Adaptive array detection in noise and completely unknown jamming

The presence of jamming usually degrades the detection performance of a detector. Moreover, sufficient information about the jamming may be difficult to be obtained. To overcome the problem of adaptive array signal detection in noise and completely unknown jamming, we temporarily assume the jamming belongs to a subspace which is orthogonal to the signal steering vector in the stage of detector design. Consequently, by resorting to the criteria of generalized likelihood ratio test (GLRT) and Wald test, we propose two adaptive detectors, which can achieve signal detection and jamming suppression. It is shown, by Monte Carlo simulations, that the two proposed adaptive detectors have improved detection performance over existing ones.     

Persymmetric adaptive subspace detectors for range-spread targets

This paper investigates the problem of adaptive detection of a range-spread target in colored Gaussian disturbance. The range-spread target is described by a multi-rank subspace model, which lies in a subspace but with unknown coordinates. The disturbance, usually including clutter and thermal noise, has an unknown covariance matrix. Under the above assumption, we design the Rao and generalized likelihood ratio test (GLRT) detectors by the two-step procedure, which incorporates persymmetric structure of received data. The two detectors are shown to coincide with each other. Remarkably, the proposed detector ensures constant false alarm rate property. Experimental results conducted by both simulation and real data verify that the proposed detector outperforms the existing counterparts in training-limited scenarios.     

有色噪声中信号的GLRT检测算法研究

针对在高斯广义平稳(WSS)有色噪声环境中含有未知参数信号的检测问题,对于广义似然比检验(GLRT)和匹配子空间方法,提出了一种新的检测算法.通过AR(自回归)参数模型得到含有未知参数的信号加噪声渐进概率密度函数,利用参数变换的方法把一个非线性最小二乘估计问题转化为线性求解问题,然后采用最小二乘估计方法得到参数的极大似然估计(MLE),从而建立了具有恒虚警特性的GLRT检测器.最后结合匹配子空间方法得到了检测概率和虚警概率的表达式,计算机仿真实验验证了方法的有效性.     

Adaptive detection for distributed targets in Gaussian noise with Rao and Wald tests

The problem of adaptively detecting a range distributed target or targets in Gaussian interference is considered in this paper.It is assumed that a set of secondary data is available.Firstly,we derive the adaptive Rao and Wald tests with one-step detection algorithm,and find that both of them are of the same asymptotic performance as the GLRT one.Secondly,the Rao and Wald tests are derived by resorting to the two-step design strategy.To our surprise,our derivations show that all the Rao,Wald and GLR tests in the two-step design strategy are equivalent.Thirdly,the property assessments are presented.It is shown that these new detectors guarantee CFAR property with respect to the Gaussian noise.Finally,simulation results show that these results are accurate.     

Adaptive detectors in the Krylov subspace

The validity of the application of the Krylov subspace techniques in adaptive filtering and detection is investigated. A new verification of the equivalence of two well-known methods in the Krylov subspace, namely the multistage Wiener filters （MWF） and the auxiliary-vector filtering （AVF）, is given in this paper. The MWF and AVF are incorporated into two well-known detectors, namely, the adaptive matched filter （AMF） and Kelly＇s generalized likelihood ratio test （CLRT） including their diagonally loaded versions, which form new detectors. Compared to the conventional AMF, CLRT, and their diagonally loaded versions as well as the reduced-rank AMF and GLRT, the probabilities of detection （PDs） of the new detectors are improved especially when the sample support is low. More importantly, the new detectors are robust of the rank selection of the clutter subspace compared to the reduced-rank AMF and GLRT. These new detectors all possess asymptotic constant false alarm rate （CFAR） property.     

Efficient resource allocation scheme for multi-service based on interference mitigation in LTE-Advanced networks

The exponential growth of various services demands the increased capacity of the next-generation broadband wireless access networks, which is toward the deployment of femtocell in macrocell network based on orthogonal frequency division multiple access. However, serious time-varying interference obstructs this macro/femto overlaid network to realize its true potential. In this article, we present a macro services guaranteed resource allocation scheme, which can mitigate various dominant interferences and provide multiple services in macro/femto overlaid Third-Generation Partnership Project Long Term Evolution-Advanced networks. We model our multiple services resource allocation scheme into a multiobjective optimization problem, which is a non-deterministic polynomial-time （NP）-hard problem. Then, we give a low-complexity algorithm consisting of two layers based on chordal graph. Simulation results verify that the proposed scheme can achieve better efficiency than the previous works and raise the satisfaction ratio of Guaranteed Bit Rate （GBR） services while improving the average performance of non-GBR services.     

Tensor-based blind signal recovery for multi-carrier amplify-and-forward relay networks

Cooperative communications have great potentials in performance enhancement via deploying relay nodes. However, these kinds of benefits usually come at the cost of more system parameters to be estimated. This fact definitely reduces the efficiency of wireless systems and then it motivates the research on the blind algorithms for cooperative communications that need fewer parameters. In this paper, we investigate the parallel factors （PARAFAC） decomposition-based blind signal recovery algorithm design for two-hop amplify-and-forward （AF） multi-carrier cooperative networks. In particular, the uniqueness of the PARAFAC decomposition used in the proposed algorithm is first investigated in detail, and then the performance of signal recovery is analyzed. Finally, the simulation results assess the performance of our proposed algorithm.     

Adaptive subspace detection of range-distributed target in compound-Gaussian clutter

This paper deals with high-resolution radar (HRR) adaptive detection of range-distributed target embedded in compound-Gaussian clutter which is modeled as a spherically invariant random process (SIRP). Using multiple dominant scattering (MDS) model of range-distributed target, we justify that range-distributed target can be modeled as a subspace random signal. The unknown deterministic parameters are replaced by their ML estimates and then the nonadaptive detector is proposed. A closed-form expression for the probability of false alarm of the nonadaptive detector is derived and it ensures CFAR property with respect to the unknown statistics of the clutter texture component. Moreover, an adaptive detector is obtained relying on a two-step GLRT-based design procedure. Performances of these proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detector.     

Cooperative spectrum sensing against noise uncertainty using Neyman–Pearson lemma on fuzzy hypothesis test

In this paper, we consider the problem of cooperative spectrum sensing in the presence of the noise power uncertainty. We propose a new spectrum sensing method based on the fuzzy hypothesis test (FHT) that utilizes membership functions as hypotheses for the modeling and analyzing such uncertainty. In particular, we apply the Neyman–Pearson lemma on the FHT and propose a threshold-based local detector at each secondary user (SU) in which the threshold depends on the noise power uncertainty. In the proposed scheme, a centralized manner in the cooperative spectrum sensing is deployed in which each SU sends its one bit decision to a fusion center. The fusion center makes a final decision about the absence/presence of a primary user (PU). The performance of the PU's signal detection is evaluated by the probability of signal detection for a specific signal to noise ratio when the probability of false alarm is set to a fixed value. The performance of the proposed algorithm is compared numerically with two classical threshold-based energy detectors. Simulation results show that the proposed algorithm considerably outperforms the methods with a bi-thresholds energy detector and a simple energy detector in the presence of the noise power uncertainty.     

A novel adaptive V-BLAST algorithm based on subspace tracking and Hermitian matrix perturbation theorem

A novel vertical Bell laboratories layered space-time(V-BLAST)system with adaptive successive interference cancellation(SIC)detector based on subspace tracking(SST)and Hermitian matrix perturbation theorem is proposed in this paper,and the corresponding optimal symbol detection order operation is obtained.Moreover,asymptotic limit theorems for the detectors are established.The final simulation results verify that the symbol error probability(SEP)performance,the immunity to channel estimation errors and the algorithm convergence rate are superior to that of the conventional V-BLAST detection algorithm when channel estimation errors exist.     

Generalized adaptive subspace detector for range-Doppler spread target with high resolution radar

A generalized adaptive subspace detector for range-Doppler spread target(RDST-GASD)in the non-Gaussian clutter is derived in this paper.The subspace model of multi-pulse wideband radar target returns is established in the frequency-slow time domain.The clutters are modeled as nonhomogeneous spherically invariant random vectors(SIRVs);that is,the power of the clutter is different from one range cell to another.The clutter covariance matrix is estimated with the secondary data.The constant false alarm rate(CF...     

一种新的子空间投影抗干扰改进算法

子空间投影算法是一类重要的天线阵抗干扰算法。在强干扰环境下,子空间投影抗干扰算法能够有效抑制强干扰,但同时也会造成有用信号损耗。本文从天线阵列增益的角度出发,提出一种基于子空间投影的抗干扰新算法,以有效降低有用信号损耗。仿真实验结果证明,本文提出的新算法可使阵列输出载噪比至少提高1.2dB。     

An adaptive cell averaging-based CFAR detector for interfering targets and clutter-edge situations

In practice, there are two common situations when the independent and identically distributed (IID) assumption no longer holds: (i) there is a clutter edge and (ii) there is an outlier, e.g., a clutter spike, an impulsive interference, or another interfering target. These can result in masking of weaker targets near stronger ones and excessive false alarms at clutter edge transitions. In this paper, a new constant false alarm (CFAR) detector is proposed, which uses a goodness of fit test to verify the IID assumption. If it is decided that the data in the reference window is IID, the cell averaging (CA)-detector is applied. Otherwise, a range-heterogeneous detection algorithm is applied to provide homogeneous samples to develop a CA-based detector. The performance study shows that the proposed detector performs like the CA detector in the homogeneous situation and outperforms other competing CFAR detectors in heterogeneous situations caused by multiple targets and clutter edge.     

Model-based Rao test for adaptive range-spread target detection

This paper deals with the problem of detecting range-spread target in Gaussian disturbance with unknown covariance matrix. A model-based Rao detector is derived by modeling the disturbance as an autoregressive (AR) process with unknown parameters. Meanwhile, the asymptotic expressions for the probabilities of false alarm and detection are derived in closed form, which show that the newly proposed detector is asymptotically constant false alarm rate with respect to the disturbance covariance matrix. The performance assessment conducted by resorting to the simulated data, also in comparison to the previously proposed detectors, has confirmed the effectiveness of the newly proposed detectors.     

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi-square test-based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi-square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real-time attack detection.     

Two adaptive detectors for range-spread targets in non-Gaussian clutter

This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both ...     

Adaptive detection with conic rejection to suppress deceptive jamming for frequency diverse MIMO radar

With traditional radar systems, it is not easy to classify and suppress the intended false targets, especially in case of the mainlobe deceptive jamming. In this paper, the deceptive jamming suppression issue is formulated as a problem of detecting the presence of target signal while rejecting potential deceptive jamming under the multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) as the transmit array. The problem at hand is settled by resorting to the generalized likelihood ratio test (GLRT); at the design stage we define the sets where the false target and true target lie in. Since FDA-MIMO radar provides controllable degrees of freedom in both range and angle domains, the sets are range–angle related. This implies that both range and angle properties can be used to suppress the false targets. Based on the detector, a two-pulse scheme is proposed to guarantee high rejection probability of deceptive jamming. At the analysis stage, the performance of the detector is assessed in comparison with the conventional MIMO scenario and other detectors. The performance assessment shows that the proposed solutions are effective in the presence of electronic countermeasure (ECM) systems, especially when facing mainlobe deceptive jamming.     

基于子空间跟踪的修正MMSE多用户检测算法

在研究基于子空间跟踪的最小均方误差（MMSE）多用户检测器（MUD）的基础上，为解决原算法因引入特征值估计误差而导致检测性能下降的问题，设计了一种改进的算法——修正的MMSE多用户检测器．采用正交性能优良的OPAST算法跟踪子空间，提出一种基于OPAST的修正MMSE多用户检测算法．仿真结果显示,与基于OPAST的MMSE MUD相比，基于OPAST的修正MMSE MUD算法收敛速度快，输出信干比和误码率性能逼近SVD MUD算法，并且计算复杂度较低．     

图像边缘轮廓自适应阈值的角点检测算法

目的 基于边缘轮廓的角点检测算法的检测性能虽然相对比较稳定,但是它对边缘轮廓的局部变化敏感,并且只是给予一个经验门限去提取角点,为此提出一种对局部变化和噪声稳健的基于图像边缘轮廓自适应阈值的角点检测算法。方法 该算法利用各向异性高斯方向导数滤波器对不同边缘和角点模型进行表征,提取表征边缘和角点的灰度及几何变化的不变属性,并通过正则化计算得到区别边缘和角点的自适应阈值。该算法首先利用Canny边缘检测器检测输入图像的边缘映射并从边缘映射中提取出边缘轮廓;然后利用各向异性高斯方向导数滤波器对所提取出的边缘曲线进行滤波平滑,计算出每一像素点的响应并与自适应阈值作比较,把响应大于阈值的点作为候选角点;最后,对候选角点进行非极大值抑制得到最终角点集。结果 提出的算法分别与Harris算法,He & Yung算法,以及ANDDs算法在仿射变换和高斯噪声的实验环境下进行比较,其性能指标为平均重复率与定位误差;并且对每个角点检测算法在无噪声和有噪声的情况下进行了角点匹配比较。4种算法的两个指标的平均排名为Harris 3.375,He &Yung 2.625,ANDDs 2.625,本文算法 1.375。本文算法在仿射变换以及高斯噪声的情况下有着良好的平均重复率和定位误差,优于其他3种算法。匹配实验中的错误点以及丢失点也少于其他3种算法。结论 图像的特征检测在计算机视觉领域是一个重要的课题,在许多视觉系统中,检测特征往往作为复杂计算的第1步。因此,这一步的可靠性会极大地影响着视觉系统整体的结果。而角点作为图像的重要特征,对其研究具有重大意义。本文算法不同于传统的基于边缘的角点检测器仅利用边缘轮廓的信息,还利用到图像边缘像素的灰度信息。而且,本文算法还采用一个自适应全局阈值,避免了角点的误判。正则化的灰度变化有效减少了噪声或者光照对检测性能的影响。通过角点匹配实验、仿射变换实验以及高斯噪声实验,可以看出,本文的角点检测器拥有良好的检测性能,并且对噪声具有稳健性。     

基于正交水印的盲水印检测技术

提出一种稳健的盲水印检测技术.利用水印信号与主数据之间的正交性,水印检测时 不需要使用原始数据并且可以彻底消除主数据噪声的干扰,从而大大提高了水印检测器的稳健 性.无论是根据Neyman-Pearson准则还是最小错误概率准则,理论分析表明本文检测器在性能 上可以取得很大改善.利用对水印加权的分组技术,盲水印检测器在性能上逼近非盲水印检测 器.各种失真下的实验结果表明这种盲水印检测技术是有效的.