非高斯背景下基于ODV的距离扩展目标检测器

 本文研究了球不变随机向量杂波背景下的距离扩展目标检测问题.给出了修正的距离扩展目标检测器MSDD,推导了相应的虚警概率与检测阈值的解析表达式.MSDD需要事先估计目标散射点个数,而散射点个数估计失配时会出现严重的信杂比损失,针对这一问题,利用有序数据方差估计散射点个数,提出了ODV-MSDD检测器.仿真结果表明,ODV-MSDD对目标所占距离窗的大小具有很好的鲁棒性,并且其检测性能随着目标散射点个数、阵元数以及杂波尖峰的增加而提高.与MSDD相比,ODV-MSDD能根据实际观测数据自适应地估计散射点个数,有效提高检测器性能,增强系统的鲁棒性.     

基于距离单元筛选的扩展目标检测新方法

高斯白噪声背景下,采用滑窗能量积累检测器进行扩展目标检测存在陷落损失问题.对此,提出了基于距离单元筛选的扩展目标检测方法.采用参与能量积累的距离单元所具有的等效信噪比最大化作为距离单元筛选准则,并对筛选出来的距离单元进行能量积累检测.通过距离单元筛选减少滑窗能量积累检测中参与积累的背景噪声单元数量来改善检测性能.最后,通过仿真实验验证了距离单元筛选性能以及基于距离单元筛选的扩展目标检测方法的检测性能.实验结果表明,对于散射中心稀疏分布的扩展目标,该方法能够有效改善距离像滑窗能量积累检测的检测性能.     

基于窗口参数估计的距离扩展目标CFAR检测

雷达在探测目标时,若目标尺寸远大于雷达的距离分辨率,会在距离维上呈现出距离扩展目标特性,采用传统的点目标检测方法因无法利用全部能量而造成检测性能下降。本文针对距离扩展目标检测问题,以外辐射源雷达为背景,首先建立了外辐射源雷达距离扩展目标回波模型,提出了基于最大广义信噪比准则的距离扩展窗口参数估计方法来预估距离扩展目标的位置与宽度;为进一步判断窗口内是否为目标,研究了距离扩展窗口内的模糊恒虚警率（CFAR）检测算法,仿真比较了基于不同积累准则对各类起伏目标的模糊CFAR检测性能,并与传统的二进制积累CFAR比较,表明在均匀噪声环境和多目标环境下,基于模糊代数积积累的模糊CFAR检测性能均最优。最后结合外辐射源雷达舰船目标探测实验的实测数据处理结果,证明了基于模糊代数积积累的模糊CFAR具有最优的检测性能。     

扩展目标检测的陷落损失问题与改进方法

高斯白噪声背景下,对扩展目标采用距离像能量积累检测时,陷落损失往往是造成检测性能下降的主要原因。文中详细分析了陷落损失的产生机理、概念及其与距离像稀疏度关系,并在对距离像幅度进行有序排列及散射中心个数预估的基础上,提出了一种改进的扩展目标检测算法,有效地克服了陷落损失所造成的稀疏分布目标检测性能下降问题。仿真结果表明,对于散射中心稀疏分布的扩展目标,该方法能够有效改善距离像能量积累检测的检测性能。     

步进调频宽带雷达距离扩展目标频域检测算法

论文研究高斯杂波背景下步进调频宽带雷达的距离扩展目标检测问题。根据步进调频宽带雷达目标高分辨距离像的特点,提出一种基于回波功率谱的距离扩展目标频域检测算法。该算法无需对目标抽取形成1维距离像,直接利用目标高分辨距离像功率谱与杂波功率谱的差异,在频域实现了距离扩展目标回波能量的积累。推导了算法的检验统计量,给出了虚警概率的表达式。最后通过Monte-Carlo仿真验证了算法的检测性能。仿真结果表明该文的目标检测算法较目标抽取后的检测算法在检测性能上有明显提高,同时其检测性能也优于同条件下基于视频积累的距离扩展目标检测算法,并且具有良好的鲁棒性和工程可实现性。     

频率步进雷达距离扩展目标检测算法研究

针对宽带毫米波频率步进雷达体制下,点目标分裂成距离扩展目标所带来的检测问题,提出了先抽取后检测处理模式下的积累检测算法.该算法在有效去除过采样冗余提取出目标一维距离像的前提下,能充分利用目标上的多个视在强散射点的能量,有效提高距离扩展目标的探测能力.最后,分析了强散射点分布及IFFT时域泄漏等非理想因素对检测性能的影响.     

非均匀杂波背景下双基地MIMO雷达距离扩展目标的GLRT检测

利用球不变随机矢量(Spherically Invariant Random Vector, SIRV)描述非均匀杂波, 建立了双基地多输入多输出(Multiple-Input Multiple-Qutput, MIMO)雷达距离扩展目标的信号检测模型, 提出了距离扩展目标的两步广义似然比检测(Generalized Likelihood Ratio Test, GLRT)算法.首先, 根据目标散射系数的两种假设模型, 分别推导确定型目标、高斯型目标GLRT检测器的解析表达式, 然后利用固定点迭代算法估计杂波协方差矩阵, 获得自适应GLRT(AD-GLRT和AG-GLRT)检测器.仿真实验表明:AD-GLRT和AG-GLRT检测器的检测性能均优于非均匀杂波背景、高斯杂波背景下点目标的检测性能, 且两者的检测性能相当, 并且虚拟阵元数、目标分布的距离单元数, 以及信杂比越大, 两者的检测性能越好.     

线性相关距离扩展目标检测性能分析

随着雷达距离分辨率的不断提高,目标回波信号由占据一个距离单元的点目标会分裂为占据多个相邻距离单元的距离扩展目标,目标在不同距离单元之间的相关性是不应该被忽略的。本文研究了目标的空间相关性对距离扩展目标的自适应匹配滤波(AMF)检测器检测性能的影响。研究结果表明,如果背景的协方差矩阵结构Rc和目标的模式矩阵H可以写成对角阵的形式,那么对距离扩展目标的检测可以看作是在目标所占据的各个距离单元内分别独立完成,最后进行累加;距离扩展目标所占的单元数目L对信噪比有着重要的影响,对L的不准确估计会使输出信噪比降低;距离扩展目标占据的距离单元完全相关情况下,距离扩展目标的检测就退化为点目标检测。      

非高斯杂波下距离扩展目标检测器的失配性能分析

在球不变随机向量杂波条件下,研究了目标方向向量失配时距离扩展目标的检测问题.针对不依赖于散射点密度的广义似然比检验检测器,从理论上分析了信号实际方向向量与导向矢量失配对检测性能的影响,推导了失配条件下的检测和虚警概率公式,证明了检测器的恒虚警率特性.仿真实验表明,检测器对能量均匀分布的目标具有最佳性能,对失配目标信号具有很强的抑制能力;进一步分析表明,目标幅度起伏会带来一定的检测损失,而距离单元间散射点的相关性在高值检测概率区也会引起检测损失.     

跟踪信息辅助的扩展目标检测

雷达带宽增加带来的回波信息增量,能够更准确地辅助环境感知并获取目标信息。但是,雷达带宽的提高通常会造成目标回波在距离维的扩展,进而造成目标回波能量的分散,使单个距离单元的回波信噪比下降,不利于目标的远距离探测。此时,通过融合连续多个距离单元的回波进行检测能够提升目标探测性能。为了更充分地利用目标回波信息设计融合检测器,文中结合认知探测的思想,利用跟踪状态下的目标先验信息设计了跟踪信息辅助的扩展目标检测算法。本算法首先基于扩展目标的跟踪信息预测目标位置及其回波在各距离单元的分布,再基于预测信息设计融合检测器,以此实现从跟踪到检测的闭环,更充分地挖掘和利用了历史目标回波信息。实验表明：所设计的跟踪信息辅助的扩展目标检测算法相较于传统扩展目标检测算法,能够提升目标检测性能,推远雷达对目标的有效跟踪距离。     

Coded differential space-time modulation for flat fading channels

In this paper, powerful coding techniques for differential space-time modulation (DSTM) over Rayleigh flat fading channels and noncoherent detection without channel state information at the receiver are investigated. In particular, multilevel coding, bit-interleaved coded modulation, and so-called hybrid coded modulation (HCM) are devised and compared. For improved noncoherent reception multiple-symbol differential detection (MSDD) is adapted to DSTM. In order to reduce the computational effort required for MSDD, a low-complexity version of MSDD is applied. Evaluating the ergodic channel capacity for the different schemes as appropriate performance measure, HCM with simplified MSDD is shown to offer a favorable tradeoff between complexity and achievable power efficiency. Simulation results employing turbo codes in properly designed HCM schemes confirm the predictions from information theory.     

Tree-Search Multiple-Symbol Differential Decoding for Unitary Space-Time Modulation

Differential space-time modulation (DSTM) using unitary-matrix signal constellations is an attractive solution for transmission over multiple-input multiple-output (MIMO) fading channels without requiring channel state information (CSI) at the receiver. To avoid a high error floor for DSTM in relatively fast MIMO fading channels, multiple-symbol differential detection (MSDD) has to be applied at the receiver. MSDD jointly processes blocks of several received matrix-symbols, and power efficiency improves as the blocksize increases. But since the search space of MSDD grows exponentially with the blocksize and also with the number of transmit antennas and the data rate, the complexity of MSDD quickly becomes prohibitive. In this paper, we investigate the application of tree-search algorithms to overcome the complexity limitation of MSDD. We devise a nested MSDD structure consisting of an outer and a number of inner tree-search decoders, which renders MSDD feasible for wide ranges of system parameters. Decoder designs tailored for diagonal and orthogonal DSTM codes are given, and a more power-efficient variant of MSDD, so-called subset MSDD, is proposed. Furthermore, we derive a tight symbol-error rate approximation for MSDD, which lends itself to efficient numerical evaluation. Numerical and simulation results for different DSTM constellations and fading channel scenarios show that the new tree-search MSDD achieves a significantly better performance-complexity tradeoff than benchmark decoders.     

DPSK信号多码元差分检测的低复杂度实现

本文提出了一种亲折ＤＰＳＫ信号的多码元差分检测方案,其复杂度随观察码元长度Ｎ仅呈线性上升,而Ｄｉｖｓａｌａｒ提出的最佳多码元差分检测实现复杂度随Ｎ呈指数上升＾［１］。分析与模拟结果表明：两者性能相仿。     

Low complexity multisymbol differential detection of MDPSK overflat correlated Rayleigh fading channels

Low complexity algorithms for implementing multisymbol differential detection (MSDD) over flat correlated Rayleigh fading channels are proposed, which are extensions of the low complexity MSDD over additive white Gaussian noise channels also proposed by the authors. The complexities of the proposed algorithms increase roughly linearly with block length N while optimum MSDD increases exponentially with N. Simulation results show almost the same performances for practical N     

一种低复杂实现框架及在多码元差分检测中的应用

本文基于信息论考虑提出了一种低复杂实现的框架,对最大似然序列检测的分组（block)检测结构分析了该低复杂实现方案的性能并提出两命题,可用于寻求低复杂实现算法,另外,我们将该低复杂实现框架成功应用于高斯白声信道及衰落信道下多码元差分检测（MSDDD,multi-symbol differential detection)的低复杂实现,在基本保持其的条件下实现了检测复杂度多码元观察长度的线性增长,而最佳MSDD复杂度呈指数增工。     

A new reduced-complexity algorithm for multiple-symbol differential detection

Although multiple-symbol differential detection (MSDD) provides much better error performance than conventional differential detection, its complexity is much higher. We propose a reduced-complexity algorithm for MSDD. Our simulations show that the performance of the reduced complexity receiver is nearly identical to that of the original receiver, but with typical parameters, the number of phase sequences searched can be reduced by a factor of sixteen for QPSK to over four thousand for 16-PSK thereby leading to significant receiver complexity reduction.     

Multiple-symbol differential detection based on combinatorial geometry

In this paper, the application of combinatorial geometry to noncoherent multiple-symbol differential detection (MSDD) is considered. The resulting algorithm is referred to as CG-MSDD. Analytical expressions for the complexity of CG-MSDD are derived and it is shown that it is polynomial in the length N of the MSDD observation window if the rank of the N times N channel autocorrelation matrix is fixed, but in fact exponential in N if standard fading models are considered. Compared to popular sphere-decoder based MSDD, CG-MSDD is superior (i) in low-signal-to-noise power ratio (SNR) slow-fading channels as its complexity is independent of the SNR, (ii) as its complexity is constant, i.e., independent of the particular channel and noise realization, and (iii) asymptotically, as its complexity exponent only scales linearly with the bandwidth of the fading process.     

Quasi-maximum-likelihood multiple-symbol differential detection for time-varying Rayleigh fading channel

The maximum-likelihood multiple-symbol differential detector (ML-MSDD) has better bit-error-rate performance than many other detectors for differential modulation. Unfortunately, the computational complexity of ML-MSDD quickly becomes prohibitive as the observation window size grows. While low-complexity MSDD algorithms for the time-invariant Rayleigh fading channel have been considered before, there is a need for low-complexity MSDD algorithms for general time-varying Rayleigh fading channels. A polynomial-time complexity approach called semi-definite relaxation (SDR) is employed to achieve differential detection with near maximum-likelihood (ML) performance. The proposed SDR quasi-maximum-likelihood (QML) multiple-symbol differential detection (SDR-QML-MSDD) is efficient in that its complexity is polynomial in the observation window size, even in the worst case, while it exhibits almost the same performance as ML-MSDD does.     

Multiple symbol differential detection with diversity reception

In this letter we first consider the maximum-likelihood sequence estimator for multiple symbol differential detection (MSDD) over the slow fading diversity channel. Since this optimum decision metric results in a complex receiver implementation whose average bit-error probability (BEP) performance is difficult (if not impossible) to obtain analytically, we then focus our attention on evaluating the average BEP for MSDD with diversity reception in the form of postdetection equal-gain combining (EGC) giving emphasis to its ability to bridge the gap between EGC of conventional differentially detected M-PSK and maximal-ratio combining of coherently detected M-PSK with differential encoding