Sequential nonlinear tracking filter without requirement of measurement decorrelation

Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure- ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error （MMSE） estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.

The Effect and Compensation of Phase Noise on Orthogonal Frequency Division Multiplexing (OFDM) System

Orthogonal Frequency Division Multiplexing (OFDM) is characterized by its high data rate. However, the modulation method used in the system is subject to the influence of phase noise due to the need of time synchronization. In this paper, an algorithm based on MMSE (minimum mean square error) is developed to compensate the influence of both the common phase error (CPE) and inter carrier interference (ICI), which are two aspects of phase noise, under common Gaussian white noise. The result of noise cancellation is presented in signal-to-noise ratio (SNR) and symbol error rate (SER). Like digital signal in general, SNR can reduce SER with or without phase noise compensation. The compensation of phase noise significantly reduces the SER of the decoded signal. However, the bandwidth of phase noise still determines the signal accuracy. Under high bandwidth of phase noise, increasing SNR will only slightly increase SER, which is not efficient.

Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.

Okumura Hata Propagation Model Optimization in 400 MHz Band Based on Differential Evolution Algorithm: Application to the City of Bertoua

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon.

COST 231-Hata Propagation Model Optimization in 1800 MHz Band Based on Magnetic Optimization Algorithm: Application to the City of Limbé

Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.

应答器上行链路信号自适应解调方法的FPGA实现

为降低电磁干扰对信号传输的影响,分析了应答器上行链路信号传输过程及其易遭受干扰信号的特点,设计了基于符号最小均方误差(least mean square,LMS)算法的自适应解调方法。为在硬件平台中实现该解调方法,通过仿真计算,确定LMS算法的自适应算法中间变量变化范围,使用截位操作完成权值系数的更新,设置均衡器长度、步长因子、中值滤波系数分别为1、1/64、16,可在不占用过多硬件资源情况下获得良好的解调性能。解调算法在现场可编程门阵列(field programmable gata array,FPGA)上予以验证,实验表明,当信噪比为6 dB时,FPGA中自适应解调误码率为0.000001,在信噪比大于等于6 dB时,实测误码率与仿真分析误码率基本一致;FPGA自适应解调方法在列车不同速度等级下误码率均小于10^(-6)。

基于自适应噪声抵消的MIMO-OFDM信道估计

对多输入多输出正交频分复用系统的信道估计进行了研究,利用自适应噪声抵消的思想,提出了一种基于自适应噪声抵消和奇异值分解的信道估计方法。该方法首先利用自适应噪声抵消对最小二乘(LS)估计进行去噪处理,然后将其结果用于奇异值分解(SVD)的MMSE低阶近似方法中。仿真结果表明,利用此方法有效地抑制了噪声的影响,其系统性能优于利用最小二乘方法所获得的系统性能。

基于变分模态分解和SG滤波的二次谐波降噪

针对可调谐半导体激光器吸收光谱学,在测量低浓度气体过程中二次谐波存在的噪声问题,提出利用变分模态分解(VMD)和Savitzky-Golay滤波(SG滤波)相结合方法(简记为VMD-SG滤波)对含噪信号进行降噪。分析了VMD平衡参数的最优选取,利用所设框长调节因子P,选取SG滤波的最优框长。通过与其他几种降噪算法相比,VMD-SG滤波降噪算法在信噪比、均方根误差、波形恢复、二次谐波特征点误差方面均表现较优。在信噪比(SNR)为-7.8389 dB下该算法SNR提升量为22.047 dB,均方根误差为0.0154,与标准二次谐波相关系数为0.985。结果表明,基于VMD-SG滤波降噪算法的可调谐二极管吸收光谱复合降噪技术,在弱信号方面可以提高信号质量,有利于提高气体反演精度、系统检测灵敏度。

基于不同平差软件的GNSS控制测量数据处理方法研究

以某工程场地控制测量项目为研究对象,通过对GNSS控制网布设方法进行优化设计,采用高质量独立基线构成项目GNSS控制网,利用三维无约束平差对观测基线进行粗差检验,保证基线质量。分别采用TBC和COSA 2种GNSS数据处理软件对GNSS控制网进行平差计算,验证了在相同观测数据、相同控制网形、相同起算数据的条件下,不同平差软件精度指标和解算结果会存在一定差异,但成果较差相对较小,故在GNSS控制测量过程中,可采用多种平差软件进行独立计算,成果互为验证,进一步保障控制测量成果的可靠性。

面向粒子图像测速的光流金字塔插值优化方法

提出了一种面向粒子图像测速的光流金字塔插值优化方法,可以减小重建角度误差和均方根误差,尤其是图像边缘部分的角度误差。使用该优化算法分别对单涡流、双涡流、DNS湍流粒子场进行速度场重建,得到三种流场的均方根误差和平均角度误差比优化前至少降低了15.96%和19.87%。搭建二维PIV实验系统,在待测流场中均匀撒入100μm的PSP粒子,进行旋转实验和注水实验,分别模拟涡流场和射流场,所提出的优化算法可以获得与常见算法流形一致的速度场。

基于最小二乘法的振动监测系统测量噪声现场标定

分析振动监测系统的测量噪声水平是结构健康监测设计的重要步骤,有利于优化测试方案和后期模态参数识别。本文从数据采集全过程入手分析测量噪声的主要来源和统计特性,基于最小二乘法原理,提出标定硬件噪声水平的“n信道”方法和估计多信道同步误差的方法。前者允许以任意数量的信道组合进行噪声估计,能够充分利用所有信道的信息,增强结果的稳定性;后者运用时滞测量信号的频域特性,通过互功率谱密度的比值实现信道间亚毫秒级同步误差估计。所提方法的正确性、鲁棒性和实用性通过某桥梁振动监测系统的测量数据得到验证。

基于机载激光雷达的运营铁路非接触测量技术研究及应用

传统的运营铁路既有线测绘“RTK+水准”测量方式存在安全风险高、测量速度慢、数据多源应用性差等不足,故提出一种基于“无人机+激光雷达”的既有线非接触测量作业模式,在控制飞行速度、航摄高度、地面基站、设备脉冲频率的情况下,可获得高精度、高密度的点云数据,同时外业按照500 m间距沿既有线两侧布设靶标点对,经过点云航带配准、靶标纠正处理,可进一步提高点云航带间的相对精度和绝对精度。研究结果表明,该方法获取的既有线中线平面和高程中误差分别为1.77 cm和1.27 cm,同时,该方法避免了上线作业,安全性高、灵活性强,减少了大量外业测量工作,提高了生产效率,且点云数据运用潜力较大,在运营铁路扩能改造中具有良好的应用前景。

基于密度优化初始聚类中心的K-means算法

针对K-means算法随机选择初始聚类中心,对噪音和异常点比较敏感,聚类结果过多依赖于专家经验从而缺乏一定客观性的问题,提出一种新的度量样本密度的方法优化K-means算法对初始聚类中心的选择。该方法基于样本实际分布,以最优超球体中样本个数与超球体中样本相似性作为度量样本密度的关键,能够有效选出较优的聚类中心,使得选择的初始聚类中心更接近样本集的实际分布。算法在乳腺癌数据集、常用UCI数据集以及人工模拟数据集上进行测试,实验结果表明,与已有同类方法相比,该算法在各数据集上的聚类评价指标均有提高,而且运行速度更快,聚类结果更稳定,聚类准确率更高:在乳腺癌数据集wdbc上的准确率为91.04%,提高了6%。在Iris数据集上的准确率为94%,提高了5%。

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

An Effective Method of SNR Estimation for LDPC-CPM

The technique of SNR estimation is one of the key technologies in adaptive frequency hopping system. The methods of channel quality estimation for non-linear continuous phase modulation (CPM) signals have some limitations. Therefore, the algorithm of channel quality estimation for CPM signals is worthy of further study. Some similar phase characteristics between sampling CPM and MPSK motivate us to propose a channel estimation algorithm with applications to nonlinear CPM using linear modulation signal processing. A comprehensive analysis of LDPC-CPM schemes using proposed algorithm is presented, and simulation results indicate that the proposed method can not only estimate channel quality well but also make the normalized MSE (NMSE) of SNR estimate close to/less than 0.1 dB at SNR of 4 dB using short block codes. It shows that the algorithm in this paper is effective enough to estimate the signal to noise ratio (SNR). Meanwhile, the algorithm in this paper reduces the complexity of computation compared with other traditional algorithms.

一种改进的小波阈值去噪算法

针对阈值函数存在降噪效果不理想的问题,提出一种新的小波阈值去噪函数。利用Matlab进行模拟仿真实验,并用几种阈值算法对含噪的局部放电信号进行去噪,采用SNR和均方根误差(MSE)这两个指标来衡量降噪效果,对比实验结果发现,新的阈值函数处理后的结果优于传统方法。

GNSS差分技术在水准测量中的应用

介绍了GNSS差分技术在水准测距中的应用原理、方法和具体的实现方案。分析了GNSS差分技术在测绘行业的应用前景。

基于改进变步长最小均方算法的心电图信号去噪

针对定步长和现有变步长最小均方(LMS)自适应滤波算法对心电图(ECG)信号去噪效果较差的问题,提出一种基于分式函数改进的变步长LMS算法。首先利用分式函数构造出改进的变步长LMS算法的步长函数式,通过理论和仿真分析得到该算法参数的最佳取值,并在相同条件下与定步长和其他变步长LMS算法进行性能对比,验证了该算法具有更快的收敛速度、更低的稳态误差和更小的计算复杂度。然后将该算法与定步长和其他变步长LMS算法在相同条件下对含多种真实噪声的ECG信号进行去噪效果对比。实验结果表明,相比于其他算法,该算法能更好地去除ECG信号的噪声,且去噪后ECG信号的信噪比最大,均方误差最小,Pearson相关系数最接近于1。

GNSS/SINS组合导航系统的自适应UKF算法

针对基于无迹卡尔曼滤波(unscented Kalman filter, UKF)算法的GNSS/SINS组合导航系统缺乏对量测噪声异常的自适应调节能力,提出一种GNSS/SINS组合导航系统的自适应UKF算法。首先对GNSS/SINS组合导航系统进行非线性滤波建模;然后基于变分贝叶斯原理,在UKF算法的时间更新与量测更新过程中引入量测噪声方差估计模型,得到自适应UKF算法;最后对GNSS/SINS组合导航系统进行仿真验证。结果表明,本文算法能够对量测噪声方差的突变或缓变进行实时、准确的跟踪,相比于常规UKF算法,可明显提高组合导航系统的精度。