子空间干扰加高斯杂波背景下基于 GLRT的斜对称方向检测器设计

文章研究了背景为子空间干扰加高斯杂波的距离扩展目标方向检测问题。杂波是均值为零协方差矩阵未知但具有斜对称特性的高斯杂波,目标与干扰分别通过具备斜对称特性的目标子空间和干扰子空间描述。针对方向检测问题,利用上述斜对称性,根据广义似然比检验(Generalized Likeli-hood Ratio Test,GLRT)准则的一步与两步设计方法,设计了基于 GLRT的一步法与两步法的距离扩展目标方向检测器。通过理论推导证明了这 2种检测器相对于未知杂波协方差矩阵都具有恒虚警率。对比相同背景下已有检测器,特别是在辅助数据有限的场景下,文章提出的 2个检测器表现出了优越的检测性能。     

临近空间高超声速多目标检测前跟踪算法

针对临近空间不同径向距离高超声速目标难以同时被有效检测的问题,提出一种通过峰值聚优改进Hough变换积累结果的检测前跟踪(TBD)算法。采用径向距离-时间坐标描述数据,减少量测误差影响,规格化坐标使2个维度处于同一量级。进行Hough变换,分割参数单元进行点数积累和能量积累,采用标签矩阵记录每个量测点曲线经过的参数单元,遍历所有量测点将每个量测点只存于其所能构成能量积累值最大的参数单元,得到新的点数积累和能量积累结果。只对点数积累空间设置门限提取目标航迹,进行航迹约束和合并,输出最终航迹。仿真结果显示,算法将3个目标全部检测出的概率可达到90%,具有较好的多目标检测性能。     

一种基于波形的距离扩展目标检测方法

针对高分辨率雷达距离扩展目标检测问题,提出了一种基于一维距离像波形的距离扩展目标检测器。分析了目标一维距离像的波形特点,对一维距离像的离散序列进行FFT变换,获得变换后的低频分量平均值与高频分量平均值的比值,以其中最大的比值作为检测统计量并进行有无目标的判决。仿真结果表明,此检测方法的检测性能要优于依赖于散射中心空间分布密度的广义似然比（SSD—GLRT,Spatial Scattering Density—Generalized Likelihood Ratio Test）检测器,并且明显优于基于波形熵的检测器。     

基于3DT的空时自适应单脉冲参数估计算法

空时自适应处理(STAP)是机载预警雷达抑制杂波和干扰的一项关键技术,而多普勒三通道联合自适应处理(3DT)是适合工程实现的降维(RD)STAP方法。STAP目标检测后还需进一步估计目标的角度参数,因此将自适应单脉冲(AM)技术引入3DT,提出了一种高精度联合估计目标速度与方位空间角的空时自适应单脉冲算法。理论分析与仿真实验结果表明,当目标多普勒频率偏离检测多普勒单元中心频率时,该算法能同时减少目标多普勒跨越损失和空时导引矢量失配损失,进而提高输出信杂噪比(SCNR),改善目标测角精度。     

距离扩展目标检测中的 强散射点自适应估计方法

高分辨雷达通过积累多个距离单元上的目标回波,达到提高距离扩展目标检测性能的目的。由于目标散射中心的稀疏分布,常规的能量积累检测方法存在陷落损失,而基于目标强散射点的积累检测方法可有效克服因陷落损失造成的检测性能下降问题。利用最大类间方差法对目标强散射点进行自适应估计,并通过仿真验证了该方法改善检测性能的有效性。     

基于三迭代与二阶锥规划的机载MIMO雷达稳健降维STAP方法

针对机载多输入多输出(MIMO)雷达空时自适应处理(STAP)中期望目标导向矢量的失配问题,提出一种基于三迭代(TRIA)与二阶锥规划(SOCP)的稳健降维MIMO-STAP方法。首先将MIMO-STAP权矢量分解为发射、接收、多普勒3个低维权矢量的Kronecker积,然后分别限定实际发射、接收、多普勒导向矢量与假定导向矢量之间的误差范数边界,通过优化最差性能,利用SOCP对各个低维权矢量进行三迭代求解,最后进行权矢量合成。该方法在保证机载MIMO雷达获取稳健STAP性能的同时,通过三迭代降维处理,能够有效降低训练样本数需求与运算复杂度,因此更具有实际应用价值。仿真结果验证了算法的有效性。     

基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法

杂波谱稀疏恢复空时自适应处理（STAP）是一种有效减少杂波样本数需求的机载雷达杂波抑制方法。然而,空时平面被离散地划分为若干个网格点来构建空时导向矢量字典,当字典在失配时,杂波脊不能准确落在预先离散化的网格点上,稀疏恢复STAP性能严重下降。提出了一种基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法,首先利用二维泰勒级数建立空时动态字典模型,然后将字典失配误差作为待估超参数构建贝叶斯稀疏恢复模型,并利用失配误差估计值对空时导向矢量字典进行修正,最后利用修正后的空时导向矢量字典重构杂波协方差矩阵,进而计算杂波空时谱。实验证明,该方法能够有效提高字典失配情况下的杂波谱稀疏恢复精度,杂波抑制性能优于已有字典预先离散化的稀疏贝叶斯学习STAP方法。     

非正侧视阵列机载雷达多空间角补偿算法

 非正侧阵列机载雷达的杂波分布随距离变化而变化,各距离单元的杂波分布不再满足独立同分布条件,造成统计型空时自适应处理(STAP)处理器性能下降。本文提出了一种多空间角补偿(MSAC)的非正侧视机载雷达杂波抑制方法,该方法先通过角度-多普勒补偿(ADC)预处理以消除在谱中心处的杂波非均匀,然后采用MSAC法在多个多普勒方向使参考单元和待检测单元的杂波谱保持一致,从而进一步消除在其余方位的杂波非均匀。仿真结果表明了该方法的性能明显优于ADC法,且运算量增加不多。     

非高斯杂波下自适应雷达目标检测新方法

在球不变随机向量(SIRV)非高斯杂波背景下,研究了多脉冲相参雷达目标的自适应检测问题。假设杂波具有相同的协方差矩阵结构和可能相关的纹理分量,提出了新的协方差矩阵估计器,并获得了相应的自适应归一化匹配滤波器(ANMF)。理论分析表明,在估计杂波分组大小与实际情况匹配时,所获得的ANMF对杂波功率水平和协方差矩阵结构均具有恒虚警率(CFAR)特性。仿真结果表明:当估计的杂波分组大小失配时,所获得的ANMF具有近似CFAR特性,并进一步分析了不同参数变化对所提检测器性能的影响。与已有的ANMF相比,所获得的ANMF具有更好的检测性能,且迭代次数更小,其相对于已知杂波协方差矩阵的最优归一化匹配滤波器(NMF)的检测损失也更小,具有很好的实际应用前景。     

基于Keystone变换和MDCFT的高机动弱目标检测与参数估计

 常规雷达对隐身、超声速和高机动目标存在回波信噪比不足、距离徙动和多普勒谱扩展问题。将Keystone变换和修正离散Chirp-Fourier变换(MDCFT)相结合,提出了一种新的雷达信号处理算法。该算法通过Keystone变换补偿距离徙动问题,利用MDCFT对多普勒谱严重扩展的目标回波进行相参积累,提高目标检测性能的同时完成了对目标参数的估计,且该算法在方位向欠采样时仍可适用。最后对算法运算量及性能进行了分析,通过仿真验证了该算法的有效性。     

Moving target detection via airborne HRR phased array radar

We study moving target detection in the presence of temporally and spatially correlated ground clutter for airborne high range resolution (HRR) phased array radar. We divide the HRR range profiles into large range segments to avoid the range migration problems that occur in the HRR radar data. Since each range segment contains a sequence of HRR range bins, no information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector autoregressive (VAR) filtering technique to suppress the ground clutter. Then a moving target detector based on a generalized likelihood ratio test (GLRT) detection strategy is derived. The detection threshold is determined according to the desired false alarm rate, which is made possible via an asymptotic statistical analysis. After the target Doppler frequency and spatial signature vectors are estimated from the VAR-filtered data as if a target were present, a simple detection variable is computed and compared with the detection threshold to render a decision on the presence of a target. Numerical results are provided to demonstrate the performance of the proposed moving target detection algorithm     

GLRT subspace detection for range and Doppler distributed targets

A generalized likelihood ratio test (GLRT) is derived for adaptive detection of range and Doppler-distributed targets. The clutter is modeled as a spherically invariant random process (SIRP) and its texture component is range dependent (heterogeneous clutter). We suppose here that the speckle component covariance matrix is known or estimated thanks to a secondary data set. Thus, unknown parameters to be estimated are local texture values, the complex amplitudes and Doppler frequencies of all scattering centers. To do so, we use superresolution methods. The proposed detector assumes a priori knowledge on the spatial distribution of the target and has the precious property of having a constant false alarm rate (CFAR) with the assumption of a known speckle covariance matrix or by the use of frequency agility.     

Adaptive resolution of mainlobe and sidelobe detections using model order determination

This work presents the development and performance evaluation of a methodology for distinguishing between mainlobe and sidelobe detections that arise in adaptive radar systems operating in adverse environments. Various adaptive detection test statistics such as the adaptive matched filter (AMF), the generalized likelihood ratio test (GLRT), and adaptive coherence estimate (ACE), and combinations of these, have been previously analyzed with respect to their sidelobe rejection capabilities. In contrast to these methods which are based on detecting a single target with known direction and Doppler, the present method uses model order determination techniques applied to the AMF or GLRT data observed over the range of unknown angle and Doppler parameters. The determination of model order, i.e., the number of signals present in the data, is made by using least-squares model fit error residuals and applying the Akaike Information Criterion (AIC). Comprehensive computer simulation results are presented which demonstrate substantial improvement in sidelobe rejection performance and detections of multiple sources compared with previous methods.     

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 CFAR adaptive matched filter detector

An adaptive algorithm for radar target detection using an antenna array is proposed. The detector is derived in a manner similar to that of the generalized likelihood-ratio test (GLRT) but contains a simplified test statistic that is a limiting case of the GLRT detector. This simplified detector is analyzed for performance to signals on boresight, as well as when the signal direction is misaligned with the look direction     

Difference beam aided target detection in monopulse radar

The classical detection step in a monopulse radar system is based on the sum beam only,the performance of which is not optimal when target is not at the beam center. Target detection aided by the difference beam can improve the performance at this case. However, the existing difference beam aided target detectors have the problem of performance deterioration at the beam center, which has limited their application in real systems. To solve this problem, two detectors are proposed in this paper. Assuming the monopulse ratio is known, a generalized likelihood ratio test(GLRT) detector is derived, which can be used when targeting information on target direction is available. A practical dual-stage detector is proposed for the case that the monopulse ratio is unknown. Simulation results show that performances of the proposed detectors are superior to that of the classical detector.     

使用电场传感器阵列的水下运动目标检测与参数估计

研究了基于电场传感器阵列的水下运动目标检测与参数估计方法。在背景噪声为高斯白噪声和目标作匀速直线运动的假设下,利用电场传感器阵列获取的船舶水下准静态电场空时特征,通过广义似然比检测和随机搜索寻优算法,可实现对海水中运动船舶的目标检测和参数估计。仿真试验结果表明:在信噪比大于0 dB时能够取得较好的估计精度;在信噪比低至10 dB时仍能有效检测目标。