序贯检测在“北斗”卫星信号捕获过程中的应用

为了提高“北斗”系统的捕获性能,解决在弱信号环境中无法有效捕获与检到卫星信号的问题,在“北斗”系统的捕获方法中采用序贯检测的方式进行了改进与优化。分析后得出,序贯检测通过双门限与待检测区,使得样本数灵活可变,可以有效应对不同信噪比的信号,在强信号与弱信号下都有良好的效果。通过仿真对序贯检测方式与传统检测方式进行了性能上的比较与分析,结果表明,序贯检测逻辑不仅达到了传统检测方法中预期的检测概率和虚警概率,同时具备更高的性能与灵活性,强信号下缩短了捕获时间,提高了捕获效率;弱信号下提高了2～3 dB信噪比增益与捕获性能;既保证了该检测逻辑的可行性,又为捕获检测过程提供了一种思路与方法。     

小型化低功耗机载显示器图形系统设计与实现

为了满足机载显示器图形生成系统的小尺寸、低功耗需求,提出了一种基于龙芯2K1000的全国产图形生成与处理方案。该方案以2K1000为核心构建硬件平台,使用2K1000内部集成的中央处理器、图形处理器、显示控制器,以及外部的内存实现图形的计算生成和双缓存显示,配合国产可编程逻辑器件实现图形与外视频的叠加显示及机载通信总线扩展。软件上采用航空专用的国产天脉操作系统,基于天脉操作系统设计了关键的图形显示驱动、帧存驱动、显示控制器驱动。实验结果表明,在输出1 024 pixel×768 pixel分辨率显示时,典型机载图形画面帧率达到35 frame/s,整体功耗约10 W。该方案扩展性强、功耗低,满足实时显示需求,在机载显示器领域有着广泛的应用空间。     

TETRA通信与干扰信号智能识别技术

陆上集群无线电(Terrestrial Trunked Radio,TETRA)数字集群通信系统因其开放性易受到内外部电磁干扰。干扰信号的类型多种多样,针对不同的干扰样式,采取的抗干扰措施也各不相同,因此干扰信号的识别具有重大意义。基于此,提出了一种干扰信号智能识别技术。该技术首先对受到不同干扰后的TETRA音频数据进行特征提取,并筛选出具有分类能力的特征,其次使用决策树、支持向量机和随机森林3种分类模型对特征提取后的待测试信号进行智能分类识别。实验结果表明,使用的这3种模型能够有效判断TETRA系统中的信号是否受到干扰,以及受到何种样式的干扰,可为后续TETRA系统中的信号干扰识别提供参考。     

基于FPGA的图像自适应AWB系统设计与实现

为了解决常见自动白平衡(Auto White Balance, AWB)方法的场景适应能力不足且实时性较差等问题,提出了一种基于颜色通道直方图重构的自适应AWB方法,并使用现场可编程门阵列(FPGA)对所提出的算法进行硬件电路实现,在校正图像白平衡的同时也确保了系统高速实时处理图像。首先对图像进行限制对比度自适应直方图均衡化(CLAHE)处理来提高图像对比度,然后对图像进行灰度级区间的通道分区统计,对不同场景类别的图像采用颜色直方图匹配或平移的重构方式做自适应处理。实验结果表明,该算法在处理图像白平衡时,相比基于光源估计的AWB算法,色温校正准确率提高了14%,对不同色彩场景有更好的适应性,具有实时处理能力。     

基于TDOA和SDOA的宽带辐射源双机无源定位算法

针对双无人机无源雷达对宽带辐射源目标的直接无源定位(DPD)初始值敏感,提出一种基于小生境拓扑结构改进矢量粒子群算法(NTVIPSO)的直接无源定位算法。本文算法将直接法和间接法结合起来,依靠间接法中尺度差参数构造约束条件,利用小生境矢量粒子群求解得到精确的位置。仿真结果表明本文粒子群算法优于矢量粒子群算法和邻域拓扑结构粒子群算法。提出的存在扰动情况下尺度差约束条件下的直接无源定算法精确度低于直接法,但优于间接法;计算复杂度低于直接法,大于间接法。     

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).     

Comprehensive summary of solid oxide fuel cell control: a state-of-the-art review

A thermoelectric generation (TEG) system has the weakness of relatively low thermoelectric conversion efficiency caused by heterogeneous temperature distribution (HgTD). Dynamic reconfiguration is an effective technique to improve its overall energy efficiency under HgTD. Nevertheless, numerous combinations of electrical switches make dynamic reconfiguration a complex combinatorial optimization problem. This paper aims to design a novel adaptive coordinated seeker (ACS) based on an optimal configuration strategy for large-scale TEG systems with series–parallel connected modules under HgTDs. To properly balance global exploration and local exploitation, ACS is based on ‘divide-and-conquer’ parallel computing, which synthetically coordinates the local searching capability of tabu search (TS) and the global searching capability of a pelican optimization algorithm (POA) during iterations. In addition, an equivalent re-optimization strategy for a reconfiguration solution obtained by meta-heuristic algorithms (MhAs) is proposed to reduce redundant switching actions caused by the randomness of MhAs. Two case studies are carried out to assess the feasibility and superiority of ACS in comparison with the artificial bee colony algorithm, ant colony optimization, genetic algorithm, particle swarm optimization, simulated annealing algorithm, TS, and POA. Simulation results indicate that ACS can realize fast and stable dynamic reconfiguration of a TEG system under HgTDs. In addition, RTLAB platform-based hardware-in-the-loop experiments are carried out to further validate the hardware implementation feasibility.     

Frequency regularization of a linked wind–diesel system using dual structure fuzzy with ultra-capacitor

Microgrids (μ-grids) are gaining increased interest around the world for supplying cheap and clean energy. In this paper, a μ-grid comprising a wind turbine generator (WTG) and diesel generator (DG) is considered. It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas. However, wind energy is intermittent in nature while load demand changes frequently. Therefore, a µ-grid can experience large frequency and power fluctuations. The speed governor of the DG tries to minimize the frequency and power deviations in µ-grid though its operation is slow and cannot adequately minimize system deviations. The paper proposes a novel arrangement based on a dual structured fuzzy (DSF) whose structure changes according to the switching limit with a reduced rule base. It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of the μ-grid. The proposed strategy is tested in a μ-grid and the results considering step load alteration, load alteration at different instants of time, nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design. In addition, it shows that ultra-capacitor establishment in a μ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.     

光伏快速充电站配电变压器端电能质量综合控制研究

针对光伏快速充电站将光伏发电与电动汽车快速充电站有机结合,在充分发挥清洁能源优势和大量提升充电效率的同时所带来的电能质量扰动类型复杂、变化快速等问题,提出了一种基于串并联组合配电网柔性交流输电(distribution flexible AC transmission system, DFACTS)的新型电能质量补偿器的电能质量综合控制方法。深入研究了新型电能质量补偿器主电路拓扑,建立起了动态工作模型。详细分析了新型电能质量补偿器线性自抗扰控制方法的工作原理,设计出线性自抗扰控制器。并针对各DFACTS控制器间的交互影响,提出多DFACTS控制器的协调控制策略。最后搭建仿真模型,进行仿真验证。仿真结果表明,所提出的新型电能质量补偿器采用线性自抗扰控制方法相比传统PI控制方法,提高了电能质量扰动快速变化时的综合补偿能力和动态响应能力。并且验证了该协调控制能有效提高系统电压稳定能力,具有良好的可行性和有效性。     

基于IVIF-VIKOR的雷达辐射源信号分选识别特征性能综合评价方法

为解决雷达辐射源信号分选识别特征评价不够客观和缺乏评价依据等问题,提出了一种基于区间模糊原理、模糊交叉熵和多准则折衷法的特征评价方法. 首先通过区间模糊原理建立信噪比分级评价模型,并基于汉明距离进行寻优得出信噪比权重;其次结合信噪比权重和区间直觉模糊加权平均算子将分级模型整合成群决策矩阵,使用熵最大化法计算属性权重;最后基于多准则折衷法框架,采取模糊交叉熵实现特征方案排序. 仿真实验结果表明,所提方法能够给出与实际仿真实验相一致的分选识别特征评价排序结果,并优于逼近理想点方法,验证了所提方法的可行性和有效性.