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.     

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.     

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     

Bayesian and Dempster-Shafer target identification for radarsurveillance

This paper considers the problem of target track identification in a radar surveillance system. To build a target identifier alongside a tracker, four features which are available for real-time processing in an air surveillance system are used here: target identity (TID) from a friend-and-foe identification (IFF) system, elevation measurement from the radar, target speed, and acceleration estimated by a tracker. These four features are combined to classify air targets into five different air target categories: friendly commercial, friendly military, hostile commercial (or unknown airline), hostile military, and false targets (clutter). Two popular statistic-based techniques, namely, the Bayesian and Dempster-Shafer methods, are applied to develop radar target identification algorithms for our application. Real-life as well as simulated air surveillance radar data are used to evaluate the practicality and effectiveness of this track identification approach in a radar surveillance system     

Optimal polarimetric detection of radar target in a slowlyfluctuating environment of clutter

It is shown that in a situation where a radar target is distant enough from the radar and is included in a natural or artificial clutter environment in such a manner that the conventional detection methods fail, it is possible to improve the radar detection performance by using appropriate signal processing on two orthogonal polarization states. A CFAR (constant false alarm rate) polarimetric detection system based on the study of the polarization difference between clutter and target is proposed. Since the polarization state of the clutter echoes fluctuates slowly from cell to cell, an autoregressive model can be applied to the components of the polarization vector to predict the detection thresholds needed to follow the polarization state variation. The detection thresholds are determined to maintain a false alarm probability equal to 10^-6. The presence of a target registers as a significant variation of the estimation error of the polarization vector. Results obtained from measurements of simple and canonical targets with artificial clutter are presented, and these results validate the principle of polarimetric detection     

Clutter rejection and transmitter-receiver requirements in anairborne radar

Ground clutter rejection requirements imposed on the exciter transmitter-receiver units of multirole airborne radar are examined. Methods are given to determine the characteristics of the units, such as noise, spurious spectral lines level, and analog-to-digital converter (ADC) dynamics, in both high- and medium-pulse-repetition-frequency modes of operation. It is shown that the spectral noise does not depend on either the wave form or the Doppler bandwidth. The reference oscillator spectral noise must be about -155 dB_c/Hz. The level of spurious lines generally depends only on the duty cycle, and the requirement is -80 dB_c for each line. ADC requires 12 to 14 b. The high-frequency mode is the most promising for detecting targets with very low radar cross sections in head-on configuration (free clutter domain)     

Coherent spatial filtering for SAR detection of stationary targets

Analysis of UHF synthetic aperture radar (SAR) images has revealed a spatially correlated phase structure over a resolved target such as a long wire or a large truck. This phase variation, approximated by a simple, range-dependent factor 4πr/λ, results in a line-shaped image spatial spectrum. Such image phase and spectral features can be exploited by coherent spatial filtering to improve target-to-clutter ratios of large targets and stationary target detectability in a strong clutter environment     

Coherent detection of radar targets in a non-gaussian background

The problem of detecting radar targets against a background of coherent, correlated, non-Gaussian clutter is studied with a two-step procedure. In the first step, the structure of the amplitude and the multivariate probability density functions (pdfs) describing the statistical properties of the clutter is derived. The starting point for this derivation is the basic scattering problem, and the statistics are obtained from an extension of the central limit theorem (CLT). This extension leads to modeling the clutter amplitude statistics by a mixture of Rayleigh distributions. The end product of the first step is a multidimensional pdf in the form of a Gaussian mixture, which is then used in step 2. The aim of step 2 is to derive both the optimal and a suboptimal detection structure for detecting radar targets in this type of clutter. Some performance results for the new detection processor are also given     

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.     

Sum-difference Reduced-dimensional Processing in SAR Image Domain and Statistical Analysis for Ground Moving Target Indication

In this article, a new reduced-dimensional adaptive processing algorithm based on joint pixels sum-difference data for clutter rejection is proposed. The sum-difference data are obtained by orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multi-satellite radar system. In the sense of statistical expectation, the sum-differ- ence data contain the common and different information of the SAR images. Therefore, the objective of clutter cancellation can be achieved by adaptive processing. Moreover, based on the residual image after clutter rejection, statistical analysis of constant false-alarm rate (CFAR) detection of moving targets is also presented. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm even with heterogeneous clutter and image co-registration error.     

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。     

Hard Limiting in Synthetic Aperture Signal Processing

In synthetic aperture radar a large linear phased array is formed from the rapid movement of a single element through each position in the array. Storage and coherent combining of the successive radar echoes are central to the array-forming process. Optical processing is the most common technique because of the efficiency with which Fourier transformation may be accomplished with simple optics. Real-time operation, however, requires all-electronic processing, which is difficult to accomplish because of the huge quantity of data to be manipulated. Dynamic range compression by hard limiting may ease the problem by reducing the number of bits per frame. The effects of hard limiting are analyzed in this paper. It is shown that large targets simultaneously illuminated by the radar antenna will produce image targets or ghosts displaced in angle. Statistically homogeneous clutter will "linearize" the hard-limited receiver and suppress the ghosts without loss in contrast, as does thermal noise if it is larger than the target echoes. Pulse compression reduces the probability of images from prominent targets. Judicious choice of the pulse-compression waveform is a powerful tool for destroying coherent buildup of images from all large targets not in the same range resolution cell. Linear FM, the most common choice, unfortunately does not exhibit this desirable property.     

一种机动多目标雷达视频信号模拟器的设计

给出了一种机动多目标雷达视频信号模拟器的软硬件设计方案,它可实时输出所需的多个动目标雷达视频信号,雷达信号的类型、目标的数量、目标的回波特性、目标的运动特性、杂波的类型及参数等均可方便地进行设置。该模拟器可满足各种雷达信号处理算法(如杂波抑制、恒虚警检测、动目标跟踪、多基地数据融合等)的测试与效果分析,以及对雷达信号处理机等进行性能调试与测试的需要。     

基于稀疏重建的MIMO-OTH雷达多模杂波抑制算法

针对由多模传播引起的多普勒扩展杂波(SDC)严重影响天波超视距雷达(OTHR)对慢速船舰目标的检测这一问题,在基于多输入多输出体制的新一代天波超视距(MIMO-OTH)雷达系统下,利用空域信息抑制多模SDC。传统处理方法需要目标角度先验信息,而在实际中,该信息往往很难获得。为此,提出一种新的基于稀疏重建的多模杂波抑制算法。该算法将二维的稀疏角度搜索转变为一维的角度搜索,使运算量大大降低,且估计出的收发角(DOA-DOD)自动配对,同时可获得各个传播路径下信号的时间多普勒信息,达到了理想的MIMO-OTH雷达多模杂波抑制效果。仿真结果证明了所提算法的有效性。     

基于模型的雷达系统仿真技术研究

介绍了通用仿真技术中的模型建立与模型校验技术。从雷达设备仿真、雷达信号模拟和雷达环境仿真三个方面讨论了雷达系统仿真中研究对象的性质与模型建立的基本方法,着重说明目标模型与平面杂波模型的主要类型、建立方法和研究动态。指出了模型校验在雷达系统仿真应用中的重要性,说明了系统仿真试验应用方法及对仿真的意义。     

Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

A new approach is described for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. In particular, azimuthal measurements are assumed to be either coarse or unavailable, so that multiple sensors are required to triangulate target tracks using range and Doppler measurements only. Increasing the number of sensors can cause data association by conventional means to become impractical due to combinatorial complexity, i.e., an exponential increase in the number of mappings between signatures and target models. When the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. In the new approach, the data association is performed probabilistically, using a variation of expectation-maximization (EM). Combinatorial complexity is avoided by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The full, multi-sensor, version of the algorithm is tested on simulated data. The results demonstrate that accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. Also, as a proof-of-concept, a simplified, single-sensor range-only version of the algorithm is tested on experimental radar data acquired with a stretch radar receiver. These results are promising, and demonstrate robustness in the presence of nonhomogeneous clutter.     

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.     

A Multistage Processing Algorithm for Disturbance Removal and Target Detection in Passive Bistatic Radar

The paper examines the problem of cancellation of direct signal, multipath and clutter echoes in passive bistatic radar (PBR). This problem is exacerbated as the transmitted waveform is not under control of the radar designer and the sidelobes of the ambiguity function can mask targets including those displaced in either (or both) range and Doppler from the disturbance. A novel multistage approach is developed for disturbance cancellation and target detection based on projections of the received signal in a subspace orthogonal to both the disturbance and previously detected targets. The resulting algorithm is shown to be effective against typical simulated scenarios with a limited number of stages, and a version with computational savings is also introduced. Finally its effectiveness is demonstrated with the application to real data acquired with an experimental VHF PBR system.     

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.