圆柱体上均匀圆阵的阵增益分析

以环绕在圆柱体上的均匀圆阵为阵列模型,研究了圆阵在不同条件下的阵增益.从声传播理论出发,分析了平面波入射条件下阵列周围声场,得出了阵列常规处理增益和超增益的表达式.仿真结果表明,刚性圆柱体的存在增大了阵列的处理增益,当存在非刚性圆柱体时,通过合理设置菲刚性圆柱体的阻抗,可以在特定的频段内进一步提高阵列的增益,当圆阵与圆柱体之间存在空隙时,阵列处理增益会有所下降.     

音圈电机驱动的超精密快刀伺服系统的性能研究

基于快刀伺服系统的超精密金刚石车削加工技术是光学自由曲面高效率、高精密的加工手段。本文介绍了一种新的音圈电机驱动的超精密快刀伺服系统,行程达到30mm,最大加速度为920m/s₂。通过实验手段获得了系统的运动模型,来用于控制器的设计。针对一类典型的光学自由曲面-微透镜阵列进行了加工,并对加工结果进行了测试与分析。测试结果表明,所研制的快刀伺服系统达到了加工技术要求,为以后该系统在实际加工中更广泛的应用研究打下了基础。     

印刷对数周期天线的FDTD分析与实验

采用非均匀网格时域有限差分(NU FDTD)方法,对单层印刷对数周期阵子天线(PLPDA)进行了电磁仿真,分析了单面和双面介质盖板对PLPDA性能的影响,得到了PLPDA的驻波和方向图特性,并进行了实验验证,分析结果与实验结果吻合得较好。     

基于BP网络的管道自适应有源消声研究

由于管道有源消声系统的参数时变性和本质非线性,基于传统自适应算法的控制系统稳定性不够,容易产生振荡.文章采用神经网络BP算法,利用Matlab6.0建立了管道有源消声的仿真控制系统.通过与采用传统自适应算法的控制系统进行仿真研究对比,证明采用神经网络BP算法的控制系统消声效果更加明显.     

用于纯方位目标跟踪的修正极坐标自适应卡尔曼滤波算法

针对纯方位被动目标跟踪中,直角坐标系下的扩展卡尔曼滤波器容易发散而导致滤波精度很差的问题,提出了一种修正极坐标系下的自适应卡尔曼滤波算法,对虚拟系统噪声进行估计,动态补偿模型线性化误差,对其滤波理论及算法进行了研究和仿真。仿真结果表明,该算法提高了滤波的稳定性、快速性和精确性,优于一般的扩展卡尔曼滤波算法。     

搜索状态下相控阵雷达抗干扰能力评估

针对现有评估指标不能全面反映相控阵雷达在搜索状态下的抗干扰能力的问题,提出了利用搜索效率抗干扰改善因子这一综合指标来衡量相控阵雷达搜索状态下的抗干扰能力的方法.建立了在有源噪声干扰下搜索概率计算模型以及采用脉冲累积和累积检测技术后搜索概率的计算模型,通过分析脉冲累积数N和累积检测数M的关系,给出求解最大搜索效率的方法,最后通过实例仿真和结果分析,表明该方法计算简单,科学高效.     

基于改进SUSAN算法的红外目标边缘检测

从3个方面改进了传统的SUSAN算法:①自适应选取阈值,以提高算法自动化程度:②使用双几何阈值,增强算法的抗噪性;③加入目标点判断因子,减少计算时间.利用改进的算法进行了红外目标边缘检测,结果表明,改进算法比传统的SUSAN算法有更强的噪声抑制能力,更快的边缘检测速度,显示出更加优良的性能.对对比度比较低的红外图像,改进算法可以减少40%以上的计算时间,而对对比度较高的红外图像,改进算法可以减少70%以上的计算时间.     

均匀圆阵主特征矢量分析解相干算法

为解决均匀圆阵的相干信源波达方向估计问题,提出一种适用于均匀圆阵的虚拟均匀线阵主特征矢量分析算法。采用模式空间变换将均匀圆阵转换为虚拟均匀线阵,对其数据协方差矩阵进行处理以构造主特征矢量矩阵;引入加权最小二乘法,通过重复迭代得到子空间各元素之间的线性预测系数,从而求得相干信源的波达方向估计值。理论分析和仿真结果表明,该算法对相干信源具有较好的估计精度,且具备高分辨率和低复杂度的优点。     

Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is compara-tively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R-CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R-CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the imple-mentation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.     

A novel DOA estimation algorithm using directional antennas in cylindrical conformal arrays

In this paper, a novel direction of arrival (DOA) estimation algorithm using directional antennas in cy-lindrical conformal arrays (CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix (CCM) of the sub-arrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays, and the unitary estimation of the signal parameters via rotational invariance techniques (ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio (SNR) environment.