Multi-feature integration kernel particle filtering target tracking

In light of degradation of particle filtering and robust weakness in the utilization of single feature tracking,this paper presents a kernel particle filtering tracking method based on multi-feature integration.In this paper,a new weight upgrading method is given out during kernel particle filtering at first,and then robust tracking is realized by integrating color and texture features under the framework of kernel particle filtering.Space histogram and integral histogram is adopted to calculate color and texture features respectively.These two calculation methods effectively overcome their own defectiveness,and meanwhile,improve the real timing for particle filtering.This algorithm has also improved sampling effectiveness,resolved redundant calculation for particle filtering and degradation of particles.Finally,the experiment for target tracking is realized by using the method under complicated background and shelter.Experiment results show that the method can reliably and accurately track target and deal with target sheltering situation properly.

Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking

In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.

Bandwidth adaption for kernel particle filter

A novel particle filter bandwidth adaption for kernel particle filter （BAKPF） is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation （KDE）. The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error （AMISE） firstly. Then, particle-driven bandwidth selection is invoked in the KDE. To get a more effective allocation of the particles, the KDE with adap- tive bandwidth in the BAKPF is used to approximate the posterior probability density function （PDF） by moving particles toward the posterior. A closed-form expression of the true distribution is given. The simulation results show that the proposed BAKPF performs better than the standard particle filter （PF）, unscented particle filter （UPF） and the kernel particle filter （KPF） both in efficiency and estimation precision.

Short-term traffic flow online forecasting based on kernel adaptive filter

Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.

Improved Algorithm of Variable Bandwidth Kernel Particle Filter

Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm,a self-adjusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution,the bandwidth is dynamically adjusted,and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile,the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.

RedHat As 5下L7-filter封包过滤的搭建应用

分析了Linux netfilter/iptables架构下L7-filter的功能、工作原理和实现机制,利用iptables的独立模块L7-fiter实现了具有封锁MSN、QQ等P2P封包过滤功能,并进行相关测试。测试结果表明,该layer7的封包过滤可以在具有网络地址转换(NAT)功能的防火墙体系上实现,限制对P2P等大流量数据对网络带宽的占用,使得网络运行更加高效稳定。

River-Net:面向河道提取的Refined-Lee Kernel深度神经网络模型

高精度提取合成孔径雷达(SAR)图像中的河流边界,对河流水势监测具有重要意义。以检测郑州7·20暴雨后黄河的健康状况为实施例,该文融合精致Lee滤波思想与卷积操作的滤波特性,提出了基于河道几何特性的优化内部权值卷积核Refined-Lee Kernel,进而提出了一种新型河道提取深度神经网络模型,即River-Net。为验证所提模型的有效性,该文获取了郑州7·20暴雨前后两景欧空局Sentinel-1卫星20 m分辨率干涉宽幅(IW)影像数据,利用暴雨前的影像对模型进行训练,用于提取暴雨后的黄河河道,分析黄河在暴雨后的涨势情况。实验结果表明,相比主流语义分割模型,所提模型能够更精确地在SAR图像中提取河道,对洪水灾害的检测与评估有重要应用价值。

Linux中Netfilter/iptables的研究与应用

Netfilter/iptables是Linux 2.6内核中的通用性功能框架,能够对数据包进行处理。分析了基于Linux内核的Netfilter/iptables框架以及iptables表、链的关系与作用;应用Netfilter/iptables中的包过滤特性建立了内外网间的防火墙,通过手动配置iptables规则的方式对数据包进行有目标性的过滤,防止非法数据攻击,实现了阻隔Ping洪水攻击、拦截特定网段数据包、数据包入队等待后续处理等功能,达到了保证内网安全的效果。

Nyström kernel algorithm based on least logarithmic hyperbolic cosine loss

Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.

Theoretical convergence analysis of complex Gaussian kernel LMS algorithm

With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.

双重伪补Ockham代数的理想与滤子的构造

序代数理论在理论计算机、多值逻辑学、信息系统科学等方面有着广泛且重要的应用。在序代数研究领域,理想和滤子是刻画代数结构的重要工具。双重伪补Ockham代数是在双重伪补代数和Ockham代数基础上定义的一类新的代数类,基于双重伪补Ockham代数的运算规律以及双重伪补运算与Ockham代数运算的关联性,对双重伪补Ockham代数的理想与滤子做进一步的研究。首先,运用Ockham代数的运算构造出双重伪补Ockham代数的滤子,并探讨了相关性质。其次,借助于双重伪补Ockham代数核理想和余核滤子的充要条件构造出了核理想和余核滤子的具体集合形式。所得结论为其他序代数类的理想和滤子性质的研究提供了方法,也将有助于进一步探索序代数理论在其他学科如模糊集、计算机科学中的应用。

Face Recognition Using Fuzzy Clustering and Kernel Least Square

Over the last fifteen years, face recognition has become a popular area of research in image analysis and one of the most successful applications of machine learning and understanding. To enhance the classification rate of the image recognition, several techniques are introduced, modified and combined. The suggested model extracts the features using Fourier-Gabor filter, selects the best features using signal to noise ratio, deletes or modifies anomalous images using fuzzy c-mean clustering, uses kernel least square and optimizes it by using wild dog pack optimization. To compare the suggested method with the previous methods, four datasets are used. The results indicate that the suggested methods without fuzzy clustering and with fuzzy clustering outperform state- of-art methods for all datasets.

基于核相关滤波和卡尔曼滤波预测的混合跟踪方法

针对核相关滤波(KCF)跟踪算法在遮挡场景中出现跟踪性能降低甚至跟踪失败的问题,提出了一种核相关滤波和卡尔曼滤波(KF)预测相结合的模型自适应抗遮挡图像目标跟踪算法KCF-KF。首先,考虑到传统KCF目标跟踪算法中缺少遮挡评估的问题,通过引入响应图的峰值旁瓣比来对图像目标的遮挡情况进行判断,并将遮挡类型划分为部分遮挡和严重遮挡。其次,根据遮挡程度采取不同的模型更新策略,当目标无遮挡或者部分遮挡时,替代传统KCF跟踪算法中采用固定学习率更新模型的方法,通过自适应地调整模型学习率来更新目标外观模型,避免跟踪漂移;当目标被严重遮挡时,停止KCF模型更新。最后,应用严重遮挡之前的运动信息构建卡尔曼滤波器状态空间和位置输出模型,设计卡尔曼滤波算法预测运动目标轨迹来估计遮挡情景下的目标位置,从而解决在遮挡场景中目标跟踪失败的问题。采用OTB-2013标准数据集进行大量实验,结果表明:所提的混合跟踪算法KCF-KF的距离精度为0.796,重叠成功率为0.692。与其他传统跟踪算法相比,该混合算法的跟踪精度和跟踪成功率均优于其他算法,并且在遇到目标遮挡挑战时具有更好的跟踪性能,有效地解决了跟踪过程中的遮挡干扰问题。

基于改进的相关滤波卫星视频抗遮挡跟踪方法

卫星视频中的目标存在背景复杂、尺寸较小、容易受遮挡等问题,这将影响跟踪的准确性,甚至导致跟踪失败。提出了用改进的核相关滤波算法来解决卫星视频中目标遮挡问题,并对目标进行有效跟踪。该算法通过提取目标的HOG特征、LBP特征和SIFT特征共同描述目标,并以融合特征减少背景变化的影响。提出自适应卡尔曼滤波算法解决跟踪过程中目标被遮挡的问题,通过ITCI值判断目标是否被遮挡,并对被遮挡的目标进行位置预测,选用核相关滤波算法以满足跟踪的实时性和准确性。实验结果表明,改进的核相关滤波算法解决了目标遮挡问题,对目标背景变化有较好表现,同时跟踪的精度和成功率也有很大提高。

基于改进KCF算法的织物折皱回复检测研究

织物折皱回复性能是评价织物形态稳定性的关键指标。传统折皱回复角测试方法存在检测过程依赖人工操作、难以量化折皱动态演变等问题。为实现对折皱回复全过程的自动化监测,文章提出一种基于改进核相关滤波算法的动态折皱回复检测方法。该方法使用高速摄像机捕捉织物折皱形变回复过程,应用改进的核相关滤波算法检测追踪折皱顶点的运动角度变化。通过引入多特征融合提高检测鲁棒性,利用Canny边缘检测自适应调整目标区域,减小边界效应。在此基础上提取感兴趣区域骨架,计算折皱顶角度随时间变化信息。结果表明,不同织物折皱角度变化规律与织物组织结构高度相关。最后与标准测试结果建立线性模型,验证所提方法的有效性。文章实现了对织物折皱回复全过程的自动化检测与定量评估,提供了一种更为高效准确的折皱回复性能检测新思路,具有广阔应用前景。

改进KCF的尺度自适应目标跟踪算法研究

针对KCF跟踪算法在目标跟踪过程中存在目标尺度变化时检测精度低、目标遮挡时跟踪容易丢失等问题,提出了SMAKCF(Scale-Adaptive Multiple-Feature Anti-Occlusion KCF)跟踪算法,该算法同时优化了KCF算法中的尺度响应、特征选择及模板更新策略,融合HOG特征及CN特征,加入尺度估计滤波器并利用APCE判据改进位置滤波器的更新方式,同时引入了一个检测模块对不可靠跟踪结果进行重检测。在Visual Tracker Benchmark的50个测试视频序列上进行实验来评估算法的性能,实验表明,SMAKCF算法能够有效地解决目标的尺度变化及遮挡问题,提高跟踪算法在长时目标跟踪过程中的性能。

多传感信息融合定位方法在导弹截获中的应用

针对传统的雷达截获波束指向方法仅适用于匀速行进中作战的问题,为解决变速非直线行进中作战的截获需求,提出采用核自适应滤波算法将激光雷达和惯导融合定位的方法。所提方法与传统采用惯导速度补偿方法相比,具有定位精度高、鲁棒性好等特点,可提高波束指向精度,缩短截获时间,提高武器系统作战效率,对复杂场景下雷达行进中截获导弹具有一定的研究价值。

基于数字孪生与多模型融合的多元负荷短期预测

针对多元负荷呈波动性和非线性特性导致预测模型稳定性差和精确度低等问题,提出一种基于数字孪生与多模型融合的多元负荷短期预测方法。首先,根据数字孪生体中气象和负荷信息,利用最大信息系数(MIC)分析多源数据信息间的耦合特性,基于数据时序性和周期性构建筛选信息特征。其次,采用自适应局部迭代滤波(ALIF)将历史多元负荷数据进行分解,得到不同频率下固有模态函数(IMF)分量。然后,采用核极限学习机(KELM)和双向长短期记忆网络(BiLSTM)预测高频和低频负荷分量,融合重构得到初始负荷短期预测结果。最后,利用数字孪生体补偿初始预测结果,得到最终负荷预测结果。仿真结果表明,与单预测模型及未基于数字孪生预测模型相比,所提方法具有更好的稳定性,能有效应对负荷波动变化和非线性,提升模型预测精度。

融合卡尔曼滤波的抗遮挡目标跟踪KCF算法

针对目标跟踪中的遮挡问题,在核相关滤波(Kernel Correlation Filter,KCF)基础上,融合卡尔曼滤波(Kalman Filtering,KF)和尺度不变特征变换(Scale Invariant Feature Transform,SIFT),提出一种新的抗遮挡目标跟踪方法。在KCF跟踪过程中,使用平均峰相关能量(Average Peak-to-Correlation Energy,APCE)值来检测目标是否被遮挡。若目标被遮挡,则使用KF预测目标的位置。当目标在后续帧中出现时,利用SIFT特征匹配定位目标的准确位置。最后利用KCF完成剩余帧的跟踪过程。在OTB100数据集上的实验结果表明,该方法对遮挡问题具有一定的鲁棒性。

基于最大熵准则的GNSS/SINS组合导航滤波算法

在高斯假设下,GNSS/SINS组合导航系统的常规卡尔曼滤波器(KF)在最小均方误差(MMSE)准则下是最优的。然而,当测量噪声受到重尾脉冲噪声干扰时,KF的滤波性能会严重下降。为解决该问题,提出组合导航系统的最大熵卡尔曼滤波器(MCKF)。首先,建立MCKF的状态方程及测量方程;然后,利用相对熵的原理,建立基于最大熵准则的卡尔曼滤波器,并设计其滤波迭代流程;最后,在混合高斯噪声及重尾脉冲噪声环境下,分别对GNSS/SINS组合导航系统进行仿真实验。仿真实验结果表明,在混合高斯噪声干扰下,KF的性能优于MCKF;在重尾脉冲噪声干扰下,MCKF的滤波性能明显优于KF,且核带宽趋于无穷时,MCKF等价于KF。