海面电波传播损耗模型研究与仿真

研究了海面电波传播损耗预测模型,指出对该类传播损耗预测的仿真模型宜采用Longley-Rice模型,并对模型作了适应性修正和补充以适应海上编队通信链路的仿真.还推导了Okumura-Hata模型和Longley-Rice模型电波传输损耗预测的具体算法,数值仿真结果显示本文提出的方法预测结果与实测数据相符,最后给出对1 km以内和飞机飞行高度在10 km以上时的修正和补充方法.     

DTMB单频网规划中电波传播模型的选取与校正

研究了某地区DTMB单频网规划中电波传播模型的选取和校正方法,基于连续波测试理论采集和处理实测数据,基于ICS telecom仿真对不同电波传播模型下的接收功率进行预测,以预测值和实测值之间的均方误差最小为准则选取了一种适合该规划地区的电波传播模型,并采用最小二乘法对该模型进行校正,得到两种不同传播环境下(市区、郊区)的电波传播模型。与原模型相比,校正后模型对链路损耗预测的均方误差有较大程度改善,这对提高该规划地区单频网规划的科学性、准确性和可靠性有重要的作用。     

Okumura-Hata模型在城市环境LoRa网络中的应用

对城市建设的LoRa网络的道路覆盖性能进行拉远测试,并将实测结果与Okumura-Hata模型进行比较,发现该模型不能较为准确地反应城市环境下LoRa网络的路径损耗。考虑到当今城市发展的速度和规模已远超传统无线电传播模型建立时的预期,所以在Okumura-Hata模型的基础上,基于实测数据对该模型利用最小二乘法进行校正,然后使用RMSE(标准误差)法计算校正后的模型预测值与实测数据的标准误差,结果在允许的误差范围之内。最后在实验室搭建一个LoRa网关,对该网关的拉远测试结果与修正后的路径损耗模型的预测值相符。     

车载超短波电台电波传播预测模型研究

针对实战环境中车载超短波电台通信距离和质量受地面附着物和地形地貌影响的问题，文中基于射线追踪和机器学习，建立了车载超短波电台电波传播预测模型。采用装甲车辆与车载天线的一体化建模获得车载天线辐射方向图，融合电子地图，建立了基于射线追踪技术的电波传播仿真模型。利用随机森林机器学习算法和仿真模型的数据结果，建立了基于随机森林的电波传播预测模型，并与经典电波传播模型如Egli模型和Okumura-Hata模型进行对比。结果显示，基于随机森林的电波传播模型预测精度更高，均方根误差达到2.190 1 dB,决定系数达到0.960 1,可准确预测战术通信环境中的电波传播情况。     

地面数字电视广播覆盖的研究

随着国家地面数字电视的标准出台,地面数字电视广播将要在国内开展应用.但是地面数字电视广播与地面模拟电视广播存在着一些差异,为此,广科院在分析基本传播模型ITU-R P.370、ITU-RP.1546、ITU-R P.526、Okumura-Hata特性的基础上,借助北京、广州、深圳等地搭建的地面单频网广播系统,通过专用覆盖预测软件,采用不同传播模型,对北京等地地面数字电视广播系统覆盖场强进行了理论预测;并结合北京单频网的23万个实测数据,通过计算不同传播模型下预测值与实测值的均方差,将实测结果与预测结果进行了对比分析,提出了适合我国地面数字电视移动接收时覆盖场强预测的传播模型,为我国地面数字电视电波传播模型的确定提供了基本依据.     

中央节目无线数字化覆盖工程规划试算电波传播模型研究

本文简要介绍了三种常用的电波传播模型,包括ITU-R P.1546、ITU-R P.526和Okumura-Hata,提出了一种用于对中央工程广播电视发射台站的场强覆盖进行仿真试算时使用的电波传播模型的方法,给出了模型确定的总体思路,并以广东省阳江市望瞭岭为典型案例,按照“三步走”原则,采用理论推导结合现场实测的方法,详细介绍了确定电波传播模型的过程,并给出结论。     

高精度折光修正系统射线描迹快速算法

大气折射误差是影响外弹道测量数据的主要误差源之一.受技术条件所限,大气折射误差修正在线算法常采用简化模型,而高精度的大气折射误差修正往往置于事后数据处理工作中.为适应新形势下折光修正系统数据处理的需求,基于大气折射率随高度的分布特征,引入“虚高”概念,并给出了高度迭代步长选取的新方法.通过与传统方法的比较可知:改进方法在保证大气折射误差修正精度的同时,大大提高了射线描迹算法的运算速度;另外,该仿真结果也可为电波大气折射误差修正的工程应用提供参考.     

霍普菲尔德修正模型在实际应用中的缺陷

对流层电波折射误差修正中的霍普菲尔德修正模型和精确修正模型进行了比较研究.研究结果表明:霍普菲尔德修正模型只适用于目标高度在对流层顶以上,仰角在30度以上的情况;对于高精度测量系统,不能应用霍普菲尔德修正模型,最好采用精确电波折射误差修正模型.     

对流层电波折射修正的残差模型研究

电波折射修正常用方法是射线描迹法，其修正精度主要取决于大气时空结构参数的精度。本文给出了影响大气结构精度的四种误差（垂直大气剖面测量误差、大气时变漂移误差、大气水平不均匀性误差和大气随机起伏误差）所引起电波折射修正的残差，并给出了某连续波干涉仪系统中电波折射修正的实用残差模型。     

超短波电波传播路径损耗中值公式的数值实验建模

在超短波30~88 MHz波段,路径损耗中值公式Okumura-Hata模型已远远不能满足实际工程对损耗计算的高精度需求,本文利用电磁数值仿真方法,建立真实城市环境中的电波传播模型,通过大量的多种背景模型和收发条件下的数值实验,拟合出适用于30~88 MHz波段的电波传播路径损耗中值公式,弥补Okumura-Hata公式在低端频段的空缺,有效地扩展了Okumura-Hata公式的应用范围,提高了它的实用性。     

Small-signal model parameter extraction of E-mode N-polar GaN MOS-HEMT using optimization algorithms and its comparison

An improved small-signal parameter extraction technique for short channel enhancement-mode N-polar GaN MOS-HEMT is proposed,which is a combination of a conventional analytical method and optimization techniques.The extrinsic parameters such as parasitic capacitance,inductance and resistance are extracted under the pinch-off condition.The intrinsic parameters of the small-signal equivalent circuit (SSEC) have been extracted including gate forward and backward conductance.Different optimization algorithms such as PSO,Quasi Newton and Firefly optimization algorithm is applied to the extracted parameters to minimize the error between modeled and measured S-parameters.The different optimized SSEC models have been validated by comparing the S-parameters and unity current-gain with TCAD simulations and available experimental data from the literature.It is observed that the Firefly algorithm based optimization approach accurately extracts the small-signal model parameters as compared to other optimization algorithm techniques with a minimum error percentage of 1.3％.     

A new DOA estimation approach for QPSK Alamouti STBC using ICA technique‐based PSO algorithm

Alamouti space time block code (STBC) has been a revolutionary technology in multiple‐input multiple‐output (MIMO) wireless communication since it provides full transmission diversity. To reduce a multi‐path effect and a consumed power, the dynamic beam‐forming technique should be used to enable antennas focusing on a particular area. The aim of this paper is how to reduce the computational complexities of independent component analysis (ICA) and speed up the algorithm used in estimating the direction of arrival (DOA) angles. First, we derive a simple formula to reduce the number of unknown DOA to be one only. Then, real‐imaginary (Re‐Im) decomposition for MIMO system is used to reduce the computational complexities of ICA algorithm. The novel criteria used in this paper is that the kurtosis measuring for the extracted source will be minimum at one of the unknown values of DOA angles. Finally, particle swarm optimization (PSO) will be used as an effective tool to locate the DOA angle positions that minimize the kurtosis measuring. Performance analysis of the proposed approach with QPSK Alamouti STBC in MIMO channel was implemented using MATLAB. The validated criterions for the new approach were first examined. Then, root‐mean‐square‐error (RMSE) was employed to test the proposed approach at different SNR levels. The main parameters that influence on this approach were evaluated. It was found that superior performance could be obtained at ?DOA > 10⁰ when antenna spacing set to be λ/2 using at least 10³ snapshots. The important point observed during simulations was computational complexity (and latency) of the proposed approach was reduced to the minimum by employing Re‐Im decomposition model and PSO algorithm. Consequently, this approach is very efficient for hardware implementations.     

机动目标DOA跟踪粒子滤波算法

针对标准粒子滤波算法在机动目标波达方向(direction of arrival, DOA)随时间快速变化导致跟踪精度下降、实时性变差及多目标跟踪误差大等不足的问题,本文提出了一种改进粒子滤波(particle filter, PF)算法。该算法依据阵列信号处理模型和匀速(constant velocity,CV)模型,建立了机动目标跟踪的状态方程和观测方程作为状态空间模型,并在此基础上,借鉴多重信号分类(multiple signal classification,MUSIC)算法谱函数修改了粒子滤波的似然函数,实现了对目标方位的实时动态跟踪。仿真结果表明,与传统子空间类跟踪算法和标准粒子滤波算法相比,本文方法跟踪精度更高,收敛速度更快,抗噪能力及鲁棒性更强,对轨迹交叉的多目标跟踪性能也更优。      

Direct reconstruction of kinetic parameter images from dynamic PET data

Our goal in this paper is the estimation of kinetic model parameters for each voxel corresponding to a dense three-dimensional (3-D) positron emission tomography (PET) image. Typically, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this "indirect" approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the expectation-maximization (EM) algorithm for direct parametric reconstruction. The approach is "direct" because it estimates the optimal kinetic parameters directly from the sinogram data, without an intermediate reconstruction step. However, direct voxel-wise parametric reconstruction remained a challenge due to the unsolved complexities of inversion and spatial regularization. In this paper, we demonstrate and evaluate a new and efficient method for direct voxel-wise reconstruction of kinetic parameter images using all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. The PICD algorithm is computationally efficient and is implemented with spatial regularization in the domain of the physiologically relevant parameters. Our experimental simulations of a rat head imaged in a working small animal scanner indicate that direct parametric reconstruction can substantially reduce root-mean-squared error (RMSE) in the estimation of kinetic parameters, as compared to indirect methods, without appreciably increasing computation.     

基于总体最小二乘准则的OFDM系统时变信道估计

信道估计的准确程度直接影响正交频分复用系统的性能。为了提高时变信道估计算法的精度,基于总体最小二乘准则( TLS)提出了一种时变信道的估计方法。该方法用线性模型对时变信道进行建模,不仅考虑了信道噪声,同时也兼顾了模型误差。该方法能较好地跟踪信道的变化,显著消除模型误差。仿真结果表明所提算法的均方误差介于最小二乘算法与最小均方误差算法之间,在不同归一化多普勒频移下,该算法具有较好的稳健性。     

星载激光测高仪多模式回波参数提取方法（特邀）

全波形星载激光测高仪的接收波形特征参数可以用于反演目标的形貌信息,传统的波形处理算法不能用于混叠严重以及偏离高斯形态的多模式波形特征参数提取。针对混叠严重的多模式回波,提出一种基于偏正态拟合模型,使用激励Richardson-Lucy反卷积算法、逐层分解算法、梯度下降法和非线性最小二乘拟合算法相组合的波形特征参数提取方法。采用已知参数的波形数据集、机载仿真波形数据集和全球生态系统动态调查（GEDI）激光雷达波形数据,基于波形相关系数与均方根误差（RMSE）、波形特征参数相对误差、波形分量个数提取正确率等评价指标开展波形处理试验,并将处理结果与传统的高斯分解结果进行比较分析。已知参数波形数据集处理结果的平均波形相关系数提升了约2%,RMSE降低了约47%,波形特征参数相对误差平均降低了约5%,分量个数提取正确率提升了约34%;机载仿真数据和GEDI波形数据处理结果的平均波形相关系数分别提升了约1%和2%,RMSE分别降低了约56%和54%。同时,开展了陡坡区域植被高度解算的仿真试验,得到的植被高度准确程度明显高于传统方法。所有处理结果均表明该方法更有利于多模式回波特征参数的提取以及目标参数的反演。     

Performance Analysis of Downlink W-CDMA Systems in Weibull and Lognormal Fading Channels Using Chaotic Codes

In this paper, a subspace based blind channel estimation scheme for downlink W-CDMA systems using chaotic codes under Weibull and Lognormal fading channel conditions is proposed and compared with W-CDMA system using PN codes. The algorithm provides estimates of multiuser channels by exploiting the structural information of the data output. The subspace of the (data + noise) matrix contains sufficient information for unique determination of channels and, hence, the signature waveforms and signal constellations. The proposed channel estimation algorithm is also implemented for multiuser—orthogonal frequency division multiplexing (OFDM) system. Performance measures like bit error rate (BER) and root mean square error (RMSE) are plotted for Weibull and Lognormal fading channels. Signal constellations under Weibull and Lognormal channels are also plotted. Analytical and Simulation results for BER and RMSE are compared for W-CDMA system using PN codes and chaotic codes. Simulation results show that, chaos-based W-CDMA outperforms the PN-based W-CDMA in terms BER and RMSE. Simulation results of multiuser-OFDM system shows that performance is further improved when compared to the W-CDMA system.     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.     

A novel approach of tensor‐based data missing estimation for Internet of Vehicles

In the face of the current huge amount of intelligent traffic data, collecting and statistical processing is a necessary and important process. But the inevitable data missing problem is the current research focus. In this paper, a novel approach of tensor‐based data missing estimation for Internet of Vehicles is proposed for the problem of missing the Internet of Vehicles data: Integrated Bayesian tensor decomposition (IBTD). In the data model construction stage, the random sampling principle is used to randomly extract the missing data to generate a subset of data. And the optimized Bayesian tensor decomposition algorithm is used for interpolation. Introduce the integration idea, analyze, and sort the error results after multiple interpolations, consider the space‐time complexity, and choose the optimal average to get the best result. The performance of the proposed model was evaluated by mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results show that the proposed method can effectively interpolate the traffic data sets with different missing quantities and get good interpolation results.     

Estimating Kinetic Parameter Maps From Dynamic Contrast-Enhanced MRI Using Spatial Prior Knowledge

Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parameters obtained by fitting a pharmacokinetic model to the observed data. Least squares estimates of the highly nonlinear model parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire parameter maps at once, both bias and variance of the parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel).