Sparse support recovery using correlation information in the presence of additive noise

The correlation based framework has recently been proposed for sparse support recovery in noiseless case. To solve this framework, the constrained least absolute shrinkage and selection operator (LASSO) was employed. The regularization parameter in the constrained LASSO was found to be a key to the recovery. This paper will discuss the sparse support recoverability via the framework and adjustment of the regularization parameter in noisy case. The main contribution is to provide noise-related conditions to guarantee the sparse support recovery. It is pointed out that the candidates of the regularization parameter taken from the noise-related region can achieve the optimization and the effect of the noise cannot be ignored. When the number of the samples is finite, the sparse support recoverability is further discussed by estimating the recovery probability for the fixed regularization parameter in the region. The asymptotic consistency is obtained in probabilistic sense when the number of the samples tends to infinity. Simulations are given to demonstrate the validity of our results.     

Sparse recovery method for far-field and near-field sources localization using oblique projection

In this paper, we propose a novel source localization method to estimate parameters of arbitrary field sources, which may lie in near-field region or far-field region of array aperture. The proposed method primarily constructs two special spatial-temporal covariance matrixes which can avoid the array aperture loss, and then estimates the frequencies of signals to obtain the oblique projection matrixes. By using the oblique projection technique, the covariance matrixes can be transformed into several data matrixes which only contain single source information, respectively. At last, based on the sparse signal recovery method, these data matrixes are utilized to solve the source localization problem. Compared with the existing typical source localization algorithms, the proposed method improves the estimation accuracy, and provides higher angle resolution for closely spaced sources scenario. Simulation results are given to demonstrate the performance of the proposed algorithm.     

基于TDoA测距的传感器网络安全定位研究

针对基于TDoA测距的定位技术,分析了基本虫洞攻击与一贯虫洞攻击的特点,提出了一种利用无线多跳网络时空特性与测距数据一致性性质的攻击检测方法,并从理论上给出了此方法的成功检测攻击概率下限值.仿真结果表明提出的检测方法在各种网络参数下均优于已有的方法.     

Malicious attack-resistant secure localization algorithm for wireless sensor network

In hostile environments, localization often suffers from malicious attacks that may distort transmit power and degrade positioning accuracy significantly for wireless sensor network. A robust semidefinite relaxation secure localiza-tion algorithm RSRSL was proposed to improve the location accuracy against malicious attacks. On the assumption of unknown transmit power, which is undoubtedly approximate to the fact of WSN, a novel secure location probability model was introduced for single-target and multi-target sensor networks, respectively. Taking the computational complexity of RSRSL into account, the nonlinear and non-convex optimization problem was simplified into a semidefinite programming problem. According to the results from both simulations and field experiments, it is clearly demonstrated that the proposed RSRSL has better performance on location accuracy, in contrast to the conventional localization algorithms.     

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

The least mean p-power (LMP) is one of the most popular adaptive filtering algorithms. With a proper p value, the LMP can outperform the traditional least mean square \((p=2)\), especially under the impulsive noise environments. In sparse channel estimation, the unknown channel may have a sparse impulsive (or frequency) response. In this paper, our goal is to develop new LMP algorithms that can adapt to the underlying sparsity and achieve better performance in impulsive noise environments. Particularly, the correntropy induced metric (CIM) as an excellent approximator of the \(l_0\)-norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the \(l_1\)-norm, reweighted \(l_1\)-norm and CIM penalized LMP algorithms, which are denoted as ZALMP, RZALMP and CIMLMP respectively. The mean and mean square convergence of these algorithms are analysed. Simulation results show that the proposed new algorithms perform well in sparse channel estimation under impulsive noise environments. In particular, the CIMLMP with suitable kernel width will outperform other algorithms significantly due to the superiority of the CIM approximator for the \(l_0\)-norm.     

无线传感器网络中DPC安全定位算法研究

在资源受限的无线传感器网络中,如何安全、有效地获取节点位置信息是一个极具挑战性的问题,通过对多种安全定位算法分析,提出了一种分散式的基于RSSI测距的DPC安全定位算法,算法应用测量一致性以及测量与计算一致性原理检测并滤除多种恶意攻击生成的虚节点,而且算法没有设计中心节点,所有的正常节点都参与安全定位算法,对算法性能的讨论以及实验结果表明,在噪声环境和虚节点较多的情况下,DPC算法仍然有很高的效率,并能应对多种恶意攻击。     

Sparse representation for face recognition by discriminative low-rank matrix recovery

This paper proposes a discriminative low-rank representation (DLRR) method for face recognition in which both the training and test samples are corrupted owing to variations in occlusion and disguise. The proposed method extends the sparse representation-based classification algorithm by incorporating the low-rank structure of data representation. The DLRR algorithm recovers a clean dictionary with enhanced discrimination ability from the corrupted training samples for sparse representation. Simultaneously, it learns a low-rank projection matrix to correct corrupted test samples by projecting them onto their corresponding underlying subspaces. The dictionary elements from different classes are encouraged to be as independent as possible by regularizing the structural incoherence of the original training samples. This leads to a compact representation of a corrected test sample by a linear combination of more dictionary elements from the corrected class. The experimental results on benchmark databases show the effectiveness and robustness of our face recognition technique.     

Practical and secure localization and key distribution for wireless sensor networks

In many applications of wireless sensor networks, sensor nodes are manually deployed in hostile environments where an attacker can disrupt the localization service and tamper with legitimate in-network communication. In this article, we introduce Secure Walking GPS, a practical and cost effective secure localization and key distribution solution for real, manual deployments of WSNs. Using the location information provided by the GPS and inertial guidance modules on a special master node, Secure Walking GPS achieves accurate node localization and location-based key distribution at the same time. We evaluate our localization solution in real deployments of MicaZ. Our experiments show that 100% of the deployed nodes localize (i.e., have a location position) and that the average localization errors are within 1–2 m, due mainly to the limitations of the existing commercial GPS devices. Our further analysis and simulation results indicate that the Secure Walking GPS scheme makes a deployed WSN resistant to the Dolev-Yao, the wormhole, and the GPS-denial attacks, the scheme is practical for large-scale deployments with resource-constrained sensor nodes and has good localization and key distribution performance.     

Multiregional secure localization using compressive sensing in wireless sensor networks

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive‐sensing–based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource‐constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.     

Method for timing recovery in presence of multipath delay andcochannel interference

Timing recovery schemes using sampling points one half-bit away from the zero crossings of the eye pattern are severely degraded when delay spread is large or when cochannel interference is present. This work presents a metric for QAM modulated signals (insensitive to carrier phase errors) for estimating an optimum sampling instant when, under such severe conditions, midsymbol sampling is not satisfactory     

Semi-fragile watermarking algorithm for image tampers localization and recovery

Two watermarks are embedded into the original image. One is the authentication watermark generated by secret key, which is embedded into the sub-LSB （Least Significant Bit） of the original image for tamper localization; the other is the recovery watermark for tamper recovering. The original image is divided into 8 x 8 blocks and each block is transformed by Discrete Cosine Transform （DCT）. For each block, some lower frequency DCT coefficients are chosen to be quantized and binary encoded so as to gain the recovery watermark of each block, and the recovery watermark is embedded into the LSB of another block by chaos encryption and authentication chain technology. After the two watermarks being detected, the location of any minute changes in image can be detected, and the tampered image data can be recovered effectively. In the paper, the number of coefficients and their bit lengths are carefully chosen in order to satisfy with the payload of each block and gain the capability of self-recovering. The proposed algorithm can well resist against possible forged attacks. Experimental results show that the watermark generated by the proposed algorithm is sensitive to tiny changes in images, and it has higher accuracy of tamper localization and good capability of the tamper recovery.     

Stable recovery of sparse overcomplete representations in the presence of noise

Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system. Considering an ideal underlying signal that has a sufficiently sparse representation, it is assumed that only a noisy version of it can be observed. Assuming further that the overcomplete system is incoherent, it is shown that the optimally sparse approximation to the noisy data differs from the optimally sparse decomposition of the ideal noiseless signal by at most a constant multiple of the noise level. As this optimal-sparsity method requires heavy (combinatorial) computational effort, approximation algorithms are considered. It is shown that similar stability is also available using the basis and the matching pursuit algorithms. Furthermore, it is shown that these methods result in sparse approximation of the noisy data that contains only terms also appearing in the unique sparsest representation of the ideal noiseless sparse signal.     

Efficient closed-form solution for target localization using TDOA measurements in the presence of sensor position errors

An efficient solution for locating a target was proposed, which by using time difference of arrival (TDOA) measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares (TSWLS) method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares (WLS). The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound (CRLB) easily and effectively improve the localization accuracy.     

用于图像篡改定位和恢复的分层半脆弱数字水印算法

设计并实现了一种基于分层的半脆弱数字水印算法,用于图像内容完整性认证,篡改定位和恢复。在嵌入方,将水印信息嵌入到原始图像所有像素的2bit最低有效比特,其中认证水印由奇偶校验码和块与子块之间灰度平均值的比较组成;恢复水印为环面自同构映射块的平均灰度值。在检测方,采用分层思想,分3层对水印进行篡改定位和恢复。实验结果表明,该算法对局部发生的篡改可以有效地检测,精确定位并进行内容恢复,同时能够有效的抵抗矢量量化攻击。     

基于BJND的立体图像篡改定位及恢复水印方法

针对立体图像内容的真实性认证和完整性保护等 问题,提出一种基于双目恰可觉察失 真(BJND)的立体图像 篡改定位及恢复水印方法。首先,利用奇异值的稳定特性,设计左右图像的定位水印;然后 ,根据BJND强度将左右图像 的定位水印嵌入到左图像中;最后,利用离散余弦变换(DCT)和JPEG量化压缩 生成恢复信息,并将右图像和左图像遮挡暴露区 域的恢复信息分别嵌入到右图像和左图像中。实验结果表明,本文方法所构造的水印对JPEG 压缩、椒盐噪声、高斯白噪声等偶然攻击比较鲁棒,而对剪切、拼接、旋转等恶意攻击较为 脆弱,同时对左右图像的恶意篡改区域检测率大于98%。由于充分利用了左右图像的匹配特 性,恢复的被篡改立体图像其左右图像PSNR达36.02dB ～41.29dB。     

扰动下一类超混沌系统的同步及其在保密通信中的应用

本文基于状态观测器方法,在扰动存在的情况下,对一类超混沌系统的同步及其在保密通信中的应用问题进行了研究。研究表明,本文所给的方法克服了扰动对系统同步所造成的影响,使得系统保持着良好的同步。另外,本文考虑的超混沌系统,对保密通信系统有着较高的保密性,因此本文的方法在实际中更加可行。     

Direction-of-arrival estimation in the presence of mutual coupling based on joint sparse recovery

A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of covariance matrix of array measurement is viewed as the signal to be represented. By exploiting the geometrical property in steering vectors and the symmetric Toeplitz structure of Mutual Coupling Matrix (MCM), the redundant dictionaries containing the DOA information are constructed. Consequently, the optimization model based on joint sparse recovery is built and then is solved through Second Order Cone Program (SOCP) and Interior Point Method (IPM). The DOA estimates are gotten according to the positions of nonzeros elements. At last, computer simulations demonstrate the excellent performance of the proposed method.     

Spatial localization and adaptive filtering of weak signals in the presence of high-intensity interferences

Methods for processing of weak signals in complex interference environment are considered. A method for adaptive filtering of a set of signals is developed. A class of adaptive bearing estimation methods with rejection of high-power interfering signals, which generalizes the Capon and thermal noise methods as well as the MUSIC adaptive method, is proposed. Results of numerical modeling are presented. Advantages and prospects of the proposed methods are demonstrated.     

一个基于Internet安全电子商务系统的安全机制设计与实现

本文介绍了我们已经实现的一个基于Internet的安全电子商务系统,它已成功地嵌入到当前流行的Netscape浏览器和服务器中,本文主要讨论了基于涉及基于设计级的安全需求,系统体系结构,系统功能和使用的关键技术,全安机制的设计和实现是我们的重点。