基于图像对比度的舰船目标成像算法

针对舰船目标ISAR成像时舰船随机摆动对于成像质量的影响,提出了一种同时基于多普勒中心估计和图像对比度准则的最优成像时间段选择方法。该方法根据海舰船回波的实际数据,估计各个回波的多普勒中心频率,并根据多普勒中心曲线选取舰船目标的线性成像段。然后,通过采用图像对比度准则,对该线性段进行处理。相比已有的方法,本文的成像效果更好。该方法易于实现且运算量较低,适用于实时应用。仿真数据的处理结果验证了本文所提方法的有效性。     

基于多通道采样和注意力重构的图像压缩感知

近年来用于图像压缩感知的深度学习网络得到广泛关注,深度学习网络可以实现图像的压缩采样,并从采样数据重构出原始图像。但现有的压缩感知算法在信息分布不均匀的图像场景中,无法有效提取原始图像信息,导致重构精度较低。针对上述问题,本文提出了基于多通道采样和注意力重构的图像压缩感知算法。该算法包含了多个不同采样率的采样通道,能够根据视觉显著性对图像不同区域应用不同的采样率,使得采样数据中能够包含更多原始图像信息。重构采用了残差通道注意力结构,自适应调整通道特征来提高网络的表示能力。通过对比实验表明,本文提出的基于多通道采样和注意力重构的图像压缩感知算法能够取得更好的重构质量与视觉观感。     

逆合成孔径雷达相位补偿算法研究

逆合成孔径雷达的运动补偿技术是其成像技术中关键所在,而逆合成孔径雷达的运动补偿包括距离对准和相位补偿两方面,其中相位补偿在成像中起到了至关重要的作用,分别采用单特显点法、多普勒中心跟踪法和PGA自聚焦法对在最小熵包络对齐后的数据进行成像,仿真结果显示PGA算法在相位补偿算法中优势明显。     

Effects of leakage delay on global asymptotic stability of complex‐valued neural networks with interval time‐varying delays via new complex‐valued Jensen's inequality

This paper investigates the global asymptotic stability analysis for a class of complex‐valued neural networks with leakage delay and interval time‐varying delays. Different from previous literature, some sufficient information on a complex‐valued neuron activation function and interval time‐varying delays has been considered into the record. A suitable Lyapunov‐Krasovskii functional with some delay‐dependent terms is constructed. By applying modern integral inequalities, several sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed system model. All the proposed criteria are formulated in the structure of a complex‐valued linear matrix inequalities technique, which can be checked effortlessly by applying the YALMIP toolbox in MATLAB linear matrix inequality. Finally, two numerical examples with simulation results have been provided to demonstrate the efficiency of the proposed method.     

Compressive sensing‐based adaptive sparse predistorter design for power amplifier linearization

Greedy algorithms in the compressive sensing theory have been formed the essential method for pruning power amplifier (PA) behavioral models and digital predistorters (DPDs). However, the inherent batch mode of these algorithms limits their application in adaptive digital predistortion framework. In this paper, a powerful subspace pursuit greedy scheme combined with stochastic gradient descent adaptive algorithm is proposed to design a class of adaptive sparse DPDs. According to the given sparsity level, the proposed approach can obtain the sparse terms of DPDs and extract the corresponding coefficients adaptively. Performance improvement of the proposed method is validated by simulation results on the adaptive DPD excited by 15‐MHz 3‐carrier Long‐Term Evolution signals and 50‐MHz 16 amplitude/phase‐shift keying signals. Meanwhile, measurement results on a Doherty PA excited by 30‐MHz 3‐carrier Long‐Term Evolution signals are also performed to verify the advantage of the proposed approach. Simulation and experimental results show that proposed algorithm can efficiently construct the adaptive sparse DPD models with only a small number of parameters; both nonlinear distortions and memory effects in the PA can be almost completely removed. A comparison with the nonsparsity aware DPD techniques and batch mode compressive sensing pruning techniques has been demonstrated that the proposed method exhibit faster convergence, improving tracking capabilities and reduced computational complexity.     

W波段ISAR包络对齐方法研究

在ISAR成像处理中,目标整体在距离方向的位移会使散射点的回波无法落在相同的距离单元内,造成成像结果的散焦,因此采用包络对齐的方法对该位移进行补偿。然而在W波段的ISAR包络对齐处理过程中,较高的分辨率会使回波包络在成像时间内发生变化,导致传统方法产生对齐误差。针对该问题,提出了一种使用Keystone变换进行包络规整的方法来提高包络对齐的准确度。在Keystone变换之前,使用了目标重心跟踪及补偿的方法来实现回波信号的相干化。实验结果表明,通过包络规整处理,成像结果的极值点强度,图像熵以及图像对比度都得到了较大的提升。     

基于变分模态分解和压缩感知的电力系统宽频振荡监测方法

随着“双碳”目标的提出，未来电力系统会有更高比例可再生能源及电力电子设备并网，会引发电力系统新型宽频振荡问题。因此针对电力系统宽频振荡“高噪声”和“宽频带”的特点，提出一种基于变分模态分解和压缩感知的自适应宽频振荡监测方法。对变分模态分解(variational mode decomposition, VMD)方法进行改进，自适应确定模态分解数，抑制噪声分量并监测识别振荡信号的有效信息。若监测到宽频振荡，将降噪处理后的宽频振荡数据通过压缩感知(compressed sensing, CS)方法上传，在调度中心对压缩数据进行重构，精确恢复宽频振荡信号，方便调度主站后续分析处理。算例表明所提方法可在高强度随机噪声的情况下保持宽频振荡监测的质量，克服高速采样后数据传输带宽的限制，并在实际电力系统宽频振荡信号监测中有良好应用。     

等效斜视波数域算法

文中通过对等效斜视波数域算法的改进解决该算法在微波成像时存在的成像指标不一致性问题,所得到的改进算法对于大测绘带高分辨率微波成像具有一定的工程应用价值.     

基于混沌滤波器的压缩感知雷达互质压缩采样

提出一种基于混沌数字滤波器的压缩感知雷达回波信号互质压缩采样方法.首先,设计基于混沌数字滤波器的压缩采样矩阵,避免了传统高斯随机观测矩阵的硬件设计困难,在保证压缩感知雷达感知矩阵非相关特性的同时,提高了压缩采样矩阵的可实现性与可控性.其次,在混沌数字滤波器压缩观测矩阵的基础上,提出迸一步压缩数据量的互质压缩采样方法,显著降低了压缩感知雷达的数据处理量.仿真实验结果表明,基于混沌滤波器的互质压缩采样方法可以实现压缩感知雷达的在压缩处理数据量的同时获得目标参数的高精度估计,为压缩感知雷达硬件系统设计提供了新方法.     

A three‐vector‐based direct power control strategy for three‐phase voltage source PWM converters

For traditional direct power control strategy, there exist high steady‐state power ripples and large current harmonics. To solve this problem, this work proposes a novel three‐vector‐based direct power control strategy for three‐phase voltage source pulse‐width‐modulated (PWM) converters. Different from traditional predictive direct power control strategy, an improved vector table is presented and three voltage vectors are selected, which considers the impact of voltage vectors on the active and reactive power simultaneously. The performance of the three‐phase voltage source PWM converters with the proposed control strategy is investigated and compared with the predictive deadbeat direct power control strategy. Furthermore, the three‐phase voltage source PWM converters have also been tested in the condition of different loads and when voltage unbalance occurs. Simulation and experimental work are conducted. The results conclude that the proposed strategy is of simple structure and fast dynamic response. Besides, it can effectively reduce steady‐state power ripples and current harmonics, improving the performance of the three‐phase PWM converters. Copyright © 2016 John Wiley & Sons, Ltd.     

回溯自适应匹配追踪电能质量信号重构方法

为改善电能质量暂稳态信号重构性能,提出了基于压缩感知理论的回溯自适应匹配追踪的电能质量信号重构方法,对电能质量信号进行处理。在自适应匹配追踪的基础上改变一个回溯步骤,初选出满足条件的候选集,然后在终选步骤中利用回溯功能,剔除系数相对较小的原子,所以该方法能够灵活地删除一些被在初选时被错误选择的原子,有着更好的稀疏信号重构和逼近性能。另外该方法无需先验信号的稀疏度,具有稀疏度自适应能力。仿真实验结果表明,10种电能质量信号的压缩重构精度都在97.63%以上,能量恢复系数要高于99.57%,信噪比高于31.42dB,均方误差百分比在2.31%之内,都能达到很好的性能指标。     

m序列伪随机动态测试信号建模与压缩检测方法

针对智能电网中动态负荷对电能计量的影响问题,建立了m序列伪随机动态测试信号的参数模型,并分析了该测试信号的统计特性;证明了该动态测试信号的频域稀疏性,采用压缩感知理论建立了伪随机动态测试信号的压缩感知检测系统模型,采用稳态优化方法构建了压缩感知测量矩阵;在此基础上,针对m序列伪随机动态测试信号,提出了电能量值的压缩感知测量方法;仿真分析了长度为255位、511位、1023位单周期和多周期m序列动态测试信号的相对误差,误差均小于10~(-12),可忽略不计,表明所提压缩感知测量方法能够准确测量伪随机动态测试信号的电能量值。     

基于压缩感知的SAR宽带干扰抑制方法

压缩感知合成孔径雷达成像能够利用较少的观测数据清晰的恢复目标图像,但当回波中存在宽带压制干扰时,会严重破坏场景稀疏性,造成成像质量恶化。研究了一种基于选择性测量的自适应压缩感知宽带压制干扰抑制方法,通过构造一种压缩域投影滤波器并结合噪声联合检测算法,自适应感知干扰的位置信息,对合成孔径雷达（SAR）回波信号进行选择性测量,从“源头”上避免了干扰对SAR目标回波信号稀疏性的影响。仿真结果表明,该方法在减少SAR系统处理数据量的同时,使SAR成像质量明显提高,证明了该方法的有效性。     

A new method of power system fault recording based on compressed sensing

Wavelet transform is usually used to deal with the numerous data in fault recording of power systems, but it still needs to sample the signal with a high frequency first and then compress the data obtained. In order to solve the problem, this paper proposes a new fault recording method based on compressed sensing theory. The method can break the limit of Nyquist sampling theorem and acquire the fault signal with a much lower sampling frequency. It presents the theoretical framework of compressed sensing, analyzes the characteristics of the faulty waveform, and proposes a new sparsity adaptive and compressive sampling matching pursuit algorithm to reconstruct the fault signal. Finally, simulation results in MATLAB show that the proposed method performs well at different compression ratios in terms of the norm mean‐square error, signal‐to‐noise ratio, and waveform similarity. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

具有抗逆序及权重自适应的黑启动方案评估方法

逼近理想解排序法（technique for order preference by similarity to an ideal solution,TOPSIS）因方法简单易实施而应用广泛,但其存在逆序问题及无法合理地考虑指标权重问题。针对该问题,提出了具有抗逆序及权重自适应确定的黑启动方案评估新方法。首先通过引入正负极端方案及改进数据归一化方法,很好地克服了TOPSIS逆序问题的产生;进而提出指标权重的自适应确定方法,既充分利用专家的偏好,又使得客观信息得到充分利用;基于上述改进策略,提出了具有抗逆序及权重自适应确定的TOPSIS新方法并给出了具体实现步骤。河北南网黑启动算例证明了方法的有效性。     

Dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi‐population DEEPSO (MP‐DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi‐population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.     

Real‐valued MUSIC algorithm for power harmonics and interharmonics estimation

Recently, advanced spectrum estimation methods, including the MUSIC (Multiple Signal Classification) algorithms, are being gradually employed for high‐resolution power harmonics analysis. However, most of them are proposed to detect frequencies of complex‐valued signals, so that any real‐valued signal should be transformed into complex form. This data pre‐treatment may lead to additional computation burden. In addition, the picket‐fence effects also exist as in the FFT algorithm and cause poor frequency resolution. To overcome these drawbacks, a real‐valued MUSIC algorithm is proposed for power harmonics analysis in this paper. The algorithm is based on the subspace decomposition theory and the computation of pseudospectrum is also provided. Additionally, to improve the measuring precision, the Newton–Raphson algorithm is adopted to optimize the harmonic frequencies significantly. Simulation results show that, in the real‐valued MUSIC pseudospectrum, the spectral peaks of actual harmonic components can be more easily distinguished from the false peaks caused by noise, and the computational complexity is notably lower than that of the classic complex MUSIC, as well as the detecting accuracy is close to that of root‐MUSIC algorithm which is quite time consuming. Experimental results prove that the proposed strategy is more suitable for high‐resolution power harmonics estimation. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust Data Transmission upon Compressive Sensing for Smart Grid

Abstract

A huge amount of power system data are transmitted in the smart grid. If some data packets are lost during transmission, the received data quality will be affected or worsened. This article proposes a robust data transmission method upon compressive sensing technology for the smart grid. First, the original power system signals are transformed into wavelet domain coefficients by means of wavelet multi-resolution analysis. Then these coefficients are sampled by the compressive sensing algorithm. These compressive sensing sampling data are transmitted in communication channels. In the receiving end, the compressive sensing sampling data are reconstructed to obtain the wavelet domain coefficients, and then wavelet inverse transforms are applied to recover the power system signals. The compressive sensing transmission method can achieve higher rate-distortion performance compared with the direct data transmission method. The new method can be used for power system data communication in non-feedback communication systems.     

Estimation of the disaster building domain using RapidEye data to estimate the amount of disaster waste

To draw reconstruction plans following major earthquakes, such as the Great East Japan Earthquake of 2011, it is necessary to quickly estimate the amount of disaster waste. To this end, remote sensing data is used, which affects all subsequent processing. However, the digital number of each pixel of the remote sensing data represents the average land cover conditions. That is, the information provided by a pixel should be represented as a one‐pixel mixed class (‘mixel’) instead of a one‐pixel single class. In previous papers, we had proposed a method of land cover classification using Thailand Earth Observation System data of a stricken area. The land cover classification results were in good agreement with a manually classified map which referenced the geological map and aerial photograph. However, to detect collapsed buildings, a minimum 2‐m ground resolution is required. Therefore, we additionally proposed a method of land cover classification using RapidEye data. The ground resolution of RapidEye data was improved to 2‐m from 6.5‐m by the proposed method. These examination results suggested that the method is effective in the classification of land cover conditions using RapidEye data. Nevertheless, the disaster building domain was not yet estimated. In this paper, we therefore propose a method to estimate the disaster building domain using RapidEye data acquired before and after an earthquake. The effectiveness of this method is confirmed by evaluation results. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

一种新的电能质量扰动信号压缩感知识别方法

针对现有电能质量扰动信号识别方法存在数据量大、准确率不高的不足,提出了一种基于压缩感知稀疏向量特征提取的电能质量扰动信号分类识别方法。该方法首先针对原始信号,利用压缩感知理论获取降维的测量信号,并基于?1范数正交匹配追踪算法获取稀疏向量。然后针对稀疏向量提取最大值、次大值、均方根、标准差、峭度和裕度因子等特征,作为神经网络的输入,实现电能质量扰动信号的分类识别。最后,针对六类典型电能质量扰动信号,开展仿真实验验证。仿真结果表明,现有识别方法需要处理的原始信号长度为1024,而所提方法特征提取时所处理的数据长度仅有30,从而大大减少了所需处理的数据量,并且由于实现了以非常少的数据量保存原有全部有用特征信息,因而更有利于提高识别准确率。通过与广泛采用的小波变换识别方法进行比较,所提方法的平均准确率高达98.71%,远远高于小波变换方法的92.86%。