HRR Detector for Slow-Moving Targets in Sea Clutter

The radar detection of targets in the presence of sea clutter has historically relied upon the radial velocity of targets with respect to the radar platform either by exploiting the relative target Dopplers (for targets with sufficient radial velocity) or by discerning the paths targets traverse from scan to scan. For targets with little to no radial velocity component, though, it can become quite difficult to differentiate targets from the surrounding sea clutter. This paper addresses the detection of slow-moving targets in sea clutter using a high resolution radar (HRR) such that the target has perceptible extent in range. Under the assumption of completely random sea clutter spikes based on an epsiv-contaminated mixture model with the signal and clutter powers known, optimal detection performance results from using the likelihood ratio test (LRT). However, for realistic sea clutter, the clutter spikes tend to be a localized phenomenon. Based upon observations from real radar data measurements, a heuristic approach exploiting a salient aspect of the idealized LRT is developed which is shown to perform well when applied to real measured sea clutter.     

基于实测数据的海杂波与海面小目标特征分析

目标特性是雷达目标检测识别等精细化处理的基础。设计检测器主要是设计 1个检测统计量,使之在有无目标时有尽可能大的差别。经典的检测主要用幅度特性、相关性等。利用 X波段雷达 2~5级海况下航道浮标实测数据,分别对比分析海况连续变化下海杂波和海面小目标的时间相关性、空间相关性、相对平均幅度(RelativeAver-ageAmplitude,RAA)、相对多普勒峰高(RelativeDopplerPeakHeight,RDPH)和相对多普勒向量熵(RelativeVector Entropy,RVE)5种特征的变化情况。大量实测数据验证表明,随着海况等级增加,海杂波与海面小目标的时空强相关数值逐渐变小:2~5级海况下,RAA具有较好的可区分性;2~3级海况下,RDPH据有较好的可区分性,4~5级海况下区分效果不理想;5级海况下,RVE的具有较好的可分效果。结论对于海杂波背景下雷达目标特征检测方法优化设计具有重要意义。     

利用神经网络的海杂波幅度分布参数估计方法

海杂波是制约对海雷达探测性能的主要因素之一,掌握其特性,具有十分重要的意义。经典海杂波统计模型在参数估计方法上以传统统计学理论为基础,在样本数较少的情况下,估计结果往往较差,导致建模准确度下降。此外,在复杂非均匀探测背景下,难以实现海杂波模型参数的准确实时估计。针对该问题,文章将深度神经网络模型引入海杂波参数估计领域,通过构建合理的模型,使其具备海杂波幅度分布模型的高精度参数估计能力。该方法采用直方图统计的方法进行数据预处理,合理划分输入数据标签的分组区间,构建数据集训练神经网络,并利用测试数据得到神经网络估计结果。仿真数据和X波段IPIX雷达实测数据验证结果表明,与传统数理统计估计方法相比,该算法明显提升了海杂波统计模型参数估计精度。     

Radar Partial Coherence Theory: An Introduction

The nature of physical phenomena is such that scattering from portions of an object, a number of objects, or clutter, is not completely unrelated; the underlying environment causes some degree of order in the phenomenon. Radar partial coherence theory describes a structure for the general target, or clutter, and its relationship to radar cross section, waveform coding, and the radar output signal. The clutter ambiguity function is introduced for extended bodies and embraces the (Woodward) ambiguity function for a point target. Due to nonlinear effects caused by partial coherence within the general target, radar signals and targets are formulated in terms of mutual coherence functions. The basic quantities describing the radar output are 1) the radar mutual coherence function (formulated in terms of the radar waveform) and 2) the target mutual coherence function which depends upon target properties, physical environment, and viewing aspect. Random noise (independent point scatterers) and partially coherent portions of reflecting bodies are made accountable in the theory. Partial coherence effects are treated as patches of reflected energy: self-coherent energy patches plus mutually coherent energy among the patches.     

某雷达模拟器的目标与杂波信号模拟强度研究

建立了雷达模拟器中目标、地物杂波、海杂波、气象杂波的信号强度计算模型,根据相对运动关系解算出各目标与杂波信号的强度,并按时间流程存储在对应的数据表中,从而信号源将产生相应强度的信号注入雷达模拟台,以便后端雷达模拟台能在雷达威力覆盖范围内准确地显示出目标与杂波强度变化。通过仿真软件在雷达训练模拟器中的功能测试,实现了目标与杂波仿真效果,满足了雷达模拟训练的要求。     

Data fusion for ground moving target tracking

The aim of ground surveillance is the large scale, continuous and near real time determination of a dynamical ground picture. This task comprises detection and tracking of moving single targets and convoys, mobile weapon systems, and military equipment. The sensors of choice are airborne Ground Moving Target Indicator (GMTI) radar and synthetic aperture radar (SAR). As ground target tracking often suffers from dense target situations, high clutter, and low visibility, the integration and fusion of external background information is essential for providing precise and continuous tracks. We present Multi Hypotheses techniques for tracking several targets in complex ground situations with clutter. Methods to incorporate topographic information, in particular digital road maps, are described and demonstrated.     

A novel target detection approach based on adaptive radar waveform design

To resolve problems of complicated clutter, fast-varying scenes, and low signal-clutterratio (SCR) in application of target detection on sea for space-based radar (SBR), a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets.     

Time-frequency method for detecting an accelerating target in sea clutter

The authors design a time-frequency (TF) method for use in high-frequency surface-wave radar (HFSWR) for detecting a small accelerating target in sea clutter. The clutter is modelled by pseudo targets moving with Bragg velocity towards and away from the radar. The design is based on the Wigner distribution (WD) defined by Chan (type-III WD, in our terminology) rather than the WD defined by Claasen and Mecklenbrauker (1980) (2times type-I WD, in our terminology). Like the type-I WD, the type-III WD also concentrates a chirp signal onto a straight line in the TF plane. The type-III WD has the following advantages: 1) Its range of unambiguously measurable frequencies (RUMF) is [-pi,pi] rad/s, whereas for the type-I WD the RUMF is [-pi/2,pi/2] rad/s. 2) It allows a target separated from the clutter by pi rad/s to be detected, whereas the type-I WD coalesces such a target with the clutter and thereby mask it. An ambiguity function (AF) was defined corresponding to the type-III WD and use it to derive a smoothed type-III WD that mitigates the clutter. The smoothed type-III WD method is applied to real radar data and shown to be superior to the conventional Fourier transform method. The advantages of the type-III WD over the type-I WD are also demonstrated. The design principles laid out in the paper can also be used to develop a TF method for use in air traffic control radar (ATCR) for detecting an accelerating target in land clutter     

Target detection trials with a millimeter wave radar system

Unobstructed, large RCS targets, similar radar targets surrounded by moving foliage, and small targets in severe clutter have been used as test cases for two pre-processing algorithms and several threshold levels in an experimental millimeter wave radar system. The rather conventional "six-out-of-eight" pulse radar selection method with binary output has been compared to an algorithm that accepts a target if the pre-defined trigger level is crossed by the average of the eight consecutive pulses. In this case, however, the output is an analog value corresponding to the relative average video amplitude. In terms of plotted video, this process seems to give a slightly better combination of false alarm rate and detection probability. Large targets are easier to detect from foliage clutter with the conventional method.     

基于导航雷达实测数据的CFAR检测算法分析

为提高导航雷达在复杂环境中的目标检测能力,研究了修正中值(MMD)检测器在导航雷达中的应用,并与经典非参量广义符号(GS)检测器和参量最小选择(SO)检测器的检测结果进行对比。仿真结果表明:GS检测器对海上单一目标有较好的检测性能,但是在多目标环境下的检测性能严重下降;SO检测器虽然对上述环境有较好的检测性能,但是由于杂波包络分布类型难以准确已知,杂波抑制能力较差;MMD检测器在多目标环境下有较好的检测性能和杂波抑制能力。     

一种基于FPGA的海杂波产生器设计

海杂波是反舰导弹末制导雷达回波的主要背景噪声,在末制导雷达模拟器的设计中,海杂波产生器是一个重要组成部分。在研究海杂波噪声概率分布密度的基础上,给出了一种基于FPGA技术的海杂波产生器的设计方案,并利用Altera公司的cyclone II系列芯片和QuartusII开发软件对设计进行了仿真验证。采用FPGA技术可以方便地对设计方案、海杂波的分布形式、数据量大小进行修改。仿真结果表明,该设计能够产生符合要求的海杂波信号,并且具有结构简单、集成度高、易于修改等特点。     

IPIX雷达海尖峰统计特性研究

根据雷达海尖峰构成原理，以K分布为模型，提出了海尖峰的统计特性分析方法，并使用该方法对IPIX雷达实测海杂波数据进行了统计特性分析，分析结果证明了该方法的有效性。     

Radar Properties of Non-Rayleigh Sea Clutter

Measurements of sea clutter at low grazing angles using high- resolution radar show that the probability density p(x) of envelope detected sea clutter is not Rayleigh. Using the composite surface scattering model, a special varying clutter density p(x|?0) is proposed and is used to explain the non-Rayleigh nature of clutter. Since the clutter distribution has an enormous effect on the performance of a radar, the variation of the clutter densities, p(x) and p(x|?0), with various radar parameters such as frequency, pulsewidth, and polarization is found. Finally, a simulation of the composite surface scattering model is performed, and it verifies the effect of the various parameters on p(x).     

基于分层思路的动态非线性气动力建模方法

很多非线性气动力模型难以精确预测系统的小扰动线性特征。针对这一局限,提出了一种非线性分层模型,用于辨识跨声速非线性非定常气动力。分层建模需要同时提供微幅振荡和大幅振荡两套训练样本,首先通过线性模型(如带外输入的自回归(ARX)模型)对微幅振荡样本进行建模,而后采用非线性模型(如径向基函数神经网络(RBFNN))对大幅振荡的样本与线性模型的差量进行建模,进而把线性模型和非线性模型的输出相叠加,得到分层非线性动力学模型。算例表明建立的分层气动力模型与单一自回归径向基函数(AR-RBF)神经网络模型相比不仅具有更高的数值精度,可以精确预测大幅运动中的非线性特征,而且在小扰动状态下自动退化为线性模型,能够精确刻画结构微幅振荡下的线性动力学特性。     

基于粗糙集与RBF神经网络的雷达辐射源信号识别研究

针对新体制、新用途雷达辐射源信号难以识别的特点,用粗糙集理论对雷达辐射源信号进行离散化、属性约简、规则提取,达到分类的目的。用粗糙K-均值聚类方法计算径向基神经网络（RBFNN）的聚类中心,然后用粗糙集理论约简得到的规则构建径向基神经网络对未知雷达辐射源信号进行识别。仿真结果表明,这种基于粗糙集与RBF神经网络的识别模型减少了识别冗余特征、简化了神经网络结构,能有效地识别雷达辐射源信号。     

海杂波条件异方差特性分析与波动信息提取

由于低擦地角、高海况等易引起雷达海杂波序列的局部剧烈波动,传统的统计分布模型难以描述突然出现的具有冲激特性的强回波,因此,针对这一问题,将广义自回归条件异方差(Generalized Auto Regressive Conditional Heteroskedasticity,GARCH)模型引入海杂波建模中,通过 GARCH模型阶数步进搜索结合残差序列方差齐性检验,实现了海杂波数据的波动信息提取。经 X波段雷达实测数据验证,所提出的波动信息提取方法,可以很好地提取实测海杂波数据在局部区域或时间段内的波动信息,为特征检测方法设计提供有效的特征支撑。     

Multipath and ground clutter analysis for a UWB noise radar

An ultrawideband (UWB) random-noise radar operating in the 1-2 GHz frequency band has been developed and field-tested up to a 200 m range at the Environmental Remote Sensing Laboratory (ERSL) of the University of Nebraska. A unique heterodyne correlation technique based on a delayed transmitted waveform using a photonic delay line has been used to inject coherence within this system. The performance of this radar in the presence of ground reflections is investigated analytically and experimentally, and the mitigating effects of UWB waveform on multipath-induced interference are analyzed. In addition, the ground clutter statistics, in a look-down mode, are theoretically established and experimentally verified. The performance of this radar in detecting clutter embedded targets with small radar cross section (RCS) is also experimentally examined.     

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5–8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.     

Nonlinear clutter cancellation and detection using a memory-basedpredictor

In this paper, a nonlinear prediction (NLP) method is proposed as an alternative to the conventional linear prediction (LP) method for clutter cancellation. Because of the nonlinearity and non-Gaussianity of a clutter process, a nonlinear predictor is therefore needed to suppress clutter optimally. A memory-based predictor which uses a table look-up strategy to perform NLP is used in this work. The advantages of the memory-based approach are fast learning, algorithmic simplicity, robustness and suitability for parallel implementation. The memory-based predictor is then used as an adaptive detector for small surface target detection embedded in clutter. The effectiveness of the new method is demonstrated using real sea clutter data, and the results show improvement when compared with the conventional LP techniques