Radar Signal Processing for Interference Mitigation

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically

The evolution of national level historic district registration and conservation in China: the Beijing Yandaixiejie and Beijing Guozijian districts in Beijing, China

From the 1980s, Chinese experts from some mainland universities, such as Tongji Universtiy in Shanghai and Tsinghua University in Beijing, commenced research into heritage management and historic architectural conservation in China. With the announcement of the First and Second Lists of 10 Chinese Historic and Cultural Districts in 2009 and 2010, the conservation of historic districts was generally received and elevated in agreements from state-level government to local level governments. This paper considers literature about international and Chinese regulations and presents the evolution of historic district conservation in China. The paper explores the effective and ineffective results of the “Selection Contest of Chinese Top 10 Historic and Cultural Districts” in two cases selected from the First and Second Lists of 10 Chinese Historical and Cultural Districts during upon recent research and investigations. In each example, the paper provides a detailed examination of public awareness and their evaluation of conservation effectiveness through questionnaires

{4-[5-(4-tert-Butyl­phen­yl)-1,3,4-oxa­diazol-2-yl]phen­yl}methanol

In the title compound, C19H20N2O2, the 1,3,4-oxadiazole ring is almost coplanar with the two neighboring benzene rings [dihedral angles = 3.76 (4) and 5.49 (4)°]. In the crystal, mol­ecules are connected by strong inter­molecular O—H⋯N hydrogen bonds, forming chains parallel to the c axis

EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification

In recent years, convolutional neural networks (CNNs) have shown great potential in synthetic aperture radar (SAR) target recognition. SAR images have a strong sense of granularity and have different scales of texture features, such as speckle noise, target dominant scatterers and target contours, which are rarely considered in the traditional CNN model. This paper proposed two residual blocks, namely EMC2A blocks with multiscale receptive fields(RFs), based on a multibranch structure and then designed an efficient isotopic architecture deep CNN (DCNN), EMC2A-Net. EMC2A blocks utilize parallel dilated convolution with different dilation rates, which can effectively capture multiscale context features without significantly increasing the computational burden. To further improve the efficiency of multiscale feature fusion, this paper proposed a multiscale feature cross-channel attention module, namely the EMC2A module, adopting a local multiscale feature interaction strategy without dimensionality reduction. This strategy adaptively adjusts the weights of each channel through efficient one-dimensional (1D)-circular convolution and sigmoid function to guide attention at the global channel wise level. The comparative results on the MSTAR dataset show that EMC2A-Net outperforms the existing available models of the same type and has relatively lightweight network structure. The ablation experiment results show that the EMC2A module significantly improves the performance of the model by using only a few parameters and appropriate cross-channel interactions.Comment: 15 pages, 9 figures, Submitted to IEEE Transactions on Geoscience and Remote Sensing, 202