复杂环境中红外亚图像动目标识别跟踪

本文分析了红外玫瑰线扫描亚成像制导的成像空间分布函数,提出一种基于神经网络特性的加权图像差分动目标检测模型。应用目标与干扰特征的统计特性,采用边跟踪边识别方法识别真假目标,从而精确跟踪。最后给出了仿真结果。     

UPS Parallel Balanced Operation Without Explicit Estimation of Reactive Power—A Simpler Scheme

A novel scheme of the well-known technique for parallel operation of uninterruptible power supply systems, namely frequency and voltage droop control method, is presented. The innovation relies on active power estimation, together with the use of a piecewise-continuous nonlinear function, in order to eliminate the need for costly cascaded low-pass filters usually employed to estimate the reactive power. Moreover, the whole scheme is derived in the time domain on a state-space approach. As a consequence, models that are lower order and more amenable to nonlinear analysis are obtained. Simulated results for two UPS parallel operation connected to a resistive load are presented. The results indicate that the novel technique leads to a proper load sharing.      

Super-Resolution Time Delay Estimation in Multipath Environments

The super-resolution time delay estimation in multipath environment is very important for many applications. Conventional super-resolution approaches can only deal with signals with wideband and flat spectra. In this paper, we propose a novel super-resolution time delay estimation method that can treat signals with narrowband spectra. In our method, the time delay estimation is first transformed into the frequency domain, in which the problem is converted into the parameter estimation of sinusoidal signals with lowpass envelopes. Then a MUSIC-type algorithm taking account of the envelope variation is applied to achieve the super-resolution estimation. Time delay estimation in active and passive systems are considered. Simulation results confirm that the proposed estimators provide better performance than the classical correlation approach and the conventional MUSIC algorithm for separating closely spaced signals with narrowband spectra.     

OFDM-WLAN系统互相关超分辨TOA估计

在正交频分复用无线局域网系统的室内场景下,针对单快拍多重信号分类的超分辨到达时间估计由于多径数估计不准确导致的到达时间估计误差偏大的问题,提出了一种未知多径数的互相关超分辨到达时间估计算法。该算法根据正交频分复用无线局域网系统的物理层协议数据单元的特点,考虑到前导码中相邻两个长正交频分复用符号的信道不变性和噪声的不相关性,首先利用两个长正交频分复用符号估计两组信道频域响应,然后,采用前后向频域平滑的方法,求两组信道频域响应的互相关矩阵,进而用所求互相关矩阵逆的高次幂近似噪声特征矩阵的乘积,最后构造伪谱函数并进行谱峰搜索,从而实现到达时间的估计。仿真结果表明,该算法估计精度高,且对噪声不敏感,具有较好的鲁棒性。     

无字典的超分辨率多径稀疏信道估计方法

使用带网格的观测字典进行稀疏信道估计是近年来常用的多径稀疏信道估计方法,而网格的存在使得这种方法存在估计精度较差的问题,尤其在网格间距较大时,这种方法的劣势更加明显。本文针对这个问题,抛弃了传统的观测字典,采用连续压缩感知方法,结合线性调频训练序列的使用,提出了更加精确的多径稀疏信道估计方法。这种方法避免了网格化带来的误差,实现了高精度、高分辨率的估计。本文首先对此进行了理论阐述,进而在两种不同的多径稀疏信道模型下进行了仿真试验,并从估计精度、计算效率等方面与其他稀疏估计算法进行了对比。仿真结果证明,采用本文提出的方法进行多径稀疏信道估计时,相比其他算法可以更加精确地估计出信道冲激响应。      

Integrating the Missing Information Estimation into Multi-frame Super-Resolution

Multi-frame super-resolution image reconstruction aims to restore a high-resolution image by fusing a set of low-resolution images. The low-resolution images are usually subject to some degradation, such as warping, blurring, down-sampling, or noising, which causes substantial information loss in the low-resolution images, especially in the texture regions. The missing information is not well estimated using existing traditional methods. In this paper, having analyzed the observation model describing the degradation process starting with a high-resolution image and moving to the low-resolution images, we propose a more reasonable observation model that integrates the missing information into the super-resolution reconstruction. Our approach is fully formulated in a Bayesian framework using the Kullback–Leibler divergence. In this way, the missing information is estimated simultaneously with the high-resolution image, motion parameters, and hyper-parameters. Our proposed estimation of the missing information improves the quality of the reconstructed image. Experimental results presented in this paper show improved performance compared with that of existing traditional methods.     

基于超分辨雷达的目标参量估计研究

讨论了在车载雷达对目标自动识别的要求下,阵列信号的谱估计问题,将修正MU-SIC(MMUSIC,Modified Multiple signal classification)算法推广到时-空二维谱估计中,解决了相干信号的频率一方位超分辨估计问题,仿真结果表明了这种方法在系统实现中的可行性和有效性.     

用WSF算法实现机扫雷达多维超分辨估计

针对传统扫描体制雷达(机扫雷达)无法分辨半功率波束宽度内存在多目标的问题,基于阵列信号处理的思想,应用加权子空间拟合(WSF)方法实现了机扫雷达超分辨估计.首先分析了机扫雷达幅度调制的导向矢量模型,然后推导了基于此导向矢量的WSF DOA估计方法.仿真结果表明,该方法具有较高的估计精度.     

Error-Resilient Motion Estimation Architecture

In this paper, we propose an energy-efficient motion estimation architecture. The proposed architecture employs the principle of error-resiliency to combat logic level timing errors that may arise in average-case designs in presence of process variations and/or due to overscaling of the supply voltage [voltage overscaling (VOS)] and thereby achieves power reduction. Error-resiliency is incorporated via algorithmic noise-tolerance (ANT). Referred to as input subsampled replica ANT (ISR-ANT), the proposed technique incorporates an input subsampled replica of the main sum-of-absolute-difference (MSAD) block for detecting and correcting errors in the MSAD block. Simulations show that the proposed technique can save up to 60% power over an optimal error-free system in a 130-nm CMOS technology. These power savings increase to 78% in a 45-nm predictive process technology. Performance of the ISR-ANT architecture in the presence of process variations indicates that average peak signal-to-noise ratio (PSNR) of the ISR-ANT architecture increases by up to 1.8 dB over that of the conventional architecture in 130-nm IBM process technology. Furthermore, the PSNR variation $(sigma/mu)$ is also reduced by 7$times$ over that of the conventional architecture at the slow corner while achieving a power reduction of 33%.      

联合时频分析和谱估计的机动目标ISAR成像

机动目标逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)成像中,在对目标回波进行平动补偿之后,每一个包含目标散射点的距离单元的回波都可以表示为多分量的线性调频信号.鉴于机动目标散射点多普勒频率的时变特性,传统的距离-多普勒(Range Doppler,RD)算法获得的机动目标...     

基于复杂运动模型的运动估值算法研究

传统的块匹配运动估值算法（ＢＭＡ）不适合对下列情形进行运动估计：旋转、伸缩（ｓｃａｌｅ）、形变（ｄｅｆｏｒｍａｔｉｏｎ）、卷曲（ｗａｒｐｉｎｇ）、错切（ｓｈｅａｒ）和不规则的伸展（ｕｎｅｖｅｎｓｔｒｅｔｃｈｉｎｇ）等，因而在实际应用中常常出现方块失真、沿运动边缘方向预测性能差等现象。为克服上述缺陷，本文研究了基于多种运动模型的运动估计算法，提出了一种自适应选择运动模型的算法。为了估计模型参数，本文实现了基于模拟退火的运动参数估计算法。实验表明本文算法与传统ＢＭＡ算法相比较，较好地提高了帧间预测增益。     

超分辨测向天线阵性能的计算机仿真研究

本文在music算法的基础上首先对其进行分析、完善,并给出了在任意形状天线阵列超分辨测向的通用算法,简要地概括出了算法实现的具体步骤,同时对该通用算法做了改进、提出了插值后的music算法,该算法能展宽测向的频率范围,提高了测向的精度,然后利用music算法寺均匀直线阵、均匀正方形阵、均匀圆形阵用matlab语言进行了计算机仿真实验研究,并给出了一些具体的实验结果。     

Content-Aware Fast Motion Estimation Algorithm

In this paper, we propose the Content-Aware Fast Motion Estimation Algorithm (CAFME) that can reduce computation complexity of motion estimation (ME) in H.264/AVC while maintaining almost the same coding efficiency. Motion estimation can be divided into two phases: searching phase and matching phase. In searching phase, we propose the Simple Dynamic Search Range Algorithm (SDSR) based on video characteristics to reduce the number of search points (SP). In matching phase, we integrate the Successive Elimination Algorithm (SEA) and the integral frame to develop a new SEA for H.264/AVC video compression standard, called Successive Elimination Algorithm with Integral Frame (SEAIF). Besides, we also propose the Early Termination Algorithm (ETA) to early terminate the motion estimation of current block.We implement the proposed algorithm in the reference software JM9.4 of H.264/AVC and the experimental results show that our proposed algorithm can reduce the number of search points about 93.1%, encoding time about 42%, while maintaining almost the same bitrate and PSNR.     

一种改进的运动估计算法

运动矢量估计问题是当今一个热点问题，它在诸如视频编码、目标实时跟踪等众多领域都有着广泛的应用，目前普遍采用的估计算法是块匹配算法（BMA）。经典的块匹配算法有顺序排除算法（SEA）和多级顺序排除算法（MSEA），文章在分析两种算法的基础上。对MSEA算法做了两点改进．实验结果表明改进后的算法计算量得到有效的减少，并且保证了估计的准确性。     

改进SSMUSIC超分辨多径时延估计算法

传统的MUSIC超分辨时延估计技术是直接基于测量数据,其性能往往只对宽带且频谱近似平坦的信号较优,而对窄带信号估计性能较差。针对上述问题,本文通过利用谐波频率估计模型和DOA (Direction of Arrival)估计模型之间的等价性,将时延估计问题转化为谐波频率估计问题,提出了一种改进SSMUSIC(Signal Subspace Scaled Multiple Signal Classification)超分辨多径时延估计算法。改进后的算法采用平滑的思想和SSMUSIC算法的思想构造协方差矩阵和MUSIC谱,实现了对多径时延的超分辨估计。仿真表明,该算法能够实现对窄带信号多径时延超分辨估计且具有DP(Direct-Path)不模糊和谱峰陡峭的特点,估计性能优于传统的超分辨算法。      

图像通信中的运动估值

本文综述了图像通信中二维运动估值方法近年来的发展。分别介绍了块匹配法、像累递归法、相位相关法的基本原理和新进展。并介绍了全局运动参数的估值。     

Selective Disparity Estimation and Variable Size Motion Estimation Based on Motion Homogeneity for Multi-View Coding

Multi-view video coding (MVC) is an ongoing standard in which variable size disparity estimation (DE) and motion estimation (ME) are both employed to select the best coding mode for each macroblock (MB). This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs it from practical use. In this paper, a fast DE and ME algorithm based on motion homogeneity is proposed to reduce MVC computational complexity. The basic idea of the method is to utilize the spatial property of motion field in prediction where DE and variable size ME are needed, and only in these regions DE and variable size ME are enabled. The motion field is generated by the corresponding motion vectors (MVs) in spatial window. Simulation results show that the proposed algorithm can save 63% average computational complexity, with negligible loss of coding efficiency.      

基于运动矢量中心偏置的运动估计搜索策略

在对运动图像进行运动估计的时候,搜索策略的选择对运动估计的准确性、运动估计的速度影响重大。介绍一种基于运动矢量中心偏置的运动估计搜索策略,该搜索策略是根据运动矢量具有中心偏置分布的特点,以块误差函数最小作为调整搜索区域大小和位置的判断依据,从而实现准确的运动估计。详细叙述了算法的实现步骤,并通过实验比较,得出该算法在运算速度、信噪比方面的效果都较好。     

用于运动估计的最小化MSE滤波器设计

根据估计量的统计特性,提出了一种适用于低信噪比条件下运动估计的最小化MSE(均方误差)滤波器多尺度运动估计算法.首先,根据Cramer-Rao下界建立一个包含估计量噪声项的MSE惩罚函数.然后,最小化MSE惩罚函数设计一种用于低信噪比条件下运动估计的优化滤波器.该优化滤波器与多尺度方法相结合,使其对低信噪比条件下运动估计的精度得到了进一步提高.实验模拟表明,该方法在估计2个像素附近的噪声图像运动时,估计偏差小于0.008个像素.与传统方法相比,本文方法对低信噪比条件下的运动估计具有更高的估计精度.