Tracking the direction-of-arrival of multiple moving targets bypassive arrays: asymptotic performance analysis

In the companion paper of Zhou, Yip and Leung (see ibid., vol.47, no.10, p.2655-66, 1999), the maximum likelihood (ML) algorithm for tracking the DOAs of multiple moving targets by passive arrays is presented. In this paper, we provide an asymptotic performance analysis of the algorithm. The statistical consistency of the ML estimates is discussed, and their asymptotic covariances are derived. The Cramer-Rao bounds for the ML estimates are investigated, and their relative efficiency conditions are discussed. The asymptotic performance of the ML tracking algorithm is compared with that of the extended Kalman filter (EKF) under the assumption that the target waveforms are known. Finally, numerical simulation results are used to verify the theoretical results     

Localization of multiple sources with moving arrays

We consider the problem of localizing multiple narrowband stationary signals using an arbitrary time-varying array such as an array mounted on a moving platform. We assume a Gaussian stochastic model for the received signals and employ the generalized least squares (GLS) estimator to get an asymptotically efficient estimation of the model parameters. In case the signals are a priori known to be uncorrelated, the estimator allows the exploitation of this prior knowledge to its benefit. For the important case of a translational motion of a rigid array, a computationally efficient spatial-smoothing method is presented. Simulation results confirming the theoretical results are included     

运动目标循环移位舍弃像拼接算法

雷达目标一维高分辨距离像拼接是一维高分辨距离像用于雷达目标识别的前提。当目标运动时,距离像发生循环移位,采用传统的目标抽取算法如同距离舍弃法和同距离选大法将导致目标抽取结果产生像缺损。分析了目标运动对步进频率一维高分辨距离像的影响,提出了可避免抽取损失的循环移位舍弃像拼接算法。该算法基于幅度最强像修正抽取范围,将抽取范围与距离像同步移动,克服了目标运动对传统的距离像拼接算法的影响,同时避免了精确运动补偿计算量大的问题,具有工程应用价值。仿真实验验证了该算法的有效性。     

一种改进的Mean Shift运动目标跟踪算法

首先研究了Mean Shift算法(均值漂移算法),针对Mean Shift算法在跟踪视频运动物体应用中的不足,提出了将卡尔曼滤波预测的窗口和三帧差法提取的窗口通过加权的方式,产生一个新的检测窗口作为均值漂移算法的检索窗口,同时对核函数带宽和目标模型进行更新。实验表明经过优化的算法,对运动物体跟踪有了明显的改进,有了更好的鲁棒性。     

地杂波背景下运动点目标的SAR成像算法

以理想面目标回波作为地杂波的近似，对面目标特征、面目标回波信号及其处理进行了分析，给出了以面目标为背景的运动点目标SAR成像算法，通过具体实例模拟仿真出独立运动点目标的SAR成像图和以面目标为背景的运动点目标SAR成像图。     

基于时频域的地面动目标二维检测算法

针对地面动目标检测中强地杂波导致的匀速、匀加速直线运动目标检测概率降低的问题,提出了在时频域对目标信号能量进行积累以提高信噪比,从而提高地面动目标检测概率的算法。该方法首先将回波信号下变频至零中频,而后基于目标的运动特性,利用短时傅里叶变换将回波变换至时频域进行能量积累提高信噪比,最后在时频域进行二维检测,提高了动目标的检测概率。仿真实验验证了算法的有效性。     

一种基于FFT快速算法的空间多目标跟踪算法

本文针对阵列天线提出了一种基于FFT快速算法的多目标跟踪算法.与传统的基于特征值分解的DOA估计不同,此算法利用阵元域与空间域之间的傅里叶变换关系,对阵元输出的一次快拍数据进行快速傅里叶变换,得到信号的离散空域频谱,然后对其进行补零或内插,得到精确的空域谱来获得目标的DOA.由于此算法具有较低的计算复杂度,可以对空间快...     

动目标多视角观测图像的三维去模糊方法

当目标在多视角观测成像系统中有相对运动时,所获取的多观测点图像是模糊的且各视角图像的模糊是不一样的,模糊核长度和方向都不同。针对这一问题,提出了多视角观测图像的三维去模糊方法。现有的图像去模糊方法主要是对单视角观测图像进行二维去模糊的,没有考虑目标多视角观测图像的模糊核之间的对应关系。文中从三维空间到二维观测面的映射关系出发,建立多视角观测图像的模糊路径之间的对应关系。先采用单观测图像去模糊的方法获取两视角观测图像的模糊核,并对模糊核进行精确化处理,得到单像素点宽的模糊核路径。再通过多视角观测图像模糊核路径之间的对应关系,估计其它观测图像的模糊核路径。最后,对多视角观测图像进行统一去模糊,并对去模糊后的多视角观测图像进行三维重建。实验结果表明,文中方法能较好地去除目标多视角观测图像的三维模糊,提高了目标的三维重建质量。     

采用频率步进信号的多目标三维空间定位算法

频率步进信号是发射载频步进变化的子脉冲串合成的大带宽信号,具有较高的距离分辨率,阵列天线是借助阵列信号处理的空间谱估计提高方位分辨率。提出了一种利用频率步进信号和十字接收阵列对多目标进行三维空间定位的新方法。该方法充分利用了频率步进信号的特点,通过空时域MUSIC算法和自适应波束形算法对多目标的方位和距离二维参数进行估计,最后应用简单的关联算法,实现多目标三维坐标的估计。该方法仅需要一维谱峰搜索,计算量适中,能实现目标的方位和距离估计结果的自动配对,且有较高的分辨率。计算机仿真试验结果证实该方法的有效性。     

Radar resolution of moving targets

Conventional resolution theory is based on the assumption of constant range rate, and it neglects distortions of the modulation function as well as effects of the antenna pattern. Among the generalizations of the theory reported in the literature, some include the antenna pattern and others retain higher-order range derivatives in both carrier term and modulation function. The first part of this paper is tutorial and discusses the significance of the various generalizations of the ambiguity function. It is shown that an important class of problems can be accommodated when the conventional theory is extended by permitting higher-order range derivatives in the carrier term. Target resolvability on the basis of motion then depends only on the relative phase shifts introduced over the signal duration. In the second part, the ambiguity function is formally extended to arbitrary target motion. The modified theory retains the simplicity of the conventional approach and allows utilization of well-known results.     

一种基于多特征自适应融合的运动目标跟踪算法

针对复杂背景下的运动目标跟踪问题,提出了一种基于多特征自适应融合的运动目标跟踪算法。通过构建目标与背景的图像特征分布方差的比值函数来衡量目标与背景间的区分度,采用各特征的区分度对特征集进行线性加权自适应表示运动目标并集成在基于核的跟踪方法中。为了克服模板更新过程中的漂移,通过计算前后相邻两帧间目标模型的相似度函数,对跟踪模板进行自适应更新。基于生物视觉认知理论,目标的颜色、边缘特征以及纹理特征被用来实现基于多特征自适应融合的运动目标跟踪算法。仿真实验表明:采用本文算法能有效地对复杂背景下的运动目标进行跟踪。     

基于IRSRCKF高超声速目标跟踪

以临近空间高超声速目标作为研究对象,提出一种迭代简化平方根容积卡尔曼滤波算法(IRSRCKF)。该算法以平方根容积卡尔曼滤波算法(SRCKF)为理论框架,针对SRCKF算法对于系统的状态方程为线性时需用容积点进行加权求和的缺点,对SRCKF算法的时间更新环节线性简化,提高了实时性;结合迭代运算的思想,充分利用量测信息,对量测更新过程进行迭代运算,提高了跟踪精确度。仿真验证结果表明,该算法具有较高的精确性和有效性,为临近空间高超声速目标的跟踪提供了一种新方法。     

Application of antenna arrays to mobile communications. II.Beam-forming and direction-of-arrival considerations

Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing     

基于多颜色空间信息融合的运动船舶检测算法

传统的目标检测方法是将图像灰度化,导致很多重要信息丢失,而帧差法与背景减法相结合的检测方法虽然能很好克服单独使用一种方法所产生的问题,但仍存在目标内部不充实现象。针对以上问题,提出一种基于多颜色空间信息融合的三帧差分法与背景减法相结合的运动船舶检测算法。该算法首先利用帧差法和背景减法原理对选取的最佳颜色分量进行差分处理,然后将两种差分结果的颜色分量对应进行"与"运算,最后采用"或"运算实现颜色分量融合,获取内部充实、轮廓清晰的运动目标。     

基于Labview的雷达动目标仿真

为使雷达能够更好地检测杂波中的运动目标,首先介绍了软件Labview,然后建立雷达信号的数学模型.根据雷达动目标显示、动目标检测原理,通过双脉冲对消,三脉冲对消和盲速处理的方法,用Labview对其进行仿真.仿真结果表明:在动目标显示(MTI)和动目标检测(MTD)中使用滤波器组,能够得到更好的改善因子,降低系统检测目标所需的信噪比,去除固定杂波,提取运动目标的回波,改善在杂波背景下检测运动目标的能力.     

Tracking sensitivity: a practical algorithm

A new algorithm for tracking sensitivity, based on a matrix power series, is given. It requires less computation than previous methods, but the allowable range of the global parameter is restricted.     

基于三维分层图模型的复杂情况下多运动目标跟踪

复杂情况下的多运动目标跟踪是视频监控中的关 键问题,在多目标运动中相互遮挡时二维视觉信息容易丢失而造 成无法准确跟踪识别目标。本文采用kinect摄像机获取三维视觉信息,从节点、边、空间结 构三个层次上建立目标的三维分 层图模型表征目标的三维特征,并在每层进行通过视频帧间的匹配从而获得三维分层图模型 匹配结果,并根据匹配结果先初 步分析目标跟踪情况,如发生遮挡则通过遮挡区域聚类块与三维分层图模型中各特征匹配确 定其匹配结果,从而得到多运动 目标在复杂运动情况中的跟踪结果。实验表明,在实验室kinect拍摄的视频序列上当目标出 现遮挡等复杂情况,也能取得较 好的跟踪结果,在实验视频中比经典方法的跟踪总体性能指标改善约3%,说明本方法能较好 地实现复杂情况下的多运动目标跟踪。     

基于调幅广播信号的动目标时差提取方法

分析了调幅信号的循环平稳性,并结合CCC方法得到了基于调幅信号的固定目标时差提取方法;进一步在分析CCC方法不足的基础上得到了运动目标时差提取方法AM-ECCC方法,仿真结果表明该方法不仅比相关方法性能有明显的提高,并且具有很好的抗噪性能。     

Radio imaging of moving targets: the one-dimensional case(microwave range)

The detection of moving targets by passive radio temperature measurements is studied. A radio image is reconstructed by linear spatial filtering of simultaneous measured brightness samples, obtained by multiple regularly spaced antenna beams. With fixed spatial filters, the receiver integration time must be short enough to freeze motion, thus limiting the signal-to-noise ratio obtained in the reconstruction image. It is shown that in the one-dimensional case a time-varying matched spatial filter yields the same signal-to-noise ratio in the reconstructed image as would be obtained from a stationary target with a fixed matched filter     

激光雷达跟踪算法研究

形心跟踪算法因为简单、执行速度快而得到广泛采用。然而常规形心跟踪算法因激光雷达图像受到散斑噪声影响而变得不适合。改进算法结合了图像的滤波处理,提高了适用范围。但是这种基于图像灰度均值分割的形心跟踪算法对于小目标图像都会跟踪失败。分析了大目标图像和小目标图像的直方图,指出其均由一个背景的尖峰和目标的拖尾组成,理想的分割阈值点应为尖峰和拖尾的临界点。据此提出了基于累积直方图的形心跟踪算法。此算法以图像的累积直方图拐点为分割阈值,不受目标区面积大小的影响。处理结果表明,对于大目标图像和小目标图像均有较好的效果,并且保持了常规形心跟踪算法执行速度快的特点。