Kullback-Leibler Divergence Based ISAC Constellation and Beamforming Design in the Presence of Clutter

Integrated sensing and communication(ISAC)is regarded as a pivotal technology for 6G communication.In this paper,we employ Kullback-Leibler divergence(KLD)as the unified performance metric for ISAC systems and investigate constellation and beamforming design in the presence of clutters.In particular,the constellation design problem is solved via the successive convex approximation(SCA)technique,and the optimal beamforming in terms of sensing KLD is proven to be equivalent to maximizing the signal-to-interference-plus-noise ratio(SINR)of echo signals.Numerical results demonstrate the tradeoff between sensing and communication performance under different parameter setups.Additionally,the beampattern generated by the proposed algorithm achieves significant clutter suppression and higher SINR of echo signals compared with the conventional scheme.

Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback-Leibler data divergence

Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation（TV）-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp（0〈p〈1） quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation（TpV）. This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback-Leibler（KL） data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method（ADM） which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections（7 views evenly acquired within 180°）. The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.

RADAR HRRP RECOGNITION BASED ON THE MINIMUM KULLBACK-LEIBLER DISTANCE CRITERION

To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representa-tion. And a decision rule is established for Automatic Target Recognition (ATR) based on the mini-mum Kullback-Leibler Distance (KLD) criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier (AGC) with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.

基于KLD差的统计错误模式生成算法

研究了用于指导计算机发音质量评价的错误模式的生成算法,它是普通话CALL系统研究工作中的一部分。传统的错误模式是根据语言学知识来生成的,只能得到那些最重要的常见错误模式。为了提高错误模式的覆盖面,本文提出了一种基于KLD差的统计错误模式生成算法,用模型间KLD作为模型间的距离,以标准模型间KLD与带方言口音模型间KLD的差代表两种模型间的差异,并以之为度量来生成错误模式。实验证明在引入了此算法生成的错误模式后,系统性能由0.809提升到0.826。

Multiple-model GLMB filter based on track-before-detect for tracking multiple maneuvering targets

A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of target motions are modeled by using jump Markovian system(JMS),is presented in this paper.The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model.In update,we derive a tractable GLMB density,which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence(KLD),to enable the next recursive cycle.The relevant simulation results prove that the proposed multiple-model GLMB-TBD(MM-GLMB-TBD)algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background.Additionally,the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD(DP-TBD)algorithm.Meanwhile,the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD(JMSMeMBer-TBD)filter in estimation error with the basically same computational cost.Finally,the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.

基于一致性的FastSLAM算法的优化

本文从可观测性的角度研究FastSLAM算法的一致性问题并对算法进行了改进。相比较于传统的FastSLAM算法,一种改进的FastSLAM算法被提出从而获得更好的一致性性能。首先,在重要性采样阶段,对象标记法用于清晰地标注单个粒子状态。此外,在地图估计阶段,将第一估计雅可比矩阵(FEJ)与扩展卡尔曼滤波相结合以此提高了算法的一致性。最后,通过仿真实例验证了改进的FastSLAM算法的有效性。

基于VMD-KLD的桥梁挠度监测数据温度效应分离方法

传统经验模态分解(empirical mode decomposition, EMD)方法在处理桥梁挠度信号时存在模态混叠、分解误差累积等问题,致使分解结果尚不理想。为此,提出了一种结合变分模态分解(variational mode decomposition, VMD)和K-L散度(Kullback-Leibler divergence, KLD)的桥梁挠度监测数据温度效应分离方法。利用VMD分解桥梁挠度信号,获得若干个本征模态函数(intrinsic mode function, IMF);借助核密度估计求得各IMF分量的概率密度函数分布,进而得到各分量KLD值,剔除虚假IMF分量,选定最佳分量;运用Pearson相关系数对最佳分量进行效果评价;通过数值仿真算例与实桥监测数据,验证了该方法的有效性。结果表明:该方法融合了VMD自适应、抗噪能力强和KLD快速选取最优信号的优势,克服了传统EMD模态混叠等缺陷,减少了虚假分量的干扰,将两者结合使得分解及筛选特征信号分量高效可靠,温度效应分离效果良好;仿真信号经VMD-KLD分析得到日、年温差效应及长期挠度相关系数分别为0.994 6、0.983 7和0.970 4,实测信号得到的日、年温差效应相关系数分别为0.908 1、0.936 4;同EMD-KLD相比,VMD-KLD分离出的各挠度成分相关系数更接近于1,仿真信号分析中日、年温差效应及长期挠度分别提升了4.43%、10.84%和8.81%,实测信号分析中日、年温差效应分别提升了12.35%、5.57%。该方法可为桥梁挠度监测数据温度效应在线分离提供一种新的思路。

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder （KL-SAE） is proposed in this paper, which can be applied to battle damage assessment （BDA）. This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine （UCI） data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing by the day.Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes.The trust values are estimated based on the reputation values of each node in the network by using different mechanisms.However,these mechanisms have various challenging issues which degrade the network performance.Hence,a novel Quality of Service(QoS)Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work.Initially,the QoS-based trust estimation is proposed by using a Fuzzy logic scheme.The trust value of each node is estimated by using each node’s reputation values which are deter-mined based on the fuzzy membership function values and utilizing QoS para-meters such as residual energy,bandwidth,node mobility,and reliability.This mechanism prevents only the black hole attack in the network during transmis-sion.But,the gray hole attacks are not identified which in turn increases the pack-et drop rate significantly.Hence,the gray hole attack is also detected based on the Kullback-Leibler(KL)divergence method used for estimating the statistical mea-sures.Additional QoS metrics are considered to prevent the gray hole attack,such as packet loss,packet delivery ratio,and delay for each node.Thus,the proposed mechanism prevents both black hole and gray hole attacks simultaneously.Final-ly,the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

高斯混合分布之间K-L散度的近似计算

高斯混合分布之间的K-L散度没有闭式解,通常采用其上界来近似.对于具有相同高斯数的混合分布,基于相对熵链规则推导其K-L散度上界,提出一种更紧上界的计算方法.为计算具有不同高斯数的混合分布之间的K-L散度上界,提出基于最佳高斯分量复制的方法.在中文声韵母声学模型上的实验结果显示,所提出方法可更好地近似等高斯数的混合分布之间的K-L散度,并能有效处理具有不同高斯数的混合分布.

一种基于KL分离度的改进矩阵CFAR检测方法

矩阵CFAR检测器是根据信息几何理论提出的,但其恒虚警特性没有从理论上得到分析,且检测性能也有待进一步提高。该文首先根据矩阵流形上正态律的概念从理论上推导了矩阵CFAR检测器的恒虚警特性,并在此基础上,利用积累性能更好的KLD(KULLBACK-LEIBLER Divergence)代替测地线距离,提出了一种改进的矩阵CFAR检测器。最后通过仿真实验验证了改进方法具有更好的检测性能。

基于改进粒子滤波算法的水下目标跟踪

针对常规粒子滤波算法粒子数目保持不变的问题,提出了一种可以自适应调整粒子数目的改进算法。该算法将KL距离(Kullback-Leibler Divergence,KLD)引入粒子滤波重采样过程,保证在一定的滤波精度下,可以有效地调整滤波过程中使用的粒子数目,从而实现了滤波过程中粒子数目的自适应。将该算法应用于纯方位水下目标跟踪,仿真结果表明,该方法有效地改善了滤波效果,计算量低,适合于实际应用。

基于K-L散度的恶意代码模型聚类检测方法

在云计算应用环境下,由于服务系统越来越复杂,网络安全漏洞和被攻击情况急剧增加,传统的恶意代码检测技术和防护模式已无法适应云存储环境的需求。为此,通过引入高斯混合模型,建立恶意代码的分层检测机制,使用信息增益和文档频率等方法分析和提取样本数据特征值,结合K-L散度特性,提出基于K-L散度的恶意代码模型聚类检测方法。采用KDDCUP99数据集,使用Weka开源软件完成数据预处理和聚类分析。实验结果表明,在结合信息增益和文档频率进行特征分析的前提下,与贝叶斯算法相比,该方法在虚拟环境中恶意代码的平均检测时间降低16.6%,恶意代码的平均检测率提高1.05%。

融合KL散度和移地距离的高斯混合模型相似性度量方法

为提高高斯混合模型(GMM)间相似性度量方法的计算效率和准确性,通过对称化KL散度(KLD)并结合移地距离(EMD)提出一种新的相似性度量方法。首先计算待比较的两个高斯混合模型内各高斯成分间的KL散度,对称化处理后用于构造地面距离矩阵;然后用线性规划方法求解两个高斯混合模型间的移地距离作为高斯混合模型间的相似性度量。实验结果表明,将该相似性度量方法应用于彩色图像检索,相对于传统方法能够提高检索的时间效率和准确性。

不确定性目标的CLARANS聚类算法

在传统CLARANS聚类算法基础上,提出一种针对不确定性目标的CLARANS聚类算法。在该算法中,待聚类的每个不确定性目标都被表示成高斯混合模型,即高斯分布的一个加权和,并将Kullback-Leibler散度作为不确定性目标间的距离测度。在图片数据库上的实验结果表明,该算法具有较高的聚类精度。

Threat evaluation method of warships formation air defense based on AR(p)-DITOPSIS

For the target threat evaluation of warships formation air defense, the sample data are frequently insufficient and even incomplete. The existing evaluation methods rely too much on expertise and are difficult to carry out for the dynamic evaluation on time series. In order to solve these problems, a threat evaluation method based on the AR(p)(auto regressive(AR))-dynamic improved technique for order preference by similarity to ideal solution(DITOPSIS) method is proposed. The AR(p) model is adopted to predict the missing data on the time series. Then, the entropy weight method is applied to solve each index weight at the objective point. Kullback-Leibler divergence(KLD) is used to improve the traditional TOPSIS, and to carry out the target threat evaluation. The Poisson distribution is used to assign the weight value.Simulation results show that the improved AR(p)-DITOPSIS threat evaluation method can synthetically take into account the target threat degree in time series and is more suitable for the threat evaluation under the condition of missing the target data than the traditional TOPSIS method.

A Robust Method for Ordering Performances of Multi-assets, Based Purely on Their Return Series

This study propose a new robust method to rank the performances of multi-assets （portfolios）, based purely on their return time series. This method makes no assumption on the distributions. Topsoe distance is symmetrized Kullback-Leibler divergence by average of the probabilities. The square root of Topsoe distance is a metric. We extend this metric from probability density functions to real number series on （0, 1 ]. We call it ST-metric. We show the consistency of ST-metric with mean-variance theory and stochastic dominance method of order one and two. We demonstrate the advantages of ST-metric over mean-variance rule and stochastic dominance method of order one and two.

ASSESSING LOCAL PRIOR INFLUENCE IN BAYESIAN ANALYSIS

A general method for assessing local influence of minor perturbations of prior in Bayesian analysis is developed in this paper. U8ing some elementary ideas from differelltial geometryl we provide a unified approach for handling a variety of problexns of local prior influence. AS applications, we discuss the local influence of small perturbstions of normal-gamma prior density in linear model and investigate local prior influence from the predictive view.

Information Divergence and the Generalized Normal Distribution:A Study on Symmetricity

This paper investigates and discusses the use of information divergence,through the widely used Kullback–Leibler(KL)divergence,under the multivariate(generalized)γ-order normal distribution(γ-GND).The behavior of the KL divergence,as far as its symmetricity is concerned,is studied by calculating the divergence of γ-GND over the Student’s multivariate t-distribution and vice versa.Certain special cases are also given and discussed.Furthermore,three symmetrized forms of the KL divergence,i.e.,the Jeffreys distance,the geometric-KL as well as the harmonic-KL distances,are computed between two members of the γ-GND family,while the corresponding differences between those information distances are also discussed.

Label distribution similarity-based noise correction for crowdsourcing

In crowdsourcing scenarios,we can obtain each instance's multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation.In spite of the effectiveness of label aggregation methods,there still remains a certain level of noise in the integrated labels.Thus,some noise correction methods have been proposed to reduce the impact of noise in recent years.However,to the best of our knowledge,existing methods rarely consider an instance's information from both its features and multiple noisy labels simultaneously when identifying a noise instance.In this study,we argue that the more distinguishable an instance's features but the noisier its multiple noisy labels,the more likely it is a noise instance.Based on this premise,we propose a label distribution similarity-based noisecorrection(LDSNC)method.To measure whether an instance's features are distinguishable,we obtain each instance's predicted label distribution by building multiple classifiers using instances'features and their integrated labels.To measure whether an instance's multiple noisy labels are noisy,we obtain each instance's multiple noisy label distribution using its multiple noisy labels.Then,we use the Kullback-Leibler(KL)divergence to calculate the similarity between the predicted label distribution and multiple noisy label distribution and define the instance with the lower similarity as a noise instance.The extensive experimental results on 34 simulated and four real-world crowdsourced datasets validate the effectiveness of our method.