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。     

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。     

基于实测数据的海杂波时空相关性分析

海杂波的相关特性分为时间相关性和空间相关性,二者均与雷达系统参数、环境条件等多种因素有关。文章利用X波段雷达实测海杂波数据,重点研究了不同极化条件下海杂波的时间相关性、高低典型海况和不同入射余角条件下的海杂波距离向和方位向空间相关性。经大量实测数据验证表明,极化方式对海杂波强时间相关性影响较小,海杂波距离向空间相关性受海况影响较大、方位向相邻分辨单元空间相关性较弱,这些结论对于海杂波中目标检测方法优化设计具有重要意义。     

海杂波FRFT域分形特征在动目标检测中的应用

文中研究了海上动目标雷达回波与海杂波在分数阶Fourier域(FRFT域)的分形特性,利用两者幅值起伏程度的不同,提出了一种基于FRFT域分形维数差异的动目标检测方法。该方法以处处连续而不可导的非平稳不规则信号模型——分数布朗运动作为FRFT域分形模型,通过仿真分析实测海杂波数据的分形曲线,证明海杂波在FRFT域的无标度区间内具有分形特性。提取出FRFT域海杂波与动目标回波的分形维数,并利用其固有的差异进行目标检测。经不同极化方式下海杂波数据仿真,验证了算法的有效性。     

基于非线性自适应滤波器的海杂波抑制技术

海杂波会严重影响雷达对海洋表面目标的检测。为了提高海洋表面目标的检测性能,基于非线性自适应滤波器,提出了海杂波抑制方法,实现了海杂波抑制。介绍了非线性自适应滤波器的原理以及利用梯度下降法训练滤波器的方法。利用IPIX雷达实测海杂波数据,分别针对岸基雷达和机载雷达,进行了此方法的仿真试验,从均方预测误差(MSE)和信杂比改善因子(IF)两个方面,分析了此方法的性能。并与基于线性预测的LMS算法进行了比较,得出了此方法对慢速目标的检测性能优于线性方法。     

海杂波视景仿真

雷达图像的生成在训练模拟器视景仿真研究中意义重大.在对海作战仿真中,海杂波仿真是对海雷达图像仿真研究的重点.文章在比较海杂波概率分布模型的基础上,探讨了复合K分布实现方法,并应用Vega和Creator等软件实现海杂波视景仿真,生成了由海杂波形成的雷达图像,取得了较好效果,具有一定的借鉴意义.     

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。     

海杂波谱的机理及时变特性分析

针对海杂波背景下目标检测问题的实际需求,在机理分析的基础上,利用实测数据对海杂波谱的时变特性进行了分析。首先,分析了波束照射区域内由海表面波浪运动引起的多普勒频移和展宽现象,以及随雷达工作参数之间的依赖关系;然后,对已有海杂波谱机理研究结果进行了分析和总结。在此基础上,利用加窗的周期图法估计得到海杂波的时间-多普勒谱,进而从相关时间的统计特性、谱宽与时域海杂波拖尾程度的依赖关系两个层面对海杂波谱的时变特性进行了分析。分析结果可为海杂波谱建模、海杂波抑制及目标检测方法设计提供理论指导。     

低空目标双雷达联合定位方法研究

针对海上靶场雷达对低空、远距离小目标的测量数据中存在大量的地海杂波干扰,陆海结合试验环境复杂,大气参数难以精确测量,电波折射修正困难,多径影响较大,以及自身的开环跟踪模式等因素,导致雷达俯仰角测量误差较大的问题,在充分利用靶场雷达测量网布站特点的基础上,提出了一种利用2台雷达共有段落的方位角和斜距信息,构建双雷达联合定位模型,完成目标弹道解算的方法。实测数据与仿真数据综合测试表明,通过使用该方法可有效解决雷达俯仰角测量信息超差对整体测量结果的影响,同时提高了靶场雷测数据处理的精度和可靠性。     

雷达海杂波时频谱特性分析

针对海杂波背景下目标检测问题的实际需求,整理分析了STFT、WVD、PWVD和SPWVD 4种常见时频分析方法及其优缺点,并基于实测数据对海杂波的时频处理结果进行了对比分析。分别对纯海杂波单元和目标单元实测数据进行处理,将利用4种时频分析方法得到的结果进行对比、分析和总结。对4种方法在时频分辨率、目标能量积累程度、对海杂波抑制能力和平滑程度上各自特点进行分析,并总结得到相应的结论。     

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.     

Adaptive Clutter Suppression in Step Scan Radars

Clutter echoes with unknown power spectra (from weather, sea, chaff disturbances) can be suppressed only adaptively. The use of the discrete Fourier transform (DFT) for clutter suppression in step scan radars is investigated by use of a clutter model that is derived in analogy to measured clutter data of a radar with a rotating antenna.     

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.     

Generalized radar clutter model

A commonly used density model for radar clutter is chi-square for power, or, equivalently, Rayleigh for amplitude. However, for many modern high resolution radar systems, this density underestimates the tails of the measured clutter density. Log normal and Weibull distributions have proved to be better suited for the clutter in these high resolution radars. Generalizing the chi-square density by replacing it with the noncentral chi-square density and allowing the mean power level (the noncentrality parameter) to vary, we can both suitably shape the clutter density to produce larger tails and model the fluctuation of the average clutter power, commonly referred to as speckle. The resulting form, although appearing cumbersome, readily allows for efficient and accurate computations of the probability of detection in clutter     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

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。     

基于嵌入式仿真的雷达训练系统设计

介绍了嵌入式仿真技术及其发展,将嵌入式仿真与雷达模拟训练相结合,构成了能够嵌入到机械扫描雷达实装的模拟训练系统。描述了对嵌入式仿真训练系统的结构组成以及部分模块的功能,研究了模拟雷达视频回波数据的方法和模型,包括目标回波、地物杂波、海杂波、气象杂波、有源干扰和接收机内部噪声,利用数学模型计算回波的强度、方位、距离等特性,调制实际录取的回波数据,生成与态势设置参数相应的模拟回波数据,能够对信号处理操作和抗干扰操作做出响应。通过对视频回波进行仿真,由终端显示画面可知模拟的回波数据具有较高的逼真度,经测试模拟视频回波能够被雷达显控终端识别。     

High resolution radar clutter statistics

The generalized compound probability density function (GC-pdf) is presented for modeling high resolution radar clutter. In particular, the model is used to describe deviation of the speckle component from the Rayleigh to Weibull or other pdfs with longer tails. The GC-pdf is formed using the generalized gamma (GΓ) pdf to describe both the speckle and the modulation component of the radar clutter. The proposed model is analyzed and thermal noise is incorporated into it. The validation of the GC-pdf with real data is carried out employing the statistical moments as well as goodness-of-fit tests. A large variety of experimental data is used for this purpose. The GC-pdf outperforms the K-pdf in modeling high resolution radar clutter and reveals its structural characteristics