基于时间-Doppler分析的海杂波背景下小目标检测

为解决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,提出一种基于时间-Doppler分析法来检测小目标。该算法用离散短时傅里叶变换对实测海杂波数据分析得到不同距离门的时间-Doppler速度图,通过分析和比较距离门含有小目标的Doppler展宽和Doppler速度大小与其他距离门不含有小目标的Doppler展宽和速度大小的不同,即可检测出小目标。实验证明,该算法是有效的。     

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     

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。     

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.     

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。     

Harbor surveillance radar detection performance

System requirements for harbor surveillance radars are reviewed. Experimental sea clutter data are presented and shown to be closely matched by a log-normal clutter model. Characteristics and parameters of the log-normal clutter model are described. Detection performance against a steady target in a log-normal clutter background, using logarithmic receivers, is provided. A log-normal target model is described. Detection curves for log-normal fluctuating targets in log-normal clutter are developed. A Constant False Alarm Rate (CFAR) processor that adaptively functions in log-normal clutter is described. CFAR detection performance is derived and presented in the form of CFAR loss curves.     

基于海杂波随机游走模型的多重分形谱分析

海杂波具有非线性、非平稳和时变特性,多重分形测度分析是对物理系统局部奇异性的一个完整描述。本文根据海杂波电磁散射的经典模型——随机游走模型,利用随机微分理论对海杂波的物理特性进行了系统地建模分析。建立了描述海杂波电磁散射时变特性的随机微分方程,利用Itô公式得到了海杂波散射信号幅度和相位的扩散过程模型,并利用多重分形谱分析了该模型的奇异性。为了验证其准确性,与加拿大McMaster大学IPIX雷达实测海杂波数据的多重分形谱进行对比,实验结果表明两者完全一致,从而表明基于随机游走的随机微分模型是描述海杂波动力学机理的有效算法。     

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.     

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。     

Statistical normalization of spherically invariant non-Gaussian clutter

Conventional detection in active sonar involves comparing the normalized matched filter output power to a fixed preset threshold. Threshold crossings from contacts of interest are labeled as detections and those from undesired clutter echoes as false alarms. To maintain a constant false-alarm rate (CFAR) in the presence of strong transient clutter, the system can either increase the threshold or apply some function that suppresses this background down to an acceptable level. The latter approach leads to a more consistent background on the display, which enables operator-assisted detection. Background clutter suppression should not come at the expense of contact detection; to maximize the probability of detection (PD) for a given probability of false alarm (PFA), the likelihood ratio test (LRT) is used. However, the LRT does not address display issues, since the threshold that achieves a desired PFA varies with the input distribution. Ideally, the LRT output is monotonically transformed using a "statistical normalizer" (SN) that returns a consistent CFAR background without degrading the optimized PD. Within the radar community, clutter suppression is proposed using a LRT tuned to a K-distributed spherically invariant random vector (SIRV) model. However, this model does not lend itself to SN, as a closed-form expression for the LRT output density does not exist. In contrast, the proposed SIRV clutter model, with Pareto distributed power, leads to a closed-form density from which the SN function is readily derived. This combined Pareto-LRT/SN detector nearly matches the optimized PD performance of the K-distributed LRT and maintains a consistent CFAR background for display purposes.     

Adaptive techniques for radar detection and identification of objects in an ocean environment

New technology opens very interesting possibilities in regard to target adaptive radar systems. The paper illustrates this by considering a multifrequency radar concept applied to the detection and identification of ships against a sea clutter background.     

Active learning for detection of mine-like objects in side-scan sonar imagery

A data-adaptive algorithm is presented for the selection of the basis functions and training data used in classifier design with application to sensing mine-like targets with a side-scan sonar. Automatic detection of mine-like targets using side-scan sonar imagery is complicated by the variability of the target, clutter, and background signatures. Specifically, the strong dependence of the data on environmental conditions vitiates the assumption that one may perform a priori algorithm training using separate side-scan sonar data collected previously. In this paper, a novel active-learning algorithm is developed based on kernel classifiers with the goal of enhancing detection/classification of mines without requiring an a priori training set. It is assumed that divers and/or unmanned underwater vehicles (UUVs) may be used to determine the binary labels (target/clutter) of a small number of signatures from a given side-scan collection. These sets of signatures and associated labels are then used to train a kernel-based algorithm with which the remaining side-scan signatures are classified. Information-theoretic concepts are used to adaptively construct the form of the kernel classifier and to determine which signatures and associated labels would be most informative in the context of algorithm training. Using measured side-looking sonar data, the authors demonstrate that the number of signatures for which labels are required (via diver/UUV) is often small relative to the total number of potential targets in a given image. This procedure designs the detection/classification algorithm on the observed data itself without requiring a priori training data and also allows adaptation as environmental conditions change.     

Mitigation techniques for non-Gaussian sea clutter

This paper deals with the problem of coherent radar detection of targets embedded in clutter modeled as a compound-Gaussian process. We first provide a survey on clutter mitigation techniques with a particular emphasis on adaptive detection schemes ensuring the constant false-alarm rate (CFAR) property with respect to all of the clutter parameters. Thus, we propose a novel decision rule based on a recursive covariance estimator, which exploits the persymmetry property of the clutter covariance matrix. Remarkably, the devised receiver is fully CFAR in that its threshold can be set independently of the clutter distribution as well as of its covariance, even if the environment is highly heterogeneous; namely, the disturbance distributional parameters vary from cell to cell. At the analysis stage, we compare the performance of the novel detector with some classical radar receivers such as that of Kelly and the adaptive matched filter both in the presence of simulated as well as on real radar data, which statistical analysis has shown to be compatible with the compound-Gaussian model. The results show that the new receiving structure generally provides higher detection performance than the others and, for a fluctuating target, it uniformly outperforms the counterparts. We also provide a discussion on the CFAR behavior of the analyzed receivers as well as on their computational complexity.     

Use of nautical radar as a wave monitoring instrument

Common marine X-Band radars can be used as a sensor to survey ocean wave fields. The wave field images provided by the radars are sampled and analysed by a wave monitoring system (called WaMoS II) developed by the German research institute GKSS. This measuring system can be mounted on a ship, on offshore stations or at coastal locations. The measurement is based on the backscatter of microwaves from the ocean surface, which is visible as ‘sea clutter' on the radar screen. From this observable sea clutter, a numerical analysis is carried out. The unambiguous directional wave spectrum, the surface currents and sea state parameters such as wave periods, wave lengths, and wave directions can be derived. To provide absolute wave heights, the response of the nautical radar must be calibrated. Similar to the wave height estimations for Synthetic Aperture Radars, the so-called ‘Signal to Noise Ratio' leads to the determination of the significant wave height (H_S). In this paper, WaMoS II results are compared with directional buoy data to show the capabilities of nautical microwave radars for sea state measurements.     

Study on dim target detection and discrimination from sea clutter

Dim target detection from sea clutter is one of the difficult topics in ocean remote sensing application. By aiming at the shortcoming of false alarms when using track before detect (TBD) based on dynamic programming, a new discrimination method called statistics of direction histogram (SDH) is proposed, which is based on different features of trajectories between the true target and false one. Moreover, a new series of discrimination schemes of SDH and Local Extreme Value method (LEV) are studied and applied to simulate the actually measured radar data. The results show that the given discrimination is effective to reduce false alarms during dim targets detection.     

一种新的用于极化SAR图像船只检测的散射相似性测度

本文提出了一种新的用于极化合成孔径雷达（SAR）海上船只目标检测的测度。首先利用散射相似性参数研究船只与海杂波散射机制的差异。基于这些差异,提出了一种新的船只检测测度,该测度能够有效区分船只目标和海杂波。然后提出了利用核密度估计（KDE）方法对该测度进行建模的方法。基于统计模型,实现了自适应恒虚警率（CFAR）的检测方案。本文利用多景C波段RADARSAT-2极化SAR（Pol-SAR）数据上进行实验,系统分析了所提出测度的海杂波拟合性能与船只检测性能,并与两个经典的极化测度进行了比较,实验和比较结果证明了所提的测度的可行性。     

浅谈海域使用后评估指标体系的建设

围填海海域使用后评估工作是国际上动态地对海洋工程进行监测管理方面应用较为成熟的一种管理方法。但在我国还处于空白。开展海域使用后评估工作,能够总结项目建设经验和教训,从而提高海洋项目决策、工程实施和管理水平,使资源更加合理利用和管理。文章对于指标的挑选是以合理的准则为基础的,先是进行了构建海域使用项目这一步骤,然后在对项目进行评估之后创建了相应的指标体系框架,从而结合层次分析法的综合模型确定了指标体系中的各项指标。文章采用分项设计和定量模型相结合的方法,从预期目标、执行情况、社会影响、可持续性4个方面19个指标构建后评估指标体系,对于围填海项目监管具有一定的参考作用。     

Super-resolution processing for HF surface wave radar based onpre-whitened MUSIC

Owing to the decametric wavelength, the angular resolution of high-frequency surface wave radar (HFSWR) is usually coarse, especially when dimensions of antenna arrays are restricted such as in shipborne HFSWR applications. In this paper, the relative strength of atmospheric noise and sea clutter that will heavily degrade the capabilities of HFSWR in target detection and resolution are calculated, then a method for estimating the spatial covariance matrix of background noise is presented. By introducing a pre-whitening procedure in multiple signal classification (MUSIC), the resolution performance of MUSIC is enhanced in spatially colored noise environment. Results with data from the aircraft detecting experiment conducted by the Research Institute of Electronic Engineering of Harbin Institute of Technology in 1994 and simulated data of two targets show that pre-whitened MUSIC can provide a better resolution and accurate determination of target number. Furthermore, a post-processing method is proposed to eliminate the sidelobe of spatial spectrum arising from the estimation errors of a noise covariance matrix