一种基于单部雷达的飞行器脱靶量测试方法

对单部雷达测量飞行器脱靶量所涉及到的雷达分辨力和多目标信息获取及处理进行了分析;结合实际靶场应用介绍了脱靶量数据处理方法;分析了脱靶量的测量误差,并给出了分析结果;本方法可最大限度地补偿掉系统误差,脱靶量测量误差主要来源于雷达本身测量精度。     

Multifrequency Imaging of Radar Turntable Data

In recent years synthetic-aperture radars (SAR) have proven to be very useful two-dimensional imaging tools in various fields. Based on the synthetic-aperture concepts, different imaging modes are possibe with various operating characteristics. We describe a special case where circular-projection radar data are coherently processed to yield both azimuth and range resoultion. Experiments are performed using data obtained from the radar target scatter site (RAT SCAT) radar cross-section facility. Fairly good results are obtained which illustrate the versatility of coherent syntheticaperture processing of pulse-to-pulse high-range-resolution radar returns. A discrete multifrequency stepped and pulsed waveform is the basic transmitted signal from which range-Doppler images are generated. The RAT SCAT turntable facility allows interesting model targets to be illuminated from which radar images can then be computed. One such application of the processing is described.     

Modified Frequency Scaling Algorithm for FMCW SAR Data Processing

This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during reception and between transmission and reception will introduce serious dilation in the received signal. The dilation can cause serious distortions in the reconstructed images using conventional signal processing methods. The received signal is derived and the received signal in range-Doppler domain is given. The relation between the phase resulting from antenna motion and the azimuth frequency is analyzed. The modified frequency scaling algorithm is proposed to process the received signal with serious dilation. The algorithm can effectively eliminate the impact of the dilation. The algorithm performances are shown by the simulation results.     

基于小波变换与粗糙集的雷达目标识别方法

雷达目标识别是防空武器系统雷达信息处理的一个关键环节.在小波变换与粗糙集基础上提出一种雷达目标识别方法.小波变换能够提高了时--频分频率;粗糙集理论是一种新型的处理不确定性知识的数学工具.利用小波变换对目标原始信息进行分解,得到目标的能量特征向量;通过粗糙集简化关系表,删去冗余信息,用逻辑推理算法表示判别规则.应用小波变换与粗糙集能够满足利用不精确信息进行目标识别的需要.     

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.     

雷达和红外成像双传感器信息融合目标识别研究

提出了一种利用目标的雷达和红外成像２种独立的传感器信息的互补性来构造特征向量的信息融合方法——联合向量空间法,并用对应的自适应信息融合系统进行目标识别。仿真证实比用单传感器的效果明显优越,从而说明了本文方法的有效性。     

Radar measurement accuracy for fluctuating targets

The impact of target radar cross-section (RCS) fluctuations on the thermal noise limited accuracy of radar measurements of range, range rate, and angle are evaluated for the Swerling fluctuation models. For large signal-to-noise ratios (SNRs), the accuracy criterion used is the measurement error with the same probability span as the one corresponding to the standard deviation points of the measurement error for the nonfluctuating model.<>     

基于位置信息的高分辨雷达角闪烁抑制方法

角闪烁误差是导引头寻的制导的主要误差来源,基于高分辨力雷达的单脉冲测角算法可有效改善角闪烁现象。现有的研究大多基于幅度加权的思想,利用距离单元的幅度信息进行加权平滑处理。本文在现有高分辨测角算法的基础上,结合高分辨一维距离像的位置信息,提出了一种新的角度信息处理方法。该算法充分利用了距离像有效单元的目标信息,提高了角度测量精度。仿真实验表明该算法对角闪烁有较好的抑制作用。     

Impact of target RCS fluctuations on radar measurement accuracy

The impact of target radar cross section (RCS) fluctuations on the thermal noise limited accuracy of radar measurements of range, range rate, and angle is evaluated for Swerling fluctuation models. The impact is expressed as a modification of the large-signal approximation to the standard deviation σ of measurement error     

Phase gradient autofocus-a robust tool for high resolution SARphase correction

The phase gradient autofocus (PGA) technique for phase error correction of spotlight mode synthetic aperture radar (SAR) imagery is examined carefully in the context of four fundamental signal processing steps that constitute the algorithm. We demonstrate that excellent results over a wide variety of scene content, and phase error function structure are obtained if and only if all of these steps are included in the processing. Finally, we show that the computational demands of the fun PGA algorithm do not represent a large fraction of the total image formation problem, when mid to large size images are involved     

ADS-B用于高精度雷达标定的方法

为了更好地解决高精度雷达标定的问题,提出了基于广播式自动相关监视系统(ADS-B)固定误差及目标回波中心动态修正的雷达标定新方法。首先分析了ADS-B位置数据误差的来源、类型及在雷达坐标系下的特征,同时对民航目标回波中心的变化作了分析建模,在此基础上进一步通过对雷达数据与ADS-B数据之差作动态联合修正,最终估算出雷达系统误差,提高了雷达系统误差标定的精度和稳定性。并利用多批次的实测数据对该标定新方法与其他方法进行了对比验证,结果表明,该方法有效提高了标定的精度和稳定性,并已成功应用于雷达标定设备中。     

数值法修正雷达高仰角动态滞后误差研究

脉冲雷达在高仰角跟踪过程中存在不容忽视的动态滞后误差,该类误差的存在严重影响到雷达数据处理和弹/轨道精密确定。目前外测数据处理领域采用的基于误差电压和定向灵敏度的动态滞后误差修正模型,修正效果不理想。本文提出了一种用数值法修正雷达高仰角动态滞后误差的方法,并用任务实测数据进行了模型验证,结果显示这种方法能有效地降低动态滞后引起的误差。     

Feature enhancement and ATR performance using nonquadratic optimization-based SAR imaging

We present an evaluation of the impact of a recently proposed synthetic aperture radar (SAR) imaging technique on feature enhancement and automatic target recognition (ATR) performance. This image formation technique is based on nonquadratic optimization, and the images it produces appear to exhibit enhanced features. We quantify such feature enhancement through a number of criteria. The findings of our analysis indicate that the new feature-enhanced SAR image formation method provides images with higher resolution of scatterers, and better separability of different regions as compared with conventional SAR images. We also provide an ATR-based evaluation. We run recognition experiments using conventional and feature-enhanced SAR images of military targets, with three different classifiers. The first classifier is template based. The second classifier makes a decision through a likelihood test, based on Gaussian models for reflectivities. The third classifier is based on extracted locations of the dominant target scatterers. The experimental results demonstrate that the new feature-enhanced SAR imaging method can improve the recognition performance, especially in scenarios involving reduced data quality or quantity.     

Performance complexity study of several approaches to automatictarget recognition from SAR images

A framework which allows for the direct comparison of alternate approaches to automatic target recognition (ATR) from synthetic aperture radar (SAR) images is described and applied to variants of several ATR algorithms. This framework allows comparisons to be made on an even footing while minimizing the impact of implementation details and accounts for variation in image sizes, in angular resolution, and in the sizes of orientation windows used for training. Alternate approaches to ATR are characterized in terms of the best achievable performance as a function of the complexity of the model parameter database. Several approaches to ATR from SAR images are described and the performance achievable by each for a range of database complexities is studied and compared. These approaches are based on a likelihood test under a conditionally Gaussian model, log-magnitude least squared error, and quarter power least squared error. All approaches are evaluated for a wide range of parameterizations and the dependence on these parameters of both the resulting performance and the resulting database complexity is explored. Databases for all of the approaches are trained using identical sets of images and their performance is assessed under identical testing scenarios in terms of probability of correct classification, confusion matrices, and orientation estimation error. The results indicate that the conditionally Gaussian approach outperforms the other two approaches on average for both target recognition and orientation estimation, that accounting for radar power fluctuation improves performance for all three methods, and that the conditionally Gaussian approach normalized for power delivers average performance that is equal or superior to all other considered approaches     

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     

基于遗传算法的一种高分辨率雷达目标识别方法

目标识别是防空信息处理中的一个重要环节,而对空中目标类型的识别还没有成熟的理论。通过对高分辨雷达回波信号的分析,在遗传算法的基础上,提出了一种高分辨雷达目标识别方法,由此进行目标识别。仿真实验结果表明,该方法具有高的识别率和强的抗干扰能力。     

Sidelobe blanking with integration and target fluctuation

In radar systems, sidelobe blanking (SLB) is used to mitigate impulsive interference that enters the radar through sidelobes of the main antenna. SLB employs an auxiliary antenna channel with the output being compared with that of the main antenna channel and a decision is then made as to whether or not to blank the main channel output. SLB performance determination involves the evaluation of several probability functions. Based on the classical Maisel SLB architecture, this work extends previous performance results, in which detection was limited to the case of a single radar pulse with either Marcum or Swerling I target fluctuation. Probability expressions have been generalized to include both an arbitrary number of integrated pulses and target fluctuation models based on the gamma distribution. The Swerling fluctuation models are all special cases of the gamma distribution. Results are derived in terms of two generalized probability functions, one for detection and the other for blanking. With these generalized probability functions, the SLB design and performance results can be determined. Examples are presented and discussed.     

The Information Content of Synthetic Aperture Radar Images of Terrain

A statistical model is developed that portrays an imaging radar as a noisy communication channel with multiplicative noise, and the model is used to evaluate the average amount of information that can be extracted about a target from its radar image. The average information content is also used to define a measure of radiometric resolution for radar images. It is shown that the information content and the resolution capabilities of an imaging radar reach a limit beyond which an increase in scene dynamic range does not improve the information content or the resolution. This limitation results from the multiplicative nature of the noise introduced in the imaging process.     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements