estimation performance in low-elevation areas when based on a PSA. Nevertheless, its computational complexity is still high, and the estimation accuracy and discrimination success probability need to be further improved. In addition, it cannot estimate the polarization parameters. To solve these problems, a polarization synthesis steering vector MUSIC algorithm is proposed in this paper. First, the MUSIC algorithm is used to obtain the spatial spectrum of the meter wave PSA. Second, the received data are properly deformed and classified. The Rayleigh–Ritz method is used to decompose the angle to realize the decoupling of polarization and the direction of the arrival angle. Third, the geometric relationship and prior information of the direct wave and the reflected wave are used to continue dimension reduction processing to reduce the computational complexity of the algorithm. Finally, the geometric relationship is used to obtain the target height measurement results. Extensive simulation results illustrate the accuracy and superiority of the proposed algorithm.
在基于合成孔径雷达(SAR)图像的舰船目标检测中,针对图像背景复杂、舰船尺寸大小不一等问题,提出了一种改进的YOLOv3深度卷积神经网络(CNN),用于SAR图像中的舰船目标检测.该方法基于训练数据集中的尺寸标签信息,使用交并比作为距离度量,利用k-means聚类方法为舰船目标提取了九组先验锚点框作为后续候选框边框参数优化的初始值;引入rGIOU来代替交并比rIOU,用于更新框回归损失和置信度损失,从而得到更加合理的损失函数,能将候选框与标注框之间的相对位置信息引入候选框的边框参数优化.为了验证改进版YOLOv3网络的性能,文中基于高分辨SAR舰船检测数据集AIR-SARShip-2.0,利用平移、翻转、调整亮度等方法进行数据集扩充,得到训练数据集和测试数据集,并进行舰船目标检测实验.实验结果表明:相较于常规YOLOv3网络和Faster R-CNN网络,改进YOLOv3网络在舰船目标检测上的总体效果更好,具有更高的准确率和更少的虚警,提高了平均精度指标,且需要的计算时间更少.
复杂电磁环境下,雷达、通信等信号在时域、频域、时频域存在复杂混叠,且各类型信号带宽差异大、调制样式多样,常规信号分离方法难以应用.文中提出了一种基于信号重构的非参数化混叠信号分离方法.基于瞬时幅度的傅里叶分解,建立非参数化稀疏信号模型,将混叠信号分离转化为瞬时幅度和瞬时频率的联合估计问题,基于交替迭代思路,分别进行估计和更新.对于瞬时幅度项,基于广义近似消息传递方法进行估计;对于瞬时频率项,利用瞬时频率变化平缓性特征,建立瞬时频率更新模型,实现对瞬时频率的更新.进一步,基于瞬时幅度和瞬时频率估计结果进行各信号重构,实现混叠信号分离.仿真结果表明:文中所提方法能有效分离时频域复杂混叠信号.
针对机载火控雷达空空工作模式识别局限性大的问题,从电子情报和雷达告警系统的视角定义了八种工作状态,提出了基于一维卷积神经网络的识别方法.基于不同工作状态的波形特征开发了信号模拟器,构建了全脉冲数据预处理和工作状态自动识别的一维卷积神经网络结构,迭代训练确定最优的网络参数完成状态识别.仿真结果表明:文中提出的识别方法精度高,且对信噪比小、错漏脉冲多的信号适应能力强,具有较强的工程应用价值.
提出了一种基于空频域波形设计的雷达通信共享系统,即对多输入多输出发射波形在空域和频域进行优化,利用优化的发射方向图主瓣实现对目标区域的雷达探测,通过控制通信方位的发射方向图旁瓣水平以及通信频带上的功率谱密度水平来实现通信信息传输.为此,在方向图旁瓣电平、波形功率谱水平、波形恒模等约束下,以发射方向图的匹配误差为目标函数,建立了相应的波形优化问题,并采用半正定松弛方法和高斯随机化方法来求解该问题.文中给出了通信信息的解调过程,且分别验证了雷达性能和通信性能.
