复合高斯杂波中距离扩展目标的参数化Wald检测

本文研究复合高斯杂波环境中的距离扩展目标的自适应检测问题。有色杂波采用参数未知的自回归（AR）过程描述。结合Wald检测准则,仅需对H1假设条件下的未知参数进行最大似然估计,给出了一种新的基于参数化模型的扩展目标检测器——参数化Wald检测器。该检测器的检验统计量可解释为首先针对各个待测单元分别计算检验统计量,然后将所有待测单元的输出进行非相参累加,其对杂波的随机功率起伏具有恒虚警率(CFAR)特性。相比于常规的基于协方差矩阵的检测方法,参数化检测算法的执行过程不需要依赖辅助数据,仅利用待测扩展目标数据即可实现自适应处理,有效缓解了训练压力并降低了计算量。仿真实验表明,所提出的参数化Wald检测器的检测性能优于之前提出的参数化广义似然比检测器的性能。      

Adaptive Radar Detection of Distributed Targets in Homogeneous and Partially Homogeneous Noise Plus Subspace Interference

This paper addresses adaptive radar detection of distributed targets in noise plus interference assumed to belong to a known or unknown subspace of the observables. At the design stage we resort to either the GLRT or the so-called two-step GLRT-based design procedure and assume that a set of noise-only data is available (the so-called secondary data). Detection algorithms have been derived modeling noise vectors, corresponding to different range cells, as independent, zero-mean, complex normal ones, sharing either the same covariance matrix (homogeneous environment) or the same covariance matrix up to possibly different (mean) power levels between primary data, i.e., range cells under test, and secondary ones (partially homogeneous environment). The performance assessment has been conducted by Monte Carlo simulation, also in comparison to previously proposed detection algorithms, and confirms the effectiveness of the newly proposed ones     

The Rao and Wald Tests Designed for Distributed Targets with Polarization MIMO Radar in Compound-Gaussian Clutter

In high resolution radars, the distributed target is usually modeled as a few isolated points referred to multiple dominant scattering centers, while the clutter is a compound-Gaussian model. Additionally, the polarimetric diversity can be exploited to enhance detection performance. Motivated by extending the detection problem of multiple-input multiple-output (MIMO) radar to such cases, this paper mainly addresses distributed targets detection problem with polarization MIMO radar against a compound-Gaussian clutter dominated scenario with unknown covariance matrix. The adaptive detectors based on Rao and Wald criteria are studied, and a two-step design procedure is adopted. Specifically, the Rao and Wald tests are derived by assuming a known covariance matrix, and then a suitable estimation of the covariance matrix based on the secondary data is inserted into the derived detectors to make them fully adaptive. Some numerical results are presented together with a polarization generalized likelihood ratio test (GLRT), showing that the derived detectors provide excellent detection performance in spiky clutter for distributed targets, and that the polarimetric diversity can be exploited to improve detection performance. Overall, the Wald test performs the best.     

A CFAR adaptive subspace detector for second-order Gaussian signals

We study the problem of detecting subspace signals described by the Second-Order Gaussian (SOG) model in the presence of noise whose covariance structure and level are both unknown. Such a detection problem is often called Gauss-Gauss problem in that both the signal and the noise are assumed to have Gaussian distributions. We propose adaptive detectors for the SOG model signals based on a single observation and multiple observations. With a single observation, the detector can be derived in a manner similar to that of the generalized likelihood ratio test (GLRT), but the unknown covariance structure is replaced by sample covariance matrix based on training data. The proposed detectors are constant false alarm rate (CFAR) detectors. As a comparison, we also derive adaptive detectors for the First-Order Gaussian (FOG) model based on multiple observations under the same noise condition as for the SOG model. With a single observation, the seemingly ad hoc CFAR detector for the SOG model is a true GLRT in that it has the same form as the GLRT CFAR detector for the FOG model. We give an approximate closed form of the probability of detection and false alarm in this case. Furthermore, we study the proposed CFAR detectors and compute the performance curves.     

均匀和部分均匀杂波中子空间目标的斜对称自适应检测方法

在雷达目标的自适应检测领域, 当参考单元数不足时, 充分挖掘协方差矩阵的结构信息是有效提高检测性能的途径之一。为此, 针对多维子空间目标的检测问题, 该文在协方差矩阵关于次对角线具有斜对称结构的约束下, 分别基于一步和两步广义似然比(GLRT), 推导了均匀和部分均匀杂波中的斜对称自适应检测器。由于检测器在设计阶段利用了协方差矩阵的结构信息, 仿真结果表明, 与已有检测器相比, 在参考单元数不足时, 斜对称自适应检测器可明显改善检测性能。此外, 分别从协方差估计方法的影响、目标子空间维数的影响、目标子空间失配性能以及目标起伏的影响4个方面对检测性能进行了仿真分析。      

部分均匀环境中训练样本不足时的贝叶斯子空间检测器

为了解决部分均匀环境中训练数据不足时的子空间信号检测难题,采用贝叶斯理论,将噪声协方差矩阵建模为逆威沙特分布,并采用广义似然比准则（generalized likelihood ratio test,GLRT）、Rao准则和Wald准则设计自适应检测器,结果表明3种准则得到相同的结果。基于仿真及实测数据验证了所提检测器的有效性,并得出了影响检测性能的关键物理量。     

集中式O FDM-MIMO雷达自适应检测算法

针对频率分集条件下，集中式OFDM-MIMO雷达在未知杂波环境中的目标检测问题，首先分析了OFDM-MIMO雷达回波数据模型，由于OFDM-MIMO雷达的频率分集特性，不同频率通道回波数据相互独立，在此基础上，分别基于一步和两步广义最大似然比准则，给出了集中式OFDM-MIMO雷达GLRT和OFDM-MIMO雷达AMF两种检测器，并分析了这两种检测器的恒虚警特性。两种检测器有效利用集中式OFDM-MIMO雷达频率分集特性，提升目标检测性能，同时降低了矩阵求逆维数，以及参考单元数目的要求，并且具有恒虚警性能。计算机仿真验证了算法的有效性。     

Knowledge-aided Bayesian radar adaptive detection in heterogeneous environment: GLRT, Rao and Wald tests

Adaptive filtering is an effective method for clutter suppression and radar detection. However, the performances degrade severely if the environment is heterogeneous. To solve this problem, we resort to a Bayesian framework and design knowledge-aided detectors under partially homogeneous model assumption, which outperform their conventional counterparts in heterogeneous environment. It is also proved that the proposed Bayesian generalized likelihood ratio test (GLRT) coincides with the Bayesian Rao and Wald tests, under the assumption that the covariance matrix of the cell under test is proportional to that of the training data.     

Adaptive Detection and Estimation in the Presence of Useful Signal and Interference Mismatches

This paper considers adaptive detection and estimation in the presence of useful signal and interference mismatches. We assume a homogeneous environment where the random disturbance components from the primary and secondary data share the same covariance matrix. Moreover, the data under test contains a deterministic interference vector in addition to the possible useful signal. We focus on the situation where an energy fraction of both the useful signal and the deterministic interference may lie outside their nominal subspaces (conical uncertainty model). Under these conditions, we devise a procedure for the computation of the joint maximum likelihood (ML) estimators of the useful signal and interference vectors, resorting to a suitable rank-one decomposition of a semidefinite program (SDP) problem optimal solution. Hence, we use the aforementioned estimators for the synthesis of adaptive receivers based on different generalized likelihood ratio test (GLRT) criteria. At the analysis stage, we assess the performance of the new detectors in comparison with some decision rules, available in open literature.      

Target Detection for Heterogeneous Cyclostationary Sea Clutter

This paper mainly deals with the problem of target detection under the heterogeneous background of cyclostationary sea clutter. Conventional approaches generally assume the ideal condition requiring the secondary data to be homogeneous with the primary data in order to exactly estimate the clutter covariance matrix and implement the adaptive filters. To the contrary, the realistic clutter environments appear heterogeneous, leading to the performance degradation of these traditional processors. For the sake of alleviating the effect of the heterogeneity, the non-homogeneous detectors, especially with knowledge-aided (KA) method based on the prior knowledge, are presented under the heterogeneous Gaussian condition. However, the experimental data manifest that the compound-Gaussian distribution is successfully applied in modeling the heterogeneous sea clutter, which also presents the cyclostationarity. Accordingly, when lacking prior information as used in the KA method, a new non-homogeneous detector based on mean value (M-NHD) is proposed against the heterogeneous sea clutter with cyclostationarity by operating solely on the primary data, in terms of the generalized likelihood ratio test (GLRT) criterion. The expressions of the probabilities of detection and false alarm are subsequently given. Since the detection performance depends on the steering vector, an adaptive non-homogeneous detector based on the steering vector (SV-NHD) is proposed subject to the design method for the optimal steering vector. Finally, the numerical results evaluate the performance of the two proposed detectors with Monte Carlo method under heterogeneity.     

零训练样本的集中式MIMO雷达斜对称检测器

传统集中式多输入多输出(MIMO)雷达自适应检测器虽然不需要训练样本即可实现目标检测,但在波形采样数较少时检测性能下降明显。该文利用集中式MIMO雷达噪声协方差矩阵的斜对称结构,基于广义似然比(Generalized Likelihood Ratio Test, GLRT)准则和Wald准则,提出了相应的斜对称检测器,并给出了统计分布特性及检测概率和虚警概率的解析表达式。仿真结果表明所提检测器在波形采样数较少时仍可获得较好的检测性能,同时证明了理论分析的正确性。     

The CFAR adaptive subspace detector is a scale-invariant GLRT

The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Previously, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data, As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly (1986)     

一种高分辨率雷达海上目标自适应检测器

针对海上目标因波浪起伏和转向等导致的姿态变化引起的散射点起伏问题,在未知协方差矩阵的复高斯噪声背景下,研究了高距离分辨率雷达的距离扩展目标自适应检测问题.利用与待检测单元具有相同协方差矩阵结构的辅助教据估计未知噪声协方差矩阵,基于两步法检测策略获得了自适应检测器.恒虚警率特性分析表明,该检测器对不同噪声背景均具有很好的自适应特性.检测性能分析表明,该检测器对不同的目标模型具有很好的鲁棒性,且能有效避免“坍塌损失”.另外,通过增加传感器个数,可有效提高检测器性能.     

非高斯杂波下基于子空间的距离扩展目标检测器

在辅助数据缺失的非高斯杂波背景下,采用两步法设计策略研究了距离扩展目标检测方法.首先,在杂波纹理分量已知的条件下,对待检测数据进行高斯化,利用高斯背景下杂波协方差矩阵和目标散射点幅度的合适估计,建立检验统计量.其次,利用待检测数据在信号子空间正交补上的正交投影,估计杂波纹理分量,提出了基于子空间的距离扩展目标自适应检测器,并证明了其对杂波纹理分量的恒虚警率（CFAR,Constant False Alarm Rate）特性.仿真结果表明,在典型非高斯背景下,所提检测器的CFAR特性和检测性能均优于对比检测器;另外,阵元数、目标距离单元数或杂波尖峰的增加,能不同程度改善检测性能.     

Detection of broadband planewaves in the presence of Gaussian noiseof unknown covariance: asymptotically optimum tests using the 2-Dautoregressive noise model

This paper addresses the problem of detecting a broadband planewave in noise of unknown spatial and temporal covariance at a linear array of sensors. Results of asymptotic detection theory are applied to derive detectors that approach optimal performance for large data records. A parametric approach is used to model the statistics of the data. A 2-D autoregressive (2DAR) model is chosen to model the noise process. Two broadband planewave signal models are considered. Both models represent the signal as a sum of monochromatic planewaves. In the Gaussian model, the amplitudes are assumed to be Gaussian with a single variance parameter, whereas in the deterministic assumption, they are individual unknown parameters. Detectors based on asymptotic theory are derived for both models. As part of the development of the asymptotically (AS) optimum detector, the Fisher information matrix (FIM) is derived. A proof of the locally asymptotic normal (LAN) property is provided for the Gaussian model probability density function (PDF). Both detectors, however, are AS equivalent to the generalized likelihood ratio test (GLRT), are AS of constant false alarm rate (CFAR), and perform AS as well as the GLRT constructed with full knowledge of the noise statistics. The performance of both detectors are compared with each other and to a standard spatially normalized beamformer in a computer simulation     

非均匀环境中分布目标的参量广义似然比检测

在高距离分辨率(HRR)雷达中,目标很可能跨越多个距离门。该文研究了这种分布目标的参量自适应检测。其中,主、辅数据中的干扰信号用随机空域协方差矩阵的向量自回归模型表示。随后,分别根据贝叶斯1步参量广义似然比(B1S-PGLRT)和贝叶斯两步参量广义似然比(B2S-PGLRT)检测准则推导了对应的检测器。前者没有闭式解而后者和经典的参量自适应匹配滤波器(PAMF)具有相似的检测结构,并使用了空域协方差矩阵的最大后验(MAP)估计代替了最大似然估计(MLE)。同时,还给出了B2S-PGLRT的归一化形式。最后,分析了贝叶斯参量检测器的运算步骤和运算复杂度,并通过蒙特卡洛仿真评价了它们的检测性能。结果表明:当训练数据不足时,贝叶斯框架下的参量匹配滤波器比广义似然比性能更好。     

Adaptive Detection of a Signal Known Only to Lie on a Line in a Known Subspace, When Primary and Secondary Data are Partially Homogeneous

This paper deals with the problem of detecting a signal, known only to lie on a line in a subspace, in the presence of unknown noise, using multiple snapshots in the primary data. To account for uncertainties about a signal's signature, we assume that the steering vector belongs to a known linear subspace. Furthermore, we consider the partially homogeneous case, for which the covariance matrix of the primary and the secondary data have the same structure but possibly different levels. This provides an extension to the framework considered by Bose and Steinhardt. The natural invariances of the detection problem are studied, which leads to the derivation of the maximal invariant. Then, a detector is proposed that proceeds in two steps. First, assuming that the noise covariance matrix is known, the generalized-likelihood ratio test (GLRT) is formulated. Then, the noise covariance matrix is replaced by its sample estimate based on the secondary data to yield the final detector. The latter is compared with a similar detector that assumes the steering vector to be known     

AR-model-based adaptive detection of range-spread targets in compound Gaussian clutter

In this paper, we consider the problem of adaptive detection for range-spread targets with known Doppler and unknown complex amplitude in compound Gaussian clutter. The speckle component of the clutter is modeled as an autoregressive (AR) process. By using the generalized likelihood ratio test (GLRT) approach, we will first estimate the AR parameters and the unknown complex amplitude, and then propose an adaptive AR-based GLR detector. The performance assessments are presented too. The computer simulations show that the proposed detector, without a priori information of the covariance matrix, has the same asymptotical performances as the two-step GLR-based detector with known covariance matrix.     

Group detection for synchronous Gaussian code-divisionmultiple-access channels

The concept of group detection Is introduced to address the design of suboptimum multiuser detectors for code-division multiple-access (CDMA) channels. A group detection scheme consists of a bank of P group detectors, one each for detecting the information symbols of users in each group of a P group partition of the K simultaneously transmitting users. In a parallel group detection scheme, these group detectors operate independently, whereas in a sequential scheme, each group detector. Uses the decisions of the previous group detectors to successively cancel the interference from those users. Group detectors based on the generalized likelihood ratio test (GLRT) are obtained for the synchronous Gaussian CDMA channel. The complexity of these detectors is exponential in the group size, whereas that of the optimum detector is exponential in K. Since the partition of users is a design parameter, group sizes can be chosen to satisfy a wide range of complexity constraints. A key performance result is that the GLRT group detectors are optimally group near-far resistant. Furthermore, upper and lower bounds on the asymptotic efficiency of the sequential group detectors are derived. These bounds reveal that the sequential group detectors can, under certain conditions, perform as well as GLRT group detectors of much larger group sizes. Group detection provides a unifying approach to multiuser detection. When the users are partitioned into K single-user groups, the GLRT, a modified form of GLRT, and the sequential group detectors reduce to previously proposed suboptimal detectors; namely, the decorrelator, the two-stage detector, and the decorrelating decision-feedback detector, respectively. For the other nontrivial partitions, the group detectors are new and have a performance that is commensurate with their complexity     

Fast converging adaptive detection of Doppler-shifted,range-distributed targets

A generalized likelihood ratio test (GLRT) for the adaptive detection of a target or targets that are Doppler-shifted and distributed in range is derived. The unknown parameters associated with the hypothesis test are the complex amplitudes in range of the desired target and the unknown covariance matrix of the additive interference, which is assumed to be characterized as complex zero-mean correlated Gaussian random variables. The target's or targets' complex amplitudes are assumed to be distributed across the entire input data block (sensor × range). The unknown covariance matrix is constrained to have the reasonable form of the identity matrix (the internal noise contribution) plus an unknown positive semidefinite (psdh) matrix (the external interference contribution). It is shown via simulation for a variety of interference scenarios that the new detector has the characteristic of having a bounded constant false alarm rate (CFAR), i.e., for our problem, the probability of false alarm P_F for a given detection threshold is bounded by the P_F that results when no external interference is present. It is also shown via simulation that the new detector converges relatively fast with respect to the number of sample vectors K necessary in order to achieve a given probability of detection P_D