舰载相控阵雷达的自适应波束扫描技术优化

为了提高舰载相控阵雷达的目标定位检测能力,提出一种基于二维波束域加权谱峰搜索的舰载相控阵雷达自适应波束扫描算法.采用均匀阵列构建舰载相控阵雷达相干分布源模型,以目标的方位及目标的扩展角为二维参量,建立点目标信号源的多亮点特征分布阵列,用点目标来模拟空间的分布式目标,采用自适应波束形成算法进行目标的方位、距离及DOA的联合参量估计,采用二维波束域加权谱峰搜索方法实现远场分布式目标的空间谱特征提取,根据谱峰的位置求出目标方位,实现对目标的自适应波束扫描,提高目标的定位检测能力.仿真结果表明,采用该方法进行舰载相控阵雷达的自适应波束扫描,对目标的定位准确率较高,检测性能较好,波束输出具有很好的旁瓣抑制能力,说明抗干扰能力较强.     

近场动目标多普勒频率、距离及方位估计算法

针对近场多动目标的定位问题,提出一种近场动目标多普勒频率、距离及方位三维参数估计算法.采用多重信号分类(MUSIC)算法估计回波信号频率,以各个频率估计为参考对信号进行时域滤波,从而实现各目标回波信号的分离.使用二维MUSIC方法由每个目标的回波信号估计其距离与方位.该算法能精确估计近场动目标多普勒频率、距离及方位参数...     

频控阵MIMO雷达的目标数与方位参数联合估计方法

针对频控阵MIMO雷达定位多个目标时,需要目标数先验信息,且多维参数估计计算量大的问题,提出了一种联合估计方法。该方法利用频控阵MIMO的角度与距离依赖性,将目标数与方位参数估计转化为空间谱上的多峰优化问题;设计了一种基于密度的多峰差分进化算法对其进行求解,降低了计算量且不需要单独的目标数估计算法。仿真结果表明,所提方法的目标数和方位参数估计性能均优于已有方法,突破了已有方位参数估计方法的应用局限,可以无量化误差地得到最优方位估计结果。     

一种水下多目标方位、频率、距离联合估计新方法

本文提出了一种可在单次回波内同时完成多目标方位、频率、距离(时延)三维参数联合估计的高分辨新方法,它借用ESPRIT方法的思路构造两个空间子阵,通过对其自相关矩阵和互相关矩阵的广义特征分解,用特征值估计多目标方位,用特征向量估计多目标回波的频率,再用估计得到的频率去修正包络时延向量进而得到目标时延估计.三维参数的配对由特征向量和特征值的对应关系自动完成.计算机仿真结果表明,该方法在较低的信噪比条件下可以实现三维参数的高分辨联合估计,而且无需额外的配对算法.作者还进行了多目标三维参数联合估计的消声水池实验,也取得了较好的结果,表明该方法对阵列误差有较好的稳健性,具有良好的工程应用前景.     

基于矢量水听器线谱法目标方位估计

利用矢量水听器收集到的声压和振速信息,研究基于互谱法分析单目标的强线谱来进行目标方位估计。提出了单线谱法和多线谱法两种目标方位估计方法。其中,在分析多线谱法方位估计时,采用了直接平均和加权平均两种处理方法对多线谱进行处理,旨在提高目标方位估计精度。在原理阐述的基础上,通过仿真和湖试实验进一步分析比较了两种方位估计方法。     

一种复域压缩感知目标方位估计方法

针对频域压缩感知目标方位估计方法的性能退化问题,本文通过对线列阵接收信号进行复解析变换,按预估方位在复域对各阵元信号进行时延补偿、相关和累积处理,构建复域感知矩阵和测量值,采用复域压缩感知方法实现空间谱合成和目标方位估计.数值仿真和实测数据处理结果表明,在同一检测概率下,相比频域压缩感知方法,该方法对输入信噪比的最低要求得到近10lgMdB(M为通道数)的降低,提升了对弱目标的检测能力.     

基于波束域相位稳定性的目标检测方法研究

低信噪比下,为解决常规波束形成等权值累加空间谱各方位谱值,导致目标方位谱值被非目标方位谱值淹没,不能实现对水下辐射噪声信号未知的目标检测问题,本文提出了一种基于波束域相位稳定性的目标检测方法.依据水下目标辐射噪声含有稳定线谱及空间谱各方位对应波束域相位稳定性差异,该方法利用波束域相位方差对各方位谱值进行加权统计,实现了对水下目标方位角的有效估计.数值仿真和实验结果表明：相比常规波束形成,该方法可以进一步增强目标方位能量,抑制非目标方位噪声干扰,改善目标检测信噪比增益.     

机载正侧视合成孔径雷达地面运动目标参数估计方法

 针对距离向速度估计中存在的模糊问题,提出一种利用目标距离包络的走动轨迹,通过Radon变换技术来无模糊地估计距离向速度的方法.针对距离向加速度影响方位向速度估计问题,提出了利用目标多普勒参数来估计运动目标真实合成孔径时间,通过真实合成孔径时间与方位向速度的关系来确定目标方位向速度.该方法能很好估计方位向速度而不受距离加速度的影响.所提方法适合于单/多通道系统.仿真和实测数据验证了方法的有效性.     

宽窄带联合包络测速研究

基于宽窄带数据,提出一种宽窄带联合包络测速的新方法.利用宽带数据包络对齐估计的包络移动量与窄带距离波门的距离(解线调频的参考距离)之和,得到目标的实际距离,对此距离进行最小二乘拟合,从而得到目标速度的估计.同时文中也分析了参考距离的误差对于速度估计的影响,仿真和实测结果验证了其结论的正确性.     

塔康包络检测与方位估计算法研究

采用中频数字化技术的塔康视频信号是离散的脉冲调幅信号, 含有大量随机填充脉冲, 占空比大。首先,针对峰值搜索提取包络的方法在低信噪比环境下效果差, 提出一种新的数字包络检测算法,设计与脉冲相匹配的滤波器, 通过检测匹配滤波器输出的最大相关峰值确定脉冲到达时刻, 拟合整个脉冲估计其峰值得到包络信号; 然后,用最小二乘算法拟合包络估计方位, 通过残差分析以一定置信度剔除粗差; 最后,对连续k个方位参数数据平滑得到最终的结果。在装备模拟器上的仿真实验表明, 所提方法在0 dB功率信噪比下, 能有效检测包络、估计方位信息, 估计误差小于0.5°, 满足系统精度要求。     

基于修正BE模型的TDS-OFDM时变信道均衡

本文提出了时变信道的修正BE模型,并利用该模型推导出应用于TDS-OFDM系统的时变信道均衡算法。通过仿真,验证了该模型的准确性;测试了均衡算法的误码性能,并与现有的PN序列自相关和线性插值算法进行了比较。仿真结果表明:该算法可以很好的抵抗多径干扰和多普勒频偏对接收信号的影响,误码率较其它两种算法有显著改善,并能在移动接收速度为250km/h的环境下对时变信道实施有效的均衡。     

An efficient system for anomaly detection using deep learning classifier

In this paper, a deep learning-based anomaly detection (DLAD) system is proposed to improve the recognition problem in video processing. Our system achieves complete detection of abnormal events by involving the following significant proposed modules a Background Estimation (BE) Module, an Object Segmentation (OS) Module, a Feature Extraction (FE) Module, and an Activity Recognition (AR) Module. At first, we have presented a BE (Background Estimation) module that generated an accurate background in which two-phase model is generated to compute the background estimation. After a high-quality background is generated, the OS model is developed to extract the object from videos, and then, object tracking process is used to track the object through the overlapping detection scheme. From the tracked objects, the FE module is extracted for some useful features such as shape, wavelet, and histogram to the abnormal event detection. For the final step, the proposed AR module is classified as abnormal or normal event using the deep learning classifier. Experiments are performed on the USCD benchmark dataset of abnormal activities, and comparisons with the state-of-the-art methods validate the advantages of our algorithm. We can see that the proposed activity recognition system has outperformed by achieving better EER of 0.75 % when compared with the existing systems (20 %). Also, it shows that the proposed method achieves 85 % precision rate in the frame-level performance.     

Visual Hysteresis Based Dynamic Interference Shaping for Real-Time Video Services in Dense Deployed Cellular Networks

Due to the dense deployed base stations and the growing traffic of best effort (BE) services, frequent bursty interference becomes one of major challenges to assure quality of experience (QoE) for real-time video services. Considering those situations, a novel dynamic interference shaping method has been proposed in this paper, to assure the QoE for real-time video services. Firstly, to evaluate the quality impairment induced by bursty interference, we proposed a new QoE prediction model with content-adaptive and hysteresis effect. Secondly, the cellular interference traffic model is proposed in the framework of interference shaping. By this traffic model, the interference characteristic of best effort services is analyzed and the rate scaling factor (RSF) is introduced to adjust the transmission power of BE service, thus to neutralize the interference of real-time video service. Finally, the utility function with QoE for real-time video services and BE services is presented, and we utilize particle swarm optimization method to obtain the optimal RSF. Simulation results show that proposed method greatly improves QoE for real-time video services.     

基于直接密度比估计的SAR图像变化检测

提出了一种通过直接分布函数密度比估计的SAR图像变化检测方法。不同于以往基于分布函数的方法需要先分别估计不同时相的分布函数再计算比值,该方法直接估计分布函数比值,并使用皮尔逊散度作为差异度量获取差异图。通过将概率标签松弛(PLR)嵌入到最大期望(EM)聚类算法中,增强了变化检测结果的空间一致性。实验结果表明,该方法能够准确地提取变化区域,得到较为满意的变化检测结果。     

SCM-OFDM系统中一种新型的盲频偏估计方法

该文提出了一种用于重叠码调制-正交频分复用(SCM-OFDM)系统中基于信号检测的频偏估计方法。这种估计方法首先利用SCM迭代检测器输出的判决符号重构接收信号,再利用最小均方误差准则对频偏进行估计。该方法不需要额外的训练序列或导频符号,是一种高效的盲频偏估计方法。此外,该文还提出了一种改进的频偏估计方法。在改进的方法中,接收机将频偏估计与信号检测进行联合迭代;这样在每一次迭代过程中,信号检测和频偏估计都需要执行一次,并且接收机在信号检测时可以利用上一次得到的频偏估计值进行频偏补偿。仿真结果表明,该文提出的基于信号检测的频偏估计方法能够高效并准确地估计出SCM-OFDM系统的频偏。     

Blind downlink channel estimation for TDD‐based multiuser massive MIMO in the presence of nonlinear HPA

In time division duplex (TDD)‐based multiuser massive multiple input multiple output (MIMO) systems, the uplink channel is estimated and the results are used in downlink for signal detection. Owing to noisy uplink channel estimation, the downlink channel should also be estimated for accurate signal detection. Therefore, recently, a blind method was developed, which assumes the use of a linear high‐power amplifier (HPA) in the base station (BS). In this study, we extend this method to a scenario with a nonlinear HPA in the BS, where the Bussgang decomposition is used for HPA modeling. In the proposed method, the average power of the received signal for each user is a function of channel gain, large‐scale fading, and nonlinear distortion variance. Therefore, the channel gain is estimated, which is required for signal detection. The performance of the proposed method is analyzed theoretically. The simulation results show superior performance of the proposed method compared to that of the other methods in the literature.     

Resource Allocation in OFDMA Wireless Communications Systems Supporting Multimedia Services

We design a resource allocation algorithm for downlink of orthogonal frequency division multiple access (OFDMA) systems supporting real-time (RT) and best-effort (BE) services simultaneously over a time-varying wireless channel. The proposed algorithm aims at maximizing system throughput while satisfying quality of service (QoS) requirements of the RT and BE services. We take two kinds of QoS requirements into account. One is the required average transmission rate for both RT and BE services. The other is the tolerable average absolute deviation of transmission rate (AADTR) just for the RT services, which is used to control the fluctuation in transmission rates and to limit the RT packet delay to a moderate level. We formulate the optimization problem representing the resource allocation under consideration and solve it by using the dual optimization technique and the projection stochastic subgradient method. Simulation results show that the proposed algorithm well meets the QoS requirements with the high throughput and outperforms the modified largest weighted delay first (M-LWDF) algorithm that supports similar QoS requirements.     

基于深度学习的抗窄带干扰MSK非相干接收机

窄带干扰(Narrowband Interference,NBI),作为一种敌意的频域干扰,会严重地恶化最小频移键控(Min-imum Shift Keying,MSK)非相干检测的误码率(Bit Error Rate,BER)性能.为降低窄带干扰对BER性能的影响,MSK非相干接收机一般首先对接收信号进行干扰抑制.然...     

LTE SRS检测与噪声估计研究

LTE系统中定义了信道探测参考信号,用于基站估计上行链路的信道质量。基站基于上行SRS信道估计得到上行信道信息,将获得的信道信息输入调度器作为其上行调度的主要依据。本文详细给出了基于LTE SRS的信道估计算法,并对其中的噪声及信噪比估计进行重点讨论,最后通过仿真分析了多种典型信道条件下信噪比估计的准确性。     

利用谱估计算法的雷达信号分选

传统雷达信号分选过程中分别进行脉冲重复频率检测与初始到达时间估计时,脉冲搜索时间过长。为此,提出了一种新的基于谱估计的信号分选算法。该算法通过谐波滤除减小了脉冲初始到达时间估计的运算次数,并在进行脉冲重复频率检测的同时估计脉冲初始到达时间,从而避免了传统信号分选算法中对整个序列的遍历,节省了计算资源。仿真结果验证了该算法的有效性。