A dual channel perturbation particle filter algorithm based on GPU acceleration

The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit（GPU） parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

Design and analysis of multi-channel drop filter using dual L defected hexagonal photonic crystal ring resonator

In this paper,dual L defected hexagonal Photonic Crystal Ring Resonator(PCRR)using Channel Drop Filter(CDF)is designed for Coarse Wavelength Division Multiplexing(CWDM)systems.In this structure,the external rods of the ring resonator are arranged in a hexagon and the internal rods are removed in L arrangement for introducing defects.Scatter rods are used to prevent leakage.By using the L defected hexagonal resonator,a multi-channel CDF is designed,which exhibits multiple wavelengths of CWDM(1500 nm–1600 nm)region.In addition,the selection of rod size and the position of rods in the proposed multi-channel CDF are validated by varying the radius of coupling and scattering rods,as well as the position of resonators,respectively.By using plane wave expansion and opti Finite Difference Time Domain(FDTD)method,the electromagnetic wave propagation and the photonic band gap are obtained.

Light propagation properties of two-dimensional photonic crystal channel filters with elliptical micro-cavities

Light propagation through a channel filter based on two-dimensional photonic crystals with elliptical-rod defects is studied by the finite-difference time-domain method. Shape alteration of the defects from the usual circle to an ellipse offers a powerful approach to engineer the resonant frequency of channel filters. It is found that the resonant frequency can be flexibly adjusted by just changing the orientation angle of the elliptical defects. The sensitivity of the resonant wavelength to the alteration of the oval rods＇ shape is also studied. This kind of multi-channel filter is very suitable for systems requiring a large number of output channel filters.

ZigBee Signal Phase Tracking Using a Discrete Kalman Filter Estimator under AWGN Channel

The objective of this research is to track the phase changes in Binary Phase Shift Keying (BPSK) modulated signal in ZigBee communication systems using discrete Kalman Filter (KF). Therefore, Kalman Filtering is used to estimate and optimize the carrier phase of BPSK modulated signal, in the presence of Additive White Gaussian Noise (AWGN) channel, by minimizing the phase deviation error. Therefore, a simulation model, using MATLAB, will be created to demonstrate ZigBee transmission system with the impact of the integrated filter. The expected results will show that Kalman Filter tracks the phase of BPSK modulated signal correctly and the performance of tracking will be measured by Mean Square Error (MSE) with respect to Signal to Noise Ratio (SNR). This study proposes a new method of phase tracking in ZigBee receivers in the presence of AWGN channel which can be extended to Internet of Things (IoT) applications.

PARTICLE FILTERING BASED AUTOREGRESSIVE CHANNEL PREDICTION MODEL

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.

A Non-Maximally Decimated Dynamic Reconfigurable Channelized Structure Based on Modulated Filter Bank

A structure of dynamic reconfigurable channelized filter bank is proposed in order to solve the problem that the uniform channelized receiver cannot receive the cross-channel and wideband signal. The dynamic reconfigurable channelized filter bank is divided into two parts-the analysis filter bank and the synthesis filter bank. The function of the analysis filter bank is to divide the received signal into several sub-signals according to the channel division. Then the sub-signals of each channel need to be detected and discriminated. At last, we use the sub-signals to reconstruct the original received signal by the synthesis filter bank. The analysis filter and the synthesis filter bank of the dynamic reconfigurable channelized filter bank are all efficient polyphase structures, so it can save more hardware resources and has extensive applicability. The structure is simulated by MATLAB and the simulation results verify the correctness of this structure.

Channel Estimation for SCM-OFDM Systems by Modified Kalman Filter

In this paper, the problem of channel estimation for superposition coded modulation-orthogonal frequency division multiplexing (SCM-OFDM) systems over frequency selective channels is investigated. Assuming that the path delays are known, a new channel estimator based on modified Kalman filter algorithms is introduced for the estimation of the multipath Rayleigh channel complex gains (CG). In the simulation, the mean square error (MSE) and bit-error-rate (BER) performances are given to verify the effectiveness of the Kalman estimation algorithms for SCM-OFDM systems.

Nonstationary Filtering for Markov Jumping Systems with Fading Channel and Multiplicative Noises

This paper studies the nonstationary filtering problem of Markov jump system under l2 - l∞ performance. Due to the difference in propagation channels, signal strength and phase will inevitably change randomly and cause the waste of signals resources. In response to this problem, a channel fading model with multiplicative noise is introduced. And then a nonstationary filter, which receives signals more efficiently is designed. Meanwhile Lyapunov function is constructed for error analysis. Finally, the gain matrix for filtering is obtained by solving the matrix inequality, and the results showed that the nonstationary filter converges to the stable point more quickly than the traditional asynchronous filter, the stability of the designed filter is verified.

可重构智能表面辅助通信系统时变级联信道估计

针对可重构智能表面(RIS)辅助通信系统时变级联信道的估计中需解决的级联信道稀疏表示、时变信道参数跟踪和信号重构等关键问题,提出了一种结合Khatri-Rao积的分层贝叶斯卡尔曼滤波(KR-HBKF)算法。该算法首先利用信道的稀疏特性,通过Khatri-Rao积和克罗内克积变换得到RIS级联信道的稀疏表示,将RIS级联信道估计问题转化为低维度的稀疏信号恢复问题。然后,根据RIS级联信道的状态演化模型,在HBKF算法的预测模型中引入了时间相关性参数,应用改进的HBKF解决时变信道参数跟踪和信号重构问题,完成时变级联信道的估计。KR-HBKF算法综合利用了信道的稀疏性和时间相关性,能以较小的导频开销获得更好的估计精度。仿真结果表明,与传统压缩感知算法相比,所提算法具有约5 dB的估计性能提升,且在不同的时变信道条件下具有更好的鲁棒性。

UFMC系统中基于阈值的改进DFT信道估计算法

针对通用滤波多载波(Universal Filtered Multi-Carrier,UFMC)系统中传统的基于离散傅里叶变换(Discrete Fourier Transform,DFT)的信道估计算法存在噪声区间不连续、信道长度内噪声消除不彻底、信号经DFT运算后频谱泄露高等问题,提出一种基于阈值的改进DFT信道估计算法。算法在频域通过加窗以降低信道冲激响应(Channel Impulse Response,CIR)能量泄露,在时域通过设置阈值和指数平滑可最大限度滤除样本内噪声。理论分析与仿真结果表明,与传统的DFT估计算法相比,所提算法误比特率(Bit Error Ratio,BER)更低,在高信噪比下有1.5 dB左右的性能提升,能够进一步减少噪声对系统性能的影响,可有效提高信道估计的准确度。

基于空时域滤波的欺骗式干扰抑制技术

基于欺骗式干扰信号在空间上具有很强的方向性,通过利用SAR信号的角度-多普勒关系,可在多普勒域实现对其有效分解(投影)的特性,提出了一种基于多多普勒约束的空-时联合自适应滤波器,结合多通道SAR(MSAR)系统可实现对欺骗式干扰信号的有效抑制。首先分析和估计有效信号和转发式欺骗干扰(DFI)的时空频谱,然后对斜视MSAR数据进行处理,利用子空间投影矩阵去除主瓣DFI并利用多多普勒方向线性约束最小方差(MDD-LCMV)波束成形器实现对旁瓣DFI的抑制以及信号频谱重建。文中通过点仿真实验验证了所提算法的有效性。

Particle filter for joint frequency offset and channel estimation in MIMO-OFDM systems

A particle filter is proposed to perform joint estimation of the carrier frequency offset （CFO） and the channel in multiple-input multiple-output orthogonal frequency division multiplexing （MIMO-OFDM） wireless communication systems. It marginalizes out the channel parameters from the sampling space in sequential importance sampling （SIS）, and propagates them with the Kalman filter. Then the importance weights of the CFO particles are evaluated according to the imaginary part of the error between measurement and estimation. The varieties of particles are maintained by sequential importance resampling （SIR）. Simulation results demonstrate this algorithm can estimate the CFO and the channel parameters with high accuracy. At the same time, some robustness is kept when the channel model has small variations.

基于参数估计和Kalman滤波的单通道盲源分离算法

针对存在频谱混叠通信信号的单通道盲源分离(single channel blind source separation,SCBSS)问题,提出一种基于参数估计和Kalman滤波的SCBSS算法。首先,针对根多重信号分类(root multiple signal classification,Root-MUSIC)算法在相近载频估计方面的局限性,提出一种自适应的Root-MUSIC算法,对接收到的盲混合信号的源信号数目和载频进行估计;其次,将Kalman滤波的思想引入到SCBSS算法中,根据估计得到的源信号参数构造信号模型,将其作为Kalman滤波系统的观测向量,执行“时间更新”和“测量更新”两个过程,得到源信号的最佳估计,实现单通道盲源分离。仿真结果表明,所提算法能够有效地从存在频谱混叠的单路接收信号中准确地分离出多路源信号,比传统的算法分离精度高,运算速度快。

一种频率与带宽可调的可重构射频滤波器芯片

本文基于0.25μm砷化镓赝晶高电子迁移率晶体管工艺设计实现了一款频率与带宽皆可调的有源可重构滤波器芯片。该滤波器采用了双通道信道化拓扑结构,每个通道由两级可调谐振器与三级宽带放大器交叉级联构成,通过对通道内各级谐振器频率的同步和异步控制,实现对滤波器的频率与带宽的调谐,通过不同工作频段的通道间切换组合,实现了频率的宽范围调谐。为了验证该设计的有效性,完成了该滤波器的流片,其尺寸为3.1 mm×3.0 mm。经实测,该滤波器通带中心频率可在2.52 GHz~3.92 GHz之间进行调谐,频率调谐比达到了54%,通道内带宽可实现350 MHz~1080 MHz之间的变化,相对带宽可调范围为12%~33%,同时具备了频率和带宽的宽范围调控能力。

滤波器弹性的深度神经网络通道剪枝压缩方法

深度神经网络(deep neural network,DNN)在各个领域获得了巨大成功,由于其需要高额的计算和储存成本,难以直接将它们部署到资源受限的移动设备端。针对这个问题,对网络中的全局滤波器重要性评估进行了研究,提出滤波器弹性的通道剪枝压缩方法以轻量化神经网络的规模。该方法先设置层间局部动态阈值改进L1正则化(L1 lasso)稀疏训练中剪枝过度的不足;然后将其输出乘以通道缩放因子替换普通的卷积层模块,利用滤波器的弹性大小定义全局滤波器的重要性,其数值由泰勒公式估计得出并排序,同时设计新的滤波器迭代剪枝框架,以平衡剪枝性能和剪枝速度的矛盾;最后利用改进的L1正则化训练和全局滤波器重要性程度进行复合通道剪枝。在CIFAR-10上使用所提方法对VGG-16进行实验,减少了80.2%的浮点运算次数(FLOPs)和97.0%的参数量,而没有明显的准确性损失,表明了方法的有效性,能大规模地压缩神经网络,可部署于资源受限的终端设备。

改进的局部最小像素先验遥感图像盲复原算法

为了解决遥感图像盲复原时模糊核估计不准确、复原图像存在振铃效应的问题,提出改进的局部最小像素先验遥感图像盲复原算法。该算法首先引入极端通道先验与局部最小像素先验结合,对图像的强度进行更好的约束,有利于得到更好的潜在清晰图像;然后采用基于梯度的方法估计模糊核,模糊核估计与中间潜在清晰图像估计交替迭代进行,获得较为理想的模糊核;最后引入联合双边滤波器,采用改进的拉普拉斯与正则化图像复原算法抑制图像复原的振铃效应。实验结果表明,本文方法对遥感图像复原效果较好,恢复的图像边缘清晰,振铃伪影得到抑制且模糊核较为理想;客观评价指标峰值信噪比(PSNR)较前沿复原算法平均提高约1.40 dB,结构相似度(SSIM)平均提高约0.02。

凸台类型对离散型流道叠片过滤器水头损失的影响

离散型流道叠片过滤器是一种水头损失相对较小的新型叠片过滤器,其叠片表面均匀分布的凸台结构是影响叠片过滤器水头损失的主要因素之一。为探明凸台类型对内部流场的影响并进一步降低离散型流道叠片过滤器的水头损失,通过物理试验与数值仿真,对比分析了初始的右三角形和优化新增的菱形、椭圆形、上三角形、下三角形等共5种凸台类型的离散型流道叠片过滤器的水头损失及流场分布特征。结果表明:将离散型流道叠片过滤器凸台类型设置为下三角形有利于减小水流主流区与凸台边壁的接触长度和凸台间湍动能强度,降低离散型流道叠片过滤器的水头损失;与初始右三角形凸台相比,4种优化的凸台与主流区接触长度的降幅为9.61%~36.73%,最大湍动能的降幅为25.87%~49.96%,其中下三角形凸台降幅分别为29.99%和49.96%;根据模拟计算可得右三角形凸台流道间水头损失系数最大,下三角形凸台流道间水头损失系数最小,其能量转换效率最高;清水条件下,下三角形凸台离散型流道叠片过滤器水头损失相比初始离散型流道叠片过滤器降低了14.89%~21.93%。研究结果可为离散型流道叠片过滤器的结构优化提供参考。

基于天空区域分割的快速去雾算法研究

针对传统暗通道先验去雾算法在处理户外含雾图像时,出现天空区域颜色失真和处理速度慢的问题,提出一种可自适应识别天空区域的快速去雾算法。在天空分割方面,选用图像中细节特点明显的亮度分量为研究对象,结合最大类间方差法(OTSU)和动态参数建立自适应识别天空区域算法模型,得到最佳分割阈值,分割出有雾图像的天空区域和非天空区域,并根据天空区域计算出大气光值。在提高处理速度方面,在使用引导滤波优化透射率过程中引入图像下采样算法,保证复原后图像质量的同时减少算法耗时。最后经过与多种经典算法对比,文中算法在视觉效果上细节更加自然,SSIM、PSNR和MSE的综合指标均超过其他算法,并且处理速度较快。主观和客观评价结果均表明,文中算法在视觉效果和时间效率方面都优于其他几种算法,具有一定的实用价值。

短时傅里叶变换域最优非因果滤波器和滤波矩阵降噪算法

在现有短时傅里叶变换域单通道降噪算法考虑帧间相关性的基础上,为提高算法的灵活度以及滤波器的性能,提出了一系列最优非因果滤波器和滤波矩阵.具体地,通过引入非因果概念,设计出一组单通道非因果降噪滤波器,以实现同时利用过去帧和未来帧信息进行噪声抑制的目的.并在此基础上,推导出了一组最优单通道滤波矩阵,以降低算法的复杂度.将非因果滤波器以及滤波矩阵的思想相结合,进一步提出了一组单通道非因果滤波矩阵.随后,将所提算法扩展至多通道场景中,设计出了一组最优多通道非因果降噪滤波矩阵,以达到更好的降噪效果.最后,对所提算法进行了仿真实验.实验结果表明,与传统算法相比,本文提出的算法可有效改善带噪信号(输入信号)的信噪比,提升语音质量.