Effects of Wind on Steady-state Scan Characteristics and Hit Probability of Terminal-sensitive Projectile

The wind effects on steady-state scan characteristics and hit probability of terminal-sensitive projectile were discussed in this paper. Considering wind as the constitutions of the average wind and the impulsive wind, a simplified wind field model was established for the ballistic calculation of the steady-state scan phase; under the windy condition, the effects of the range wind and the beam wind on the steady-state scan characteristics of the terminal-sensitive projectile were analyzed in detail and its hit probabilities for a certain armored target were calculated. The calculated results show that, when the wind speed exceeds a certain value, the hit probabilities of terminal-sensitive projectile drop rapidly; the wind effects must be considered in the application of the terminal-sensitive projectiles. This paper provides some theoretical references for the fire wind speed correction and the global structure optimization of the terminal-sensitive projectile.

Application of the Probability Matching Correction Method in Precipitation Forecast

Based on the observation data of 24-hour cumulative precipitation from 92 ground meteorological observation stations in Jiangxi province from March to July during 2015-2016 and the high-resolution numerical forecast data of precipitation predicted within 24-72 h by the European Centre for Medium-Range Weather Forecasts（ ECMWF）,the Gamma function was used as the fitting function of probability distribution of cumulative precipitation to match the probability of predicted and observed precipitation. Moreover,the change of forecast score before and after the correction was tested. The results showed that the predicted values of heavy precipitation based on ECMWF model were smaller than the observed values,while the predicted values of light precipitation were larger than the observed values. The probability matching correction method could be used to effectively correct systematic errors of model forecast,and the correction effect of all grades of precipitation（ especially for rainstorm） was good.The shorter the period of validity was,the better the correction effect was. The correction method has a good application effect in the interpretation of model precipitation products,and can provide better security services for agricultural production.

Image Relaxation Matching Based on Feature Points for DSM Generation

In photogrammetry and remote sensing, image matching is a basic and crucial process for automatic DEM generation. In this paper we presented a image relaxation matching method based on feature points. This method can be considered as an extention of regular grid point based matching. It avoids the shortcome of grid point based matching. For example, with this method, we can avoid low or even no texture area where errors frequently appear in cross correlaton matching. In the mean while, it makes full use of some mature techniques such as probability relaxation, image pyramid and the like which have already been successfully used in grid point matching process. Application of the technique to DEM generaton in different regions proved that it is more reasonable and reliable.

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image(TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter(EnKF) and ensemble smoother(ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

Probability density analysis of SINR in massive MIMO systems with matched filter beamformer

This paper derives an approximate formula for probability density function(PDF) of received signal-to-interference-and-noise ratio(SINR) at user terminal when matched filter(MF) is adopted at a base station(BS).This distribution of SINR can be used to make an analysis of average sum-rate,outage probability,and symbol error rate of massive MIMO downlink with MF at BS.From simulation,it is found that the derived approximate analytical expression for PDF of SINR is consistent with the simulated exact PDF from the definition of SINR in medium-scale and large-scale MIMO systems.

Controller design for stochastic nonlinear systems with matched conditions

This paper is concerned with the global boundedness problem for a class of stochastic nonlinear systems with matched conditions. The uncertainties in the systems are due to parameter variations and external stochastic disturbance. Only the matched conditions and the possible bound of the uncertainties are demanded. Based on the stochastic Lyapunov stability theory, an explicit controller is constructed in the gradient direction, which renders responses of the closed-loop systems be globally bounded in probability. When the systems degrade to linear systems, the controller becomes linear. Illustrative examples are given to show the effectiveness of the proposed method.

New code match strategy for wideband code division multiple access code tree management

Orthogonal variable spreading factor channelization codes are widely used to provide variable data rates for supporting different bandwidth requirements in wideband code division multiple access (WCDMA) systems. A new code match scheme for WCDMA code tree management was proposed. The code match scheme is similar to the existing crowed-first scheme. When choosing a code for a user, the code match scheme only compares the one up layer of the allocated codes, unlike the crowed-first scheme which perhaps compares all up layers. So the operation of code match scheme is simple, and the average time delay is decreased by 5.1%. The simulation results also show that the code match strategy can decrease the average code blocking probability by 8.4%.

Error Probability Analysis for Ultra-Massive MIMO System and Near-Optimal Signal Detection

In this paper,average bit error probability(ABEP)bound of optimal maximum likelihood(ML)detector is first derived for ultra massive(UM)multiple-input-multiple-output(MIMO)system with generalized amplitude phase modulation(APM),which is confirmed by simulation results.Furthermore,a minimum residual criterion(MRC)based lowcomplexity near-optimal ML detector is proposed for UM-MIMO system.Specifically,we first obtain an initial estimated signal by a conventional detector,i.e.,matched filter(MF),or minimum mean square error(MMSE)and so on.Furthermore,MRC based error correction mechanism(ECM)is proposed to correct the erroneous symbol encountered in the initial result.Simulation results are shown that the performance of the proposed MRC-ECM based detector is capable of approaching theoretical ABEP of ML,despite only imposing a slightly higher complexity than that of the initial detector.

Enhanced spectrum sensing using a combination of energy detector,matched filter and cyclic prefix

In this paper,we propose a sensing scheme based on energy detection,matched filter and cyclic prefix.Both Equal Gain Combining(EGC)and optimal combination of the aforementioned detectors are investigated in cooperative and non-cooperative spectrum sensing scenarios.In packet transmission systems such as OFDM(Orthogonal Frequency Division Multiple access)systems,the proposed scheme takes advantage of utilizing more samples than individual detectors,i.e.,cyclic prefix,training or pilot samples,and data payload samples.The proposed combine-sensing scheme offers higher detection probability and lower false alarm probability,as compared with the performance of individual detectors over the same frame duration.Simulation results are congruent with the theoretical curves and confirm the validity of our derivations.

四川地区精细化降水预报融合订正试验及检验

精细化定量降水网格预报是天气预报业务的重点和难点,基于西南区域智能数值网格模式预报系统(Southwest China WRF-based Intelligent Numeric Grid forecast System,SWC-WINGS)1 km×1 km分辨率的小时降水预报,利用时间滞后和概率匹配方法开展融合订正试验,再利用中国气象局多源融合降水系统(CMA Multi-source Precipitation Analysis System,CMPAS)三源融合降水实况格点数据,对2022年7—8月四川地区的小时降水预报融合订正结果进行检验,并在四川盆地西部一次短时强降水天气过程中进行应用,结果表明:(1)时间滞后集合降水预报相较于模式降水预报,存在小量级预报过度,大量级预报过于保守的问题;(2)时间滞后结合概率匹配的降水预报融合订正方法有效提升了各量级降水预报的TS评分,尤其1~2 h预报时效提升显著,小时雨量超过0.1 mm、5 mm、10 mm和20 mm量级的TS评分平均提升率分别为7.2%、17.2%、28.3%和36.3%;(3)一次短时强降水天气过程的应用结果表明,时间滞后结合概率匹配的融合订正方法对模式小时降水预报有较好的改进效果,尤其对大量级降水预报有较强的订正能力。

基于ACM的LFM信号抗瞄准式干扰方法

瞄准式干扰由于干扰能量集中、干扰强度大,严重影响雷达目标探测性能。针对瞄准式干扰,提出了基于幅度编码调制(amplitude code modulation,ACM)的信号抗干扰方法。首先,通过设计ACM信号,实现雷达信号的频谱搬移,并根据ACM信号参数对匹配滤波器进行频移处理,获得谐波脉压的参考信号。其次,对谐波进行脉冲压缩处理,得到目标回波在谐波处的一维距离像。然后,通过能量补偿,弥补ACM带来的能量损失,即可得到与原始信号回波一致的目标一维距离像。最后,给出了ACM及回波抗干扰处理流程,并通过数字仿真进行验证。仿真结果表明,所提方法所获得的目标回波一维距离像与未被干扰信号获得的目标回波一维距离像基本一致。当调制频率为5倍信号带宽时,ACM信号较原始信号抗瞄准式干扰性能提升了32 dB。

概率-非概率调查样本的整合推断问题研究:核匹配方法

基于网络便利调查、大数据平台的数据收集方式,在实践中得到了广泛的发展,但获得的样本本质上均是非概率样本。利用非概率样本推断目标总体特征面临着潜在的偏差,如涵盖偏差、自我选择偏差等。近年来,对融合概率调查和非概率调查的数据资源,以估计有限总体特征问题的讨论较多,但依然存在较多问题。在已有研究的基础上,对非概率样本和概率样本均测量了辅助变量,但只有非概率样本测量了研究变量的背景下,介绍基于倾向得分框架的权数构造方法;在倾向得分核匹配方法的基础上,提出了基于融合概率和非概率样本协变量平衡的核函数带宽选择方法,为非概率样本构造倾向得分核匹配权数。模拟结果显示基于倾向得分核匹配的方法能够显著降低非概率样本的偏差,提出的融合样本协变量平衡的带宽方法能够有效减少估计量的相对偏差、绝对相对偏差和标准差。

PSM⁃DID在政策评价中的应用现状与改进方法

倾向得分匹配-双重差分模型(PSM⁃DID)是政策评估及因果推断中最为流行的方法之一.但是在实际应用中,该方法面临着控制变量在处理组样本和控制组样本之间非平衡性的挑战.传统基于均值差异t检验的平衡性检验容易产生片面和误导性的结论,使得后续因果推断产生偏误.为克服上述问题,本文对传统的平衡性检验提出以下改进:一是推荐更全面的多维度的平衡性测度指标,便于在匹配后更严谨地比较处理组和控制组的平衡性;二是提出了适用于非平衡样本的新估计方法:倾向得分匹配-逆概率加权-双重差分(PSM⁃IPW⁃DID),该方法结合了倾向得分匹配(PSM)克服样本自选择内生性及对非平衡样本稳健的优势和逆概率加权(inverse probability weighting,IPW)利用全样本信息的长处,在不进一步删除样本的情况下得到一种更稳健的双重差分估计方法.数据模拟和应用实例显示,本文提出的新方法能更全面、客观地评价宏观、微观政策的作用,得到更为可信的因果推断.

A knowledge push technology based on applicable probability matching and multidimensional context driving

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push＇, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

概率匹配订正法在湖北襄阳地区降水预报中的应用

利用2016—2020年汛期ECMWF模式预报降水与湖北襄阳区域站观测降水进行对比分析,结果表明:ECMWF对中雨及以上降雨的预报,第1、2天预报值偏小,第3天预报值偏大;3个预报时段强降雨中心位置偏差无规律。为更好地对ECMWF产品进行释用,提高汛期降水预报准确率,从概率匹配角度研究了不同降水量级订正值,并对2021年汛期ECMWF降水预报进行逐日检验。结果显示:概率匹配订正法可有效地改善模式预报性能,对中雨及以上降雨预报均有良好的订正效果,尤其对第1天暴雨预报改进最为明显。228站平均的TS评分提高了6%,由11.1%增加到17.1%,漏报情况改良了13.5%,由85.0%降至71.5%。采用该订正法开展定量降水预报,由于增加了当地降雨概率分布的背景信息,能表现出比原模式更高的参考价值。

基于航迹概率假设密度的多传感器多目标跟踪

针对基于概率假设密度(probability hypothesis density,PHD)的分布式多传感器多目标跟踪(distributed multi-sensor multi-target tracking,DMMT)存在无法形成航迹、计算复杂度高、目标漏检等问题。本文基于航迹PHD后验估计提出了一种DMMT方法。为此,首先构建了各节点估计航迹间相似性度量矩阵,并采用匈牙利算法实现最优航迹匹配;其次采用协方差逆准则对关联航迹实现并行融合;最后基于概率生成泛函推导了一种鲁棒的DMMT方法。仿真实验验证了所提算法在目标状态估计精度、计算有效性和实时性方面的优势。

基于图像先验信息的立体匹配算法

立体匹配是双目视觉系统分析中的重要环节之一,直接决定三维信息重建的质量和效率。为提升立体匹配实时性与准确性,提出一种基于图像先验信息的立体匹配算法。算法首先采用BP神经网络MIV方法提取并筛选图像有效特征值,并以视觉系统应用环境不同将图像数据分为的简单背景图像和复杂背景图像,然后在测距1-2m的实验室条件,利用双目摄像头和CORE I7处理器采集图像数据,并在Visual Studio 2015中按照3:2对图像进行大小裁剪,最后基于BM优化算法与SGBM改进算法对图像进行立体匹配。简单背景仿真结果表明,未裁剪BM优化算法的测量误差未0.9%,仿真时间为2s,较其它算法而言,仿真时间最短,实时性最高;复杂背景仿真显示,裁剪后的SGBM改进算法,测量误差为0.4%,仿真时间2.5s,测量误差大幅降低。在图像先验信息的基础上,通过优化BM算法提高了立体匹配实时性,基于改进SGBM算法提高了立体匹配准确性,为双目视觉系统的实际应用提供了理论依据。

FY-4A卫星地表太阳辐射产品在河南省适用性评估与订正方法研究

以河南省26个地面辐射站观测的地面总辐照度逐小时数据为基准,对FY-4A反演的总辐照度产品在河南省的适用性进行验证分析,并采用概率密度函数方法,对FY-4A总辐照度产品的系统误差进行订正。结果表明,(1)FY-4A总辐照度整体大于地面观测结果,两者平均误差在豫南偏大、豫北偏小,而且从时间演变看,两者相对误差在冬半年偏小,夏半年偏大,这与云量、大气相对湿度等气象要素以及不同下垫面对FY-4A反演总辐照度的影响有关;(2)FY-4A卫星与地面观测的总辐射辐照度平均误差随不同辐照度等级而变化,整体呈现出一种非独立系统误差的特性,即对于低辐照度有所高估,而对于高辐照度则相对低估;(3)概率密度函数方法对FY-4A总辐照度具有较好的订正能力,在夏季以年为单位建立订正模型进行误差订正效果更好,在秋冬季以季节为时间尺度建立订正模型效果更好。

基于集合预报优选台风路径订正技术的广西风雨预报研究

利用2017—2019年欧洲中心全球集合预报数据以及中国气象局热带气旋资料中心的最佳路径数据集,基于统计分析和集合预报释用技术,对影响广西的台风路径预报提出一种实时订正技术。通过优选N个误差较小的集合预报成员,生成台风路径订正产品、台风概率匹配平均降水产品和台风大风合成分析产品。结果表明,利用优选集合成员的算术平均模型预报台风路径比集合预报平均、确定性预报效果改善,且预报时效越长,改善效果越明显,24 h、48 h时效的台风路径预报误差比集合平均减小4 km和6 km,比确定性预报减小5 km和19 km;优选集合成员的概率匹配平均降水产品,在一定程度上能改善传统的集合平均降水产品,特别是对大暴雨落区预报,36 h、60 h时效大暴雨TS评分较集合平均提高10%和12%;优选集合成员的大风合成分析产品,能预报出台风影响时极大风区移动、增强、减弱的态势,36 h时效6、7、8级以上极大风的TS评分较集合平均提高18.3%、15.7%、13.4%,对9~11级以上的极大风有一定的预报能力。

基于最优TS评分的多模式集成降水预报

利用2020年1月1日至2023年1月2日欧洲中心、美国、德国、中国和日本降水预报资料和眉山市的逐日降水观测资料,用最优TS评分法对模式降水进行订正,再使用概率匹配平均、消除偏差集成平均和加权集成平均三种方案进行多模式集成预报,通过检验评分和个例分析。结果显示,概率匹配平均方案提升晴雨准确率,但对大雨和暴雨的预报效果差。消除偏差集成平均方案对降水预报的提升效果较小。加权集成平均方案和分类站点集成预报均能大幅提升晴雨准确率,并减小暴雨漏报,使暴雨TS评分得到较大提升。