基于声呐图像的水下目标检测、识别与跟踪研究综述

水下目标检测、识别和跟踪是具有重要意义的热点研究问题,在军事和民用领域都有重要的应用.鉴于此,对基于声呐图像的水下目标检测、识别和跟踪原理、方法以及典型算法的研究进展进行全面阐述.首先论述基于声呐图像的水下目标检测、图像去噪、图像分割等方面的主要进展以及典型算法和算法扩展;然后对水下目标声呐图像识别中的特征提取、特征分类方法和主要技术难点进行讨论;最后阐述基于水声信号处理和声呐图像信息的水下目标跟踪方法和算法.通过对水下目标处理过程各个过程的深入讨论和对比分析,指出基于声呐图像的水下目标检测、识别和跟踪中急需解决的关键科学问题及可能的解决思路,并对该领域的未来发展方向做进一步的展望.     

Modeling of target detection in local search for phased array radar tracking

The local search in tracking with a phased array radar can be characterized in terms of the events that can occur in the search and the probabilities of those events. In predicting the event probabilities, it is required to evaluate the expectation of a product of target detection probabilities for repeated beam-positioning in the local search. In this paper, we present a model of the local search and derive an expression for the expectation of the detection-probability product. An expression of angular measurement accuracy of target detection is also presented. Evaluation of the expressions requires numerical integration, which is computationally expensive. Approximations to the expressions are presented in analytic form, which are fairly accurate.     

Multi-robot target detection and tracking: taxonomy and survey

Target detection and tracking encompasses a variety of decisional problems such as coverage, surveillance, search, patrolling, observing and pursuit-evasion along with others. These problems are studied by several communities, that tackle them using diverse formulations, hypotheses and approaches. This variety and the fact that target related robotics problems are pertinent for a large spectrum of applications has motivated a large amount of contributions, which have mostly been surveyed according to one or another viewpoint. In this article, our objective is to go beyond the frontiers of specific communities and specific problems, and to enlarge the scope of prior surveys. We define classes of missions and problems, and relate the results from various communities according to a unifying taxonomy. We review various work related to each class of problems identified in the taxonomy, highlighting the different approaches, models and results. Finally, we propose a transverse synthesis which analyses the approaches, models and lacks that are recurrent through all the tackled problems, and isolate the current main research directions.     

Moving-in-pulse duration model-based target integration method for HSV-borne high-resolution radar

Hypersonic vehicles (HSVs) offer great advantages over airplanes and satellites, owing to their hypersonic speeds and flexible trajectories. However, HSV-borne radar may suffer from performance degradation if traditional target integration methods with stop-and-go (SAG) approximation are applied; this is especially true for high resolution radar and applications that utilize long-time coherent integration. In this paper, we consider the effects of moving-in-pulse duration (MPD) to derive an HSV-borne radar signal model without SAG approximation by specifically characterizing platform motion during the transmission and reception of a pulse. An explicit formula for wavenumber domain echo is derived using the 2-D joint stationary phase method. To mitigate the pulse-dependent echo distortion induced by the MPD effect, a target integration method based on the omega-K algorithm is proposed; this method employs revised filters for bulk focusing and revised Stolt interpolation for differential focusing to improve the overall focusing quality. The paper discusses the ambiguity functions of in-range MPD echo models, and describes the performance metrics for the integration results in both dimensions, including impulse response width (IRW), peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR). Simulation results have verified the effectiveness of the proposed method in MPD circumstances.     

Hough forests for object detection, tracking, and action recognition

Abstract—The paper introduces Hough forests, which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a categorical level. At the same time, their flexibility permits extensions of the Hough transform to new domains such as object tracking and action recognition. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time. They achieve high detection accuracy since the entries of such codebooks are optimized to cast Hough votes with small variance and since their efficiency permits dense sampling of local image patches or video cuboids during detection. The efficacy of Hough forests for a set of computer vision tasks is validated through experiments on a large set of publicly available benchmark data sets and comparisons with the state-of-the-art.     

基于PN学习的雷达目标鲁棒跟踪算法

传统雷达目标跟踪算法在强杂波环境下跟踪时会产生大量虚警估计的情况,单独跟踪或检测算法都不能对干扰杂波进行有效滤除.针对这个问题,在传统概率假设密度滤波器(PHD)算法的基础上,提出一种联合检测-跟踪-学习的目标鲁棒跟踪算法,即PN-PHD,引入属性检测器,将检测跟踪结果一起送入PN学习器,通过PN学习迭代更新检测器,并修正PHD算法的跟踪估计,以此实现在强杂波环境目标鲁棒跟踪的要求.仿真实验结果表明:PN-PHD滤波算法与传统跟踪算法相比,在强杂波环境下有效地提高了目标跟踪准确性和跟踪精度,同时也弥补了PHD算法在提供目标航迹信息方面的不足.     

采用序贯滤波的红外/雷达机动目标跟踪

当机动目标状态是非平稳和非线性时,红外传感器和雷达的目标状态方程和量测方程都是非线性和非高斯的,为了解决经典跟踪算法的残差较大或发散的问题,提出一种新的融合跟踪算法:在对红外传感器和雷达的量测数据进行时间对准和同步融合后,将融合后的量测数据送入重抽样粒子滤波器进行处理以预测和跟踪机动目标.最后给出了一个仿真跟踪实例,并与同类多雷达跟踪的效果进行了比较,说明了异类融合跟踪优于同类多雷达融合跟踪.     

基于USB2．0技术的雷达目标检测和数据采集系统的接口设计

介绍了利用USB2.0技术设计雷达目标和数据采集系统接口的方案,给出了雷达目标检测和数据采集系统的硬件结构图、USB固件程序、驱动程序及应用程序的设计方法。     

偏振光谱成像目标识别系统

设计了高光通量的光学天线、偏振模块及干涉模块的目标识别系统。通过偏振光谱成像技术,在同一视场内同时获取多个偏振方向的光谱图像,再通过图像融合算法重建目标的偏振光谱图像。在不同距离上对钢板及某型火炮进行测试,分别采用本系统与传统非偏振系统获取目标信号。结果显示,基于偏振光谱的目标识别系统信噪比高、探测距离远,与传统方法相比,本系统的目标合成图像清晰可见,抗干扰性更高,具有很高的野外适应性及稳定性。     

地面探测脉冲压缩雷达的动目标检测

介绍了基于脉冲压缩体制的地面探测雷达动目标检测的数字信号处理流程,阐述了动目标检测原理,讨论了雷达回波的函数表达式,并利用计算机进行了雷达回波仿真,在复杂的环境背景下顺利地完成了对动目标的检测。     

Waveform design and high-resolution imaging of cognitive radar based on compressive sensing

We introduce the compressive sensing(CS) theory for waveform design of cognitive radar,and then propose an algorithm for the high-resolution radar signal waveform and its corresponding imaging method based on the sparse orthogonal frequency division multiplexing-linear frequency modulation(OFDM-LFM) signal.We first present the principle of spectrum synthesis and high-resolution imaging based on OFDM-LFM signals.Then,we propose the spectrum-sparse waveform design criterion and the reconstruction algorithm for a highresolution range profile(HRRP) based on CS.Based on this,we analyze in detail the relationship between the scattering characteristics of the target and the parameters of the designed signal,and we construct the feedback of the target characteristics on the waveforms.Therefore,the "cognitive" function of radar can be achieved by adaptively adjusting the waveform with the target characteristics.Simulations are given to validate the effectiveness of the proposed algorithm.     

基于数据优先级的雷达目标跟踪偏差补偿方法

传统雷达目标跟踪偏差补偿方法雷达跟踪目标偏差补偿的精准程度较低,导致研究成果可靠性及稳定性较差。为了解决上述问题,基于数据优先级提出一种雷达目标跟踪偏差补偿方法,利用雷达极化测量目标信号,并建立三维空间坐标,引导操作数据,通过对相位控制偏差的校准操作实现对数据目标的精准测量,提升系统检验的准确性,根据数据优先级原则,对极化脉冲进行角度测量,选取适宜测量方案,设置雷达目标方向图,进行雷达目标超分辨成像,加强实验研究力度,综合考虑优化信息与操作条件,实现对雷达目标跟踪偏差补偿方法的研究。实验结果表明,基于数据优先级的雷达目标跟踪偏差补偿方法具备较高的雷达跟踪目标偏差补偿的精准程度,研究可靠性较高,稳定性有了显著提升。     

FPGA图像识别与目标跟踪系统

采用FPGA搭建图像处理系统,通过硬件算法实现图像的流水线及并行处理,实现了对具有特定颜色的物体的识别与跟踪。整个系统工作于像素频率,避免了算法的程序跑飞现象,使系统的可靠性大为提高,较好地保持了系统的低功耗特性,且优于DSP等串行处理器结合软件算法来实现的方法。     

雷达微弱目标检测的有效方法

针对低信噪比条件下微弱运动目标检测问题,提出了一种基于检测前跟踪的动态规划改进算法,根据雷达图像中包含的来袭目标运动参数信息,预测来袭目标的运动轨迹,并沿着预测运动轨迹积累能量.仿真结果表明,该算法能够较准确地估计目标航迹,计算量较小,在强杂波背景下能较好地检测出低信噪比运动目标,对于威胁预警区域较大的来袭目标能更好进行实时探测.     

Iterated wavelet transformation and signal discrimination for HRR radar target recognition

This paper explores the use of wavelets to improve the selection of discriminant features in the target recognition problem using high range resolution (HRR) radar signals in an air to air scenario. We show that there is statistically no difference among four different wavelet families in extracting discriminatory features. Since similar results can be obtained from any of the four wavelet families and wavelets within the families, the simplest wavelet (Haar) should be used. We use the box classifier to select the 128 most salient pseudo range bins and then apply the wavelet transform to this reduced set of bins. We show that by iteratively applying this approach, the classifier performance is improved. We call this the iterated wavelet transform . The number of times the feature reduction and transformation can be performed while producing improved classifier performance is small and the transformed features are shown to quickly cause the performance to approach an asymptote.     

修正最近特征分类器及其在雷达目标识别中的应用

针对最近特征线(NFL)与最近特征平面(NFP)分类器在大数据样本量与高维数时计算复杂度大的问题，依据局部最近邻准则，提出了一种新的搜索策略，使这两种分类器在保持较高识别率的同时，提高了算法的实时性能。对三类不同飞机实测数据的分类结果表明了所提方法的有效性。     

基于序贯无迹卡尔曼滤波的雷达目标跟踪方法

提出一种基于序贯无迹卡尔曼滤波的雷达目标跟踪方法。雷达跟踪系统为离散非线性系统,传统的解决方法是使用扩展卡尔曼滤波。无迹卡尔曼滤波用少量采样点表示随机变量的分布,通过非线性系统传播,能以三阶精度获得非线性变换的均值和方差的估计。为了提高无迹卡尔曼滤波的精度,用序贯无迹卡尔曼滤波方法依次处理方位角、俯仰角和距离,来进行雷达目标跟踪。通过Monte Carlo仿真,验证了该滤波算法比传统的扩展卡尔曼滤波具有更高的滤波精度和更高的计算效率。