AN EFFICIENT APPROXIMATE MAXIMUM LIKELIHOOD SIGNAL DETECTION FOR MIMO SYSTEMS

This paper proposes an efficient approximate Maximum Likelihood (ML) detection method for Multiple-Input Multiple-Output (MIMO) systems,which searches local area instead of exhaustive search and selects valid search points in each transmit antenna signal constellation instead of all hy-perplane. Both of the selection and search complexity can be reduced significantly. The method per-forms the tradeoff between computational complexity and system performance by adjusting the neighborhood size to select the valid search points. Simulation results show that the performance is comparable to that of the ML detection while the complexity is only as the small fraction of ML.

Maximum Likelihood Detection for Detect-and-Forward Relay Channels

This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).

Fall detection system in enclosed environments based on single Gaussian model

In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.

Robust Speech Endpoint Detection in Airplane Cockpit Voice Background

A method of robust speech endpoint detection in airplane cockpit voice background is presented. Based on the analysis of background noise character, a complex Laplacian distribution model directly aiming at noisy speech is established. Then the likelihood ratio test based on binary hypothesis test is carried out. The decision criterion of conventional maximum a posterior incorporating the inter-frame correlation leads to two separate thresholds. Speech endpoint detection decision is finally made depend on the previous frame and the observed spectrum, and the speech endpoint is searched based on the decision. Compared with the typical algorithms, the proposed method operates robust in the airplane cockpit voice background.

AN ENHANCED DETECTION ALGORITHM FOR V-BLAST SYSTEM

A decoding method complemented by Maximum Likelihood (ML) detection for V-BLAST (Verti- cal Bell Labs Layered Space-Time) system is presented. The ranked layers are divided into several groups. ML decoding is performed jointly for the layers within the same group while the Decision Feedback Equalization (DFE) is performed for groups. Based on the assumption of QPSK modulation and the quasi-static flat fading channel, simulations are made to testify the performance of the proposed algorithm. The results show that the algorithm outperforms the original V-BLAST detection dramatically in Symbol Error Probability (SEP) per- formance. Specifically, Signal-to-Noise Ratio (SNR) improvement of 3.4dB is obtained for SEP of 10?2 (4×4 case), with a reasonable complexity maintained.

联合RIS子块与接收天线的索引调制方案性能分析

利用大量无源反射器件设计的可重构智能表面(RIS),具有拓展无线通信覆盖范围、降低系统成本/功耗和提升未来通信系统性能的优势;而索引调制(IM),通过激活通信资源的子集可实现信息的隐性传输,有望为无线通信带来更高的频谱效率和能量效率,因此,为进一步提升未来无线通信系统的性能,该论文融合RIS与IM技术,提出一种联合RIS子块与接收天线的IM方案。在该方案中,按照相邻原则,将RIS反射元件分割为若干子块,进而利用RIS子块索引与接收天线索引同时实现附加信息传递,即对待传输比特序列,将其分割为三部分,其中第一部比特被调制为星座符号,第二部分用于指示RIS子块划分,第三部分用于指示接收天线索引;在此基础上,根据最大似然(ML)检测的成对错误概率(PEP)解析形式,分四种情况,从理论上推导出对应的矩母函数(MGF),得到无条件PEP的解析表达式,最后得到该方案ML检测的平均误码率(ABER)性能上界。仿真结果表明,与现有RIS辅助的IM方案相比,无须增加额外频谱资源,所提方案的系统可达速率和系统差错性能方面均具有一定优势;同时,验证了ML检测的ABER上界具备严格一致性。

基于EM-VB的分布式接收运动目标直接符号检测方法

相比于传统分布式组阵接收采用的参数差异估计、信号校准合成以及符号检测的逐级处理结构,直接利用多个观测信号进行符号检测能够抑制信号间校准精度不佳带来的性能损失问题,但现有方法主要针对收发均静止或收发理想同步的情形。研究了一种最大似然准则下的分布式接收运动目标直接符号检测方法,首先给出了直接符号检测求解模型,针对模型中多组未知参数的优化问题,推导分析了各参数近似闭式解,采用基于迭代重估的闭环处理结构,利用多个未知参数和信息符号进行联合寻优。仿真实验结果表明,所提方法性能明显优于传统合成处理方法,与现有联合处理结构相比,在观测站数目较多时具有明显优势。

面向高速PAM4有线收发机的自适应和低复杂度最大似然序列检测器

高速串行收发机是中央处理器、网卡和交换机等高性能芯片的关键部件.判决反馈均衡器(decision feedback equalization,DFE)是高速串行收发机的主要判决电路.针对传统DFE在高码间干扰(intersymbol interference,ISI)信道下的高误码率制约串行收发机速率提升的问题,提出一种面向4电平调制(4 pulse amplitude modulation,PAM4)串行收发机的自适应、低复杂度的减状态序列检测器(adaptive reduced-state sequence detector,ARSSD).ARSSD基于最大似然序列检测结构降低检测误码率;结合Viterbi算法和分区算法降低运算复杂度;采用基于迫零算法的ISI参数获取方式实现检测器参数的自适应更新.所提结构最终完成了行为仿真、电路设计以及系统验证.基于模拟前端芯片和现场可编程门阵列电路的实验结果表明,与传统DFE相比,当12~64 Gbps PAM4信号经过−8~−18 dB@16 GHz衰减信道时,32×4路并行ARSSD检测误码率降低2个数量级,与行为仿真结果一致.

面向6G快时变信道下OTFS系统的信号检测

OTFS调制可以有效解决无线信道的快速时变特性给信号检测带来的巨大挑战,可以为高速移动设备提供可靠的无线通信。针对最大似然算法直接应用于OTFS系统进行信号检测时复杂度过高的问题,提出基于并行干扰消除的最大似然检测算法。在延时多普勒域,信道呈现正交、稀疏、可分离特性,发送信号和信道信息呈现出二维卷积的形式。基于此性质同时利用已检测信号,在检测阶段去除已检测信号形成的干扰,降低最大似然算法的复杂度。在此基础上在发送端插入零值使得接收信号在延时方向实现多径分离,进一步降低算法的复杂度。数值仿真结果表明,所提算法在OTFS系统中能够实现高准确率的信号检测,可以有效对抗高速移动场景下的快时变信道。

基于信道短化和圆球译码的水声通信迭代序列检测

The demand for high-data-rate underwater acoustic communications(UACs)in marine development is increasing;however,severe multipaths make demodulation a challenge.The decision feedback equalizer(DFE)is one of the most popular equalizers in UAC;however,it is not the optimal algorithm.Although maximum likelihood sequence estimation(MLSE)is the optimal algorithm,its complexity increases exponentially with the number of channel taps,making it challenging to apply to UAC.Therefore,this paper proposes a complexity-reduced MLSE to improve the bit error rate(BER)performance in multipath channels.In the proposed algorithm,the original channel is first shortened using a channel-shortening method,and several dominant channel taps are selected for MLSE.Subsequently,sphere decoding(SD)is performed in the following MLSE.Iterations are applied to eliminate inter-symbol interference caused by weak channel taps.The simulation and sea experiment demonstrate the superiority of the proposed algorithm.The simulation results show that channel shortening combined with SD can drastically reduce computational complexity,and iterative SD performs better than DFE based on recursive least squares(RLS-DFE),DFE based on improved proportionate normalized least mean squares(IPNLMS-DFE),and channel estimation-based DFE(CE-DFE).Moreover,the sea experimental results at Zhairuoshan Island in Zhoushan show that the proposed receiver scheme has improved BER performance over RLSDFE,IPNLMS-DFE,and CE-DFE.Compared with the RLS-DFE,the BER,after five iterations,is reduced from 0.0076 to 0.0037 in the 8–12 k Hz band and from 0.1516 to 0.1145 in the 13–17 k Hz band at a distance of 2000 m.Thus,the proposed algorithm makes it possible to apply MLSE in UAC in practical scenarios.

实现MIMO系统定向干扰抑制的迭代MLD方案

收发多天线(Multiple Input Multiple Output,MIMO)空时分组编码(Space-Time Block Codes,STBC)系统中存在定向干扰,特别是军用通信环境下存在强定向干扰时,需进行有效的干扰抑制以避免性能恶化.本文提出了一种迭代最大似然解码(Maximum-Likelihood Decoding,MLD)方案.该方案通过噪声子空间投影实现了强干扰下的有效信道估计,利用等效信道矩阵的估计值代替真实信道矩阵的估计值实现干扰抑制和解码;并在似然函数中引入了信道估计误差以减小其对解码性能的不良影响,通过迭代累积代价函数的增量,实现了按分组进行解码,大大减少了搜索计算量.仿真结果表明该方案可有效抑制干扰,且其性能对导频长度的变化不敏感,更具适用性.

高分辨率遥感影像土地利用及其变化检测

为探究不同分类方法在高分辨率遥感影像土地利用及变化检测中的应用,基于淮南市2017-2018年高分一号宽幅相机(GF-1WFV)数据,分别采用最大似然法和支持向量机法进行土地利用及其变化检测,分析不同分类结果的空间分布、转移矩阵、动态变化,根据其分类精度,最后比较它们的优缺点。结果表明:两种分类方法的林地、耕地、水体和人工建筑分类总体精度均在98%以上,Kappa系数在97%以上,具有较高的分类精度。2017-2018年林地和建筑面积有所增加,其中林地的增加较为稳定,人工建筑的转入和转出百分比都比较高,耕地的变化在不同的分类方法下显示都是明显减少,水体的变化在使用最大似然法分类时是增加的,而使用支持向量机法分类时是减少的。因此,不同分类方法所得到的结果受特定数据集、数据预处理、特征选取、参数选择等因素的影响而存在差异。

Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network

Herbicide use is rising globally to enhance food production,causing harm to environment and the ecosystem.Precision agriculture suggests variable-rate herbicide application based on weed densities to mitigate adverse effects of herbicides.Accurate weed density estimation using advanced computer vision techniques like deep learning requires large labelled agriculture data.Labelling large agriculture data at pixel level is a time-consuming and tedious job.In this paper,a methodology is developed to accelerate manual labelling of pixels using a two-step procedure.In the first step,the background and foreground are segmented using maximum likelihood classification,and in the second step,the weed pixels are manually labelled.Such labelled data is used to train semantic segmentation models,which classify crop and background pixels as one class,and all other vegetation as the second class.This paper evaluates the proposed methodology on high-resolution colour images of canola fields and makes performance comparison of deep learning meta-architectures like SegNet and UNET and encoder blocks like VGG16 and ResNet-50.ResNet-50 based SegNet model has shown the best results with mean intersection over union value of 0.8288 and frequency weighted intersection over union value of 0.9869.

Confidence Interval Estimation of the Correlation in the Presence of Non-Detects

This article deals with correlating two variables that have values that fall below the known limit of detection (LOD) of the measuring device;these values are known as non-detects (NDs). We use simulation to compare several methods for estimating the association between two such variables. The most commonly used method, simple substitution, consists of replacing each ND with some representative value such as LOD/2. Spearman’s correlation, in which all NDs are assumed to be tied at some value just smaller than the LOD, is also used. We evaluate each method under several scenarios, including small to moderate sample size, moderate to large censoring proportions, extreme imbalance in censoring proportions, and non-bivariate normal (BVN) data. In this article, we focus on the coverage probability of 95% confidence intervals obtained using each method. Confidence intervals using a maximum likelihood approach based on the assumption of BVN data have acceptable performance under most scenarios, even with non-BVN data. Intervals based on Spearman’s coefficient also perform well under many conditions. The methods are illustrated using real data taken from the biomarker literature.

改进蚁群优化算法的最大似然DOA估计方法

针对将连续域蚁群优化算法应用于最大似然(maximum likelihood,ML)估计中存在计算量过大的问题,提出一种基于改进蚁群优化(modified ant colony optimization,MACO)算法的最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计方法.采用精英反向学习策略获得较优初始解群体,结合全局跨邻域搜索和高斯核函数局部搜索对蚁群的寻优方式进行优化,扩大了算法的搜索空间并加快了收敛速度,最终得到ML估计方法的非线性全局最优解.仿真结果表明,与基于粒子群优化(particle swarm optimization,PSO)算法、蚁群优化(ant colony optimization,ACO)算法的ML估计方法相比,ML-MACO算法的收敛速度是ML-ACO算法的4倍,计算量是ML-ACO算法的1/3,分辨成功率高于ML-PSO算法和ML-ACO算法,估计误差小于ML-PSO算法和ML-ACO算法.ML-MACO算法以更低的计算量保持了ML算法的优良估计性能,收敛性能更优且估计精度更高.

基于压缩感知的低复杂度广义空移键控信号检测算法

广义空移键控(GSSK)作为空间调制(SM)的一种简化形式,被广泛应用于大规模多输入多输出(MIMO)系统,以更好地解决传统MIMO技术中的信道间干扰(ICI)、天线间同步(IAS)和多射频(RF)链路等问题。针对GSSK系统最大似然(ML)检测算法计算复杂度高的问题,结合压缩感知(CS)中的子空间追踪(SP)算法和ML检测算法,并结合阈值的设置,提出一种基于CS理论的低复杂度GSSK信号检测算法。首先,用改进的SP算法获得部分发送天线组合(TAC);其次,删除部分天线组合,缩小搜索天线组合的集合;最后,利用ML算法和预设的门限估计发送天线组合。仿真实验结果表明,所提算法的计算复杂度明显低于ML检测算法,同时误比特率(BER)性能逼近ML检测算法,验证了所提算法的有效性。

Urbanization and Other Land Use Land Cover Change Assessment in the Greater Kumasi Area of Ghana

Urbanization posits the expression of urban expanse expansion due to population growth, rise in built-up areas, high population density and its correspondingly urban way of life. Unrestrained impetus of development and land use land cover change (LULCC) portent several issues such as unlawful urban sprawl, loss of agricultural land, forest loss and other associated complications. This study analyzed the dynamics of urbanization and other LULCC in Ghana’s Greater Kumasi area via Landsat images (TM 1986, OLI 2013 and OLI 2023) using ERDAS Imagine, Idrisi and ArcGIS software. Implementing supervised classification technique, the Maximum Likelihood Classifier (MLC) procedure was employed to categories the study area into five LULC classes. Accuracy assessment undertaken on the resultant LULC maps was deemed very satisfactory. The results from 1986-2023 pointed to an upsurge in a built-up extent as of 8% to 41%, a decrease in Closed Forest from 9% to 4%, another decrease in Open Forests from 64% to 33%, a slight increase from 16% to 20% in farmlands and a stable level of water share. Further analysis indicated that the study area had undergone LULCC within the periods 1986-2013 and 2013-2023 at 60% and 37% respectively. The findings showed uncontrolled urban sprawling along major roads and forest loss as deforestation outside protected areas and degradation in protected forest. The monitoring of urbanization and other LULCC is important for local, and national governments and other bodies charged with the implementation of programs and policies that manage and utilize natural resources. Development adapts to mitigate the effect on the environment.

基于LF RFID的进出检测算法

LF RFID技术的性能易受到复杂环境因素的影响,特别是在金属环境中,会导致检测精度低、稳定性差。为此,提出了一种基于改进的最大似然的准确定位算法的进出检测方法。该定位算法包括训练阶段和定位阶段。在训练阶段,建立了修正指纹数据库,用于修正由于环境因素造成的测距误差;在定位阶段,利用修正后的测距误差,通过最大似然算法来定位目标所携带的标签位置,以检测目标的进出状态。实验结果表明,该算法的检测精度可以达到3 cm,在设定的两种行走策略下,目标的进出检测精度可以达到99%以上,满足实际应用需要。

高光谱遥感检测复合绝缘子运行状态技术研究

挂网运行的复合绝缘子若出现劣化,电气绝缘性能降低,若不及时替换将危害线路的安全运行。目前,国内外尚缺乏成熟的绝缘子运行状态检测方法,笔者在研究国内外复合绝缘子检测方法的基础上,首次引入高光谱遥感技术检测复合绝缘子的运行状态。采用高光谱成像仪对不同运行状态的硅橡胶绝缘子伞套及绝缘子串进行影像采集,成功地构建了复合绝缘子样本光谱特征库,并直观地展示了不同运行状态复合绝缘子光谱曲线及其在特征空间中的聚类特性。继而,利用特征光谱库,采用概率判决函数和贝叶斯判别规则对目标绝缘子进行状态检测,结果显示此方法可以准确地检测出绝缘子的运行状态。

基于小波域隐马尔可夫模型故障诊断方法

针对基于小波能量谱和能量谱熵的故障诊断方法要求小波分解系数基本符合高斯分布这一不足,提出一种基于多尺度小波域隐马尔可夫模型(WHMM)参数特征的故障诊断方法。该方法分析了信号多尺度小波分解系数的统计特征,利用隐马尔可夫模型描述小波变换域系数在尺度间,尺度内的统计相关性。采用最大似然估计方法确定的模型参数作为信号特征实现故障诊断。试验结果证实了设计思想的正确性和算法的高效检测性能。最后从小波基、窗口宽度和分类器三个层面对建议方法诊断性能的影响进行分析,结果表明本文方法具有很强的稳定性和鲁棒性。