基于局部回波幅值统计的海杂波背景下小目标检测

针对传统舰载雷达和岸基雷达很难检测出海杂波背景下小目标的现状,提出一种基于局部回波幅值统计的方法来检测海杂波背景下小目标.该算法基于数学统计思想:计算不同距离单元海杂波的幅值的最大值和标准偏差,分析发现距离单元含有小目标回波的最大幅值明显小于其它距离单元只含有海杂波回波的最大幅值,距离单元含有小目标回波的幅值的标准偏差明显大于距离单元只含有海杂波回波的幅值的标准偏差,因此可以在对小目标没有任何先验知识条件下,较准确地检测出海杂波背景下的小目标.实验证明,该算法是有效的.     

一种改进的地波雷达邻近距离单元格一阶海杂波对消方法

基于邻近距离单元格海杂波特性相关性,分析了不同相干积累时间所导致的目标距离模糊,改进了邻近距离单元格海杂波对消法,此方法克服了邻近单元格海杂波对消所引起的目标对消问题。将研究方法中使用的杂波抑制处理结果应用于CFAR船只目标检测算法,可明显降低目标检测中虚警的概率。     

基于递归定量分析的实测海杂波中的小目标检测

提高海杂波中小目标的检测能力,在海洋监视、军事侦察和浮冰检测等军事、民用领域中有着广泛需求,因此海杂波的研究一直是雷达技术领域研究的热点问题。首次将递归定量分析方法引入实测海杂波数据分析中,采用该方法计算海杂波时间序列相空间重构后各递归参量并将其应用于海杂波背景下小目标检测,得到了较好的结果。实验初步证明了采用递归定量分析方法对海杂波数据进行分析和小目标检测是可行并且有效的。     

基于多帧聚类的紧凑型地波雷达海上目标航迹起始方法

紧凑型地波雷达是专属经济区内海上船只目标监测预警的重要手段。由于其发射功率低、目标回波信噪比低,在对海上船只目标检测过程中较低的检测概率极易导致目标漏检,采用序贯类方法难以及时起始航迹。对此,本文通过分析杂波和目标的地理位置以及径向速度随时间变化的特点,提出了一种基于多帧聚类的紧凑型地波雷达海上目标航迹起始方法。该方法利用目标和杂波在连续多帧内运动特征的差异,在具有噪声的基于密度的空间聚类算法（Density-Based Spatial Clustering of Applications with Noise, DBSCAN）求解聚类ε邻域的过程中增加运动特征约束,将各船只目标在多帧内的点迹聚类为不同的簇,实现海上船只目标点迹与杂波点的区分,将簇内点迹按时间顺序顺次连接得到起始航迹。利用仿真与实测紧凑型地波雷达数据开展了航迹起始实验,结果表明,与逻辑法相比,本文方法得到的航迹起始时间平均提前了5.95 min,丢点率平均降低了12.68%,解决了海上弱目标航迹起始时间滞后的问题,适用于杂波区目标与雷达远端船只目标的航迹起始。     

基于船载雷达图像的海上船只检测方法

随着雷达成像技术和高分辨率光栅显示技术的发展和应用,基于船载雷达图像的船只检测成为可能.海上船只检测的主要困难之一是雷达图像中包含固有的海面背景杂波.传统的雷达船只检测方法,如恒虚警率法(CFAR),以杂波分布模型为基础,计算待检测窗口中的信号统计分布来确定自适应阈值,取得了一些成果.但是,当海面背景杂波和船只目标的回波强度在同一数量级,甚至船只目标淹没在海面背景杂波中时,就难以确定一个有效的阈值将船只目标从雷达图像中提取出来.在分析海面背景杂波和船只目标的相关差异性基础上,提出了一种基于船载雷达序列图像的海上船只快速检测方法.该方法首先对相邻两幅图像进行互相关性分析,在两幅图像中的同一位置提取一定尺寸的移动窗口,计算其互相关函数值,窗口移动一个步长,重复操作直至遍布整幅图像,形成一幅由灰度图像互相关函数值组成的相关图像.然后使用概率神经网络模型(PNN模型)来估计相关图像背景杂波的灰度概率密度分布函数(PDF),应用CFAR技术,使用二分法求解一个区分船只和背景噪声的自适应整体阈值,并根据阈值将相关图像二值化,其中大于阈值的像元作为候选的船只目标信息,小于阈值的像元则为海面背景杂波.最后使用连通性8-邻域准则统计各个候选船只目标区域的像元数,并与预先定义的最小船只目标像元数进行比较,偏小的候选船只目标区域作为虚警去除,保留下来的候选船只目标区域即为船只检测结果.研究显示,如果图像序列中没有船只目标信息,则三维相关图像比较平整.相反,如果图像序列中含有船只目标信息,则三维相关图像上有峰值被检测出,通过测量峰值的高度,就能判断存在可能的船只目标.运用X波段船载雷达序列图像对本文提出的海上船只检测方法进行了测试.测试结果表明,该检测方法具有很好的船只检测效果,得到的船只检测结果与目视判别的结果一致.而且该检测算法原理简单,计算速度快,易于实时处理,具有广阔的应用前景.     

一种适用于水声通信的Doppler估计算法

在水下声通信中,收发双方相对运动产生的Doppler效应会导致信号的伸缩,引起信噪比的降级,需要采用Doppler补偿措施。在补偿前需要得到Doppler估计。文中提出了一种利用DFT进行Doppler估计的有效算法,该算法通过估计频率偏移来计算Doppler率。仿真结果验证了该算法的有效性。     

高频地波雷达海杂波抑制的时频-矩阵联合法

高频地波雷达海面回波信号中含有很强的海杂波分量,造成虚警与漏警现象,严重影响了对船只目标的探测与管理。本文提出基于时频分析与矩阵分解相联合的海杂波抑制方法。该方法从时频域中检测出海杂波的时变频率范围,同时通过Hankel降秩得到各回波分量奇异值及瞬时频率估计;通过频率匹配及矩阵重构给出海杂波的时域分量,予以剔除。实测雷达数据处理结果表明,在保留目标时域回波与频谱特征的情况下,本方法成功实现了海杂波分量的分离与抑制,提高了对海面目标的探测性能。     

地波雷达目标检测跟踪联合处理粒子滤波方法

针对固定粒子数PF-TBD算法计算量大、复杂环境下地波雷达海上船只目标检测与跟踪性能不佳的问题,本文将粒子滤波方法应用于地波雷达船只目标检测与跟踪中,提出了基于自适应粒子滤波的地波雷达目标检测与跟踪联合处理方法。该方法结合地波雷达回波谱中目标展宽特性,充分利用了地波雷达回波谱中面目标的粒子权重信息来设置粒子自适应采样策略,提高了目标检测和跟踪联合处理的效果。通过地波雷达实测数据的目标跟踪结果及与同步AIS信息的比对分析,结果表明:提出的检测跟踪联合处理方法在对低信噪比、快速机动等复杂环境下的多目标跟踪时,可提高目标整体跟踪性能。     

利用现场海态观测信息的地波雷达一阶海杂波内目标检测方法

为解决地波雷达一阶海杂波内船只目标检测的难题,提出了一种利用风浪流等现场海态观测信息检测海杂波内目标的方法。该方法通过对比实测雷达回波谱和基于现场海态观测信息重构的无目标回波谱,发现实测雷达回波谱中一阶谱频率范围和左右峰值比等特征的异常变化,实现船只目标的检测。将该方法应用于实测地波雷达数据,并利用同步的船舶自动识别系统信息(AIS)对检测结果进行了验证,结果表明本文方法是一种有效的一阶海杂波内目标的检测方法。     

高频地波雷达海杂波的边界约束循环对消

在对海洋监视监测的过程中,高频地波雷达的船只目标检测能力与其对海杂波的抑制能力密切相关。但是,传统海杂波时域抑制方法存在目标回波参数与海杂波相近时难以区分其各自分量,对消时目标被误消除的问题。针对上述问题,本文提出了一种适用于高频地波雷达海杂波的边界约束循环对消方法。该方法综合海杂波频移理论和实际海杂波特性分析制定出边界条件,约束建模对消过程,实现海杂波分量的抑制。实测地波雷达数据和船舶自动识别系统(AIS)数据检验的结果表明,本文方法克服了传统方法的不足且信杂比改善更加稳定,能够更加有效地实现海杂波循环对消。     

Ionospheric decontamination and sea clutter suppression for HF skywave Radars

In this paper, a cascaded correction and suppression method of reducing ionospheric phase path contamination and sea clutter to enable detection of targets travelling at speeds near the Bragg Doppler is addressed. The Hankel rank reduction (HRR) technique based on singular value decomposition (SVD) has been used to estimate the ionospheric phase distortion and suppress the sea clutter. Simulation results show that such a technique is helpful for the worse conditions when the target masking effect happens even after ionospheric phase decontamination. Finally, an attempt to combine another phase decontamination algorithm based on the piecewise polynomial phase modeling with the clutter cancellation stage for faster phase fluctuation is discussed briefly and some results are given.     

海杂波微弱目标分形检测

The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been used successfully to extract the time-domain Hurst exponent of sea-clutter series. Since the frequency of the sea clutter mainly concentrates around Doppler center so that we consider to extract frequency-do- main fractal characterization and then detect a weak target within sea clutter by using the difference of frequency-domain fractal characterization. The generalized detrended fluctuation analysis （GDFA） is more flexible than traditional DFA owing to its smoothing action for the clutters. In this paper, we apply the GDFA to evaluate the generalized Hurst exponent of sea-clutter series in the frequency domain. The difference of generalized Hurst exponents between different sea-clutter range bins would be used to determine whether the target exists. Moreover, some simulations with the real IPIX radar data have also been demonstrated in order to suooort this conclusion.     

Applying chaos to radar detection in an ocean environment: anexperimental study

Ship navigation through ice-infested waters is a problem of deep concern to the oil exploration industry of Northern countries. Conventional marine radars do not perform satisfactorily in detecting small targets such as small pieces of iceberg. This paper reports a new method for detection in an ocean environment. The approach is based on the recent observation that sea clutter, radar echoes from the sea surface, can be modeled as a nonlinear deterministic dynamical system which can be used as an alternative to the conventional stochastic process. Based on this model, detection in sea clutter is considered as dynamical system classification instead of statistical hypothesis testing. Two dynamical detection methods are proposed. The first one uses a dynamical invariant called the attractor dimension to distinguish a target from a pure clutter process. The second approach tries to detect the existence of a target by observing the “difference” of the motion of the target and the clutter process. To show the validity of the idea of dynamical detection in sea clutter, real sea clutter, and target data were used in this study     

高频地波雷达海杂波背景下的船只目标检测研究进展

针对海杂波对高频地波雷达目标检测的干扰问题,分目标处于海杂波谱区之内和之外两种情况综述了海杂波背景下的目标检测方法。对于海杂波内目标检测的难题,介绍了一种基于现场海态同步观测信息的检测新思路以及初步验证结果。对国内外相关研究进展的归纳总结和新思路的提出,为提出更加有效的海杂波干扰下的目标检测方法提供了重要的参考。     

高频地波雷达海杂波背景下的船只目标检测研究进展

针对高频地波雷达目标检测的海杂波干扰问题,分为目标处于海杂波谱区之内和之外两种情况综述了海杂波背景下的目标检测方法。对于海杂波谱区内目标检测的难题,介绍了一种基于现场海态同步观测信息的检测新思路以及初步验证结果。对国内外相关研究进展的归纳总结和新思路的提出,为深入研究相关方法,解决海杂波干扰下的目标检测问题提供了重要的参考。     

Chaotic radar signal processing over the sea

It is demonstrated that the random nature of sea clutter may be explained as a chaotic phenomenon. For different sets of real sea clutter data, a correlation dimension analysis is used to show that sea clutter can be embedded in a finite-dimensional space. The result of correlation dimension analysis is used to construct a neural network predictor for reconstructing the dynamics of sea clutter. The deterministic model so obtained is shown to be capable of predicting the evolution of sea clutter. The predictive analysis is also used to analyze the dimension of sea clutter. Using the neural network as an approximation of the underlying dynamics of sea clutter, a dynamic-based detection technique is introduced and applied to the problem of detecting growlers (small fragments of icebergs) in sea clutter. The performance of this method is shown to be superior to that of a conventional detector for the real data sets used here     

Sea-clutter modeling using a radial-basis-function neural network

Recently, neural networks have been proposed for radar clutter modeling because of the inherent nonlinearity of clutter signals. This paper performs an analysis of the practicality of using a radial basis function (RBF) neural network to model sea clutter and to detect small target embedded in sea clutter. An experiment using an instrumental quality radar was carried out on the eastcoast of Canada to create a rich sea clutter and small surface target database. This database contains both staring and scanning data under various environmental conditions. Using data-sets with different characteristics, we investigate the effects of quantization error, measurement noise, generalization of the neural net over ranges and sampling rate on the RBF clutter model. Despite these physical limitations, the RBF model was shown to approach an optimal predictive performance. The RBF predictor was also applied to detect various small targets in this database based on the constant false alarm rate (CFAR) principle. This RBF-CFAR detector was demonstrated to be able to detect small floating targets even in rough sea conditions     

基于模型相似度拟合的海杂波统计方法

本文提出一种基于模型相似度拟合的海杂波统计方法。首先根据合成孔径雷达(SAR)图像计算瑞利分布、对数正态分布、韦布尔分布、K分布、G0分布5种经典的海杂波分布的概率密度函数,然后根据模型间的相似度准则拟合得到新的海杂波分布模型。文章利用四景不同类型的真实SAR数据对算法的拟合性能进行了评价,结果显示利用该算法得到的拟合模型与真实SAR数据的平均Kullback-Leibler距离仅为0.015 84,远优于其他分布模型。基于该拟合模型的恒虚警率舰船检测算法对四景SAR数据的平均检测精度达到95.75%,在控制虚警和漏检方面均优于采用其他模型的同类方法。     

Radial velocity of ocean surface current estimated from SAR Doppler frequency measurements—a case study of Kuroshio in the East China Sea

Ocean currents are a key element in ocean processes and in meteorology, affecting material transport and modulating climate change patterns. The Doppler frequency shift information of the synthetic aperture radar (SAR) echo signal can reflect the dynamic characteristics of the sea surface, and has become an essential sea surface dynamic remote sensing parameter. Studies have verified that the instantaneous Doppler frequency shift can realize the SAR detection of the sea surface current. However, the validation of SAR-derived ocean current data and a thorough analysis of the errors associated with them remain lacking. In this study, we derive high spatial resolution flow measurements for the Kuroshio in the East China Sea from SAR data using a theoretical model of shifts in Doppler frequency driven by ocean surface current. Global ocean multi observation (MOB) products and global surface Lagrangian drifter (GLD) data are used to validate the Kuroshio flow retrieved from the SAR data. Results show that the central flow velocity for the Kuroshio derived from the SAR is 0.4–1.5 m/s. The error distribution between SAR ocean currents and MOB products is an approximate standard normal distribution, with the 90％ confidence interval concentrated between –0.1 m/s and 0.1 m/s. Comparative analysis of SAR ocean current and GLD products, the correlation coefficient is 0.803, which shows to be significant at a confidence level of 99％. The cross-validation of different ocean current dataset illustrate that the SAR radial current captures the positions and dynamics of the Kuroshio central flow and the Kuroshio Counter Current, and has the capability to monitor current velocity over a wide range of values.