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Actuator Fault Detection of T–S Fuzzy Hypersonic Flight Vehicle Model: A T–S Fuzzy-Based H∞ Sliding Mode Observer Approach T-S模糊高超声速飞行器模型执行器故障检测:基于T-S模糊的H∞滑模观测器方法
Pub Date : 2023-08-22 DOI: 10.1109/JMASS.2023.3278654
Meijie Liu;Ping Luo;Changhua Hu;Rui Guo;Xiaoxiang Hu
A T–S fuzzy-based $Hinfty $ sliding mode observer (SMO)-based fault detection scheme is conducted to realize the actuator fault detection issue, including stuck fault detection and partial loss of effectiveness (PLOE) fault detection in our work. First, a T–S fuzzy attitude control model with an uncertainty term is derived from the original nonlinear hypersonic flight vehicle (HSV) model by combining local linear models at four equilibrium points. Second, the actuator fault model is introduced to further establish a T–S fuzzy HSV model with actuator faults. Then, a T–S fuzzy-based $Hinfty $ SMO is designed for fault detection based on matrix coordinate transformation. Finally, the SMO observer simulation is conducted to the T–S fuzzy HSV model for single-input single-style actuator fault detection. The simulation results show that stuck faults can be timely and accurately detected at the fault time and the state change amplitude is approximately in direct-ratio relation with the amplitude of stuck faults, which is caused by the implicit relationship between the states and the flap. Unfortunately, the detection of PLOE faults encounters some difficulties for acceptable reasons and needs further attention and investigation.
提出了一种基于T–S模糊的$Hinfty$滑模观测器(SMO)故障检测方案来实现执行器故障检测问题,包括我们工作中的卡住故障检测和部分失效(PLOE)故障检测。首先,将四个平衡点的局部线性模型相结合,从原始的非线性高超音速飞行器(HSV)模型出发,导出了一个带有不确定性项的T–S模糊姿态控制模型。其次,引入执行器故障模型,进一步建立了执行器故障的T–S模糊HSV模型。然后,设计了一个基于T–S模糊的$Hinfty$SMO,用于基于矩阵坐标变换的故障检测。最后,对T–S模糊HSV模型进行了SMO观测器仿真,用于单输入单型执行器故障检测。仿真结果表明,在故障时刻能够及时、准确地检测出卡滞故障,状态变化幅度与卡滞故障幅度近似成正比关系,这是由状态与襟翼之间的隐式关系引起的。不幸的是,由于可接受的原因,PLOE故障的检测遇到了一些困难,需要进一步关注和调查。
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引用次数: 0
A Novel High Dynamic Image Fusion Method via an Unsupervised End-to-End Framework 一种基于无监督端到端框架的高动态图像融合方法
Pub Date : 2023-08-14 DOI: 10.1109/JMASS.2023.3305241
Xinglin Hou;Jiayi Yan;Tao Sun;Huannan Qi;Wen Sun
For the sake of high-quality images of the high dynamic range (HDR) scenes, it is effective means to fuse the multiexposure sequences for the same HDR scene. However, the fused images using the existing fusion methods are prone to detail loss or block effect. Aiming at these problems, a novel unsupervised end-to-end framework is developed to provide solutions for the multiexposure image fusion. Instead of conventional manual setting, the optimal image weight coefficients of the multiexposure images are learned automatically, which makes this model more suitable for application. Most importantly, a customized loss function is designed to enhance the network achievement and automatically learn the parameters in the direction of optimal fusion image. According to the quantitative and qualitative results of a large number of experiments, it is demonstrated that the proposed framework performs its superiority and effectiveness compared with the state-of-the-art approaches.
为了获得高动态范围(HDR)场景的高质量图像,对同一HDR场景进行多曝光序列融合是一种有效手段。然而,现有的融合方法所得到的融合图像容易出现细节丢失或块效应。针对这些问题,提出了一种新的端到端无监督框架,为多曝光图像融合提供了解决方案。该模型代替了传统的手动设置,自动学习了多次曝光图像的最优权重系数,使其更适合应用。最重要的是,设计了自定义的损失函数来增强网络效果,并自动学习最优融合图像方向的参数。大量的定量和定性实验结果表明,与现有的方法相比,该框架具有优越性和有效性。
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引用次数: 0
An Adaptive Parameter Estimation Algorithm of Radar Linear Frequency Modulation Signal Based on Nonlinear Transform Under Different Alpha Stable Distribution Noise Environments 不同α稳定分布噪声环境下基于线性变换的雷达线性调频信号自适应参数估计算法
Pub Date : 2023-08-14 DOI: 10.1109/JMASS.2023.3304139
Yuhong Zhang;Yixin Zhang
In order to address the impact of alpha stable distribution noise in the field of parameter estimation of radar linear frequency modulation (LFM) signal, the Lv’s distribution (LVD) class algorithms have been proposed in recent works. However, they just can be applied under the single noisy environment and suffered severe performance degradation at low signal-to-noise ratios (SNRs). In this article, an adaptive nonlinear function LVD (ANF-LVD) algorithm is proposed, different from the traditional LVD algorithms, which makes full use of the geometric information of the LFM signal to adapt to different alpha stable distribution noise environments. Then, based on the geometric information of the LFM signal, an appropriate nonlinear function is selected to suppress the noise under different alpha stable distribution noise environments, which has high parameter estimation accuracy even under an extremely low SNR environment. Simulation experiments show that the proposed algorithm has stronger adaptability and higher parameter estimation accuracy than the traditional LVD algorithm under different alpha stable distribution noise environments.
为了解决α稳定分布噪声对雷达线性调频信号参数估计的影响,近年来提出了Lv分布(LVD)类算法。然而,它们只能在单一噪声环境下使用,在低信噪比(SNRs)下性能会严重下降。本文提出了一种不同于传统LVD算法的自适应非线性函数LVD (ANF-LVD)算法,该算法充分利用LFM信号的几何信息来适应不同的α稳定分布噪声环境。然后,根据LFM信号的几何信息,选择合适的非线性函数来抑制不同α稳定分布噪声环境下的噪声,即使在极低信噪比环境下也具有较高的参数估计精度。仿真实验表明,在不同的α稳定分布噪声环境下,该算法比传统LVD算法具有更强的适应性和更高的参数估计精度。
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引用次数: 0
A Novel Two-Step DInSAR Phase Unwrapping for Large Gradient Mining Deformation 针对大梯度采矿变形的新型两步 DInSAR 相位解包法
Pub Date : 2023-08-14 DOI: 10.1109/JMASS.2023.3305242
Yandong Gao;Nanshan Zheng;Shijin Li;Yansuo Zhang;Qiuzhao Zhang;Shubi Zhang
Phase unwrapping (PhU) of the large gradient deformation in the coal mining subsidence center has always been the main problem in the differential interferometric synthetic aperture radar (DInSAR) data processing. The accuracy of PhU will directly affect the deformation results of the mining subsidence center. To address this issue, in this article, we proposed a two-step PhU method which combines $L^{1}$ -norm and $L^{2}$ -norm. This method can effectively obtain the PhU results of the large gradient deformation in the mining subsidence center. First, the filtered DInSAR interferometric phase is unwrapped using $L^{2}$ -norm, and the first-step unwrapped phase is obtained. Then, the first-step unwrapped phase is rewrapped, which performs conjugate multiplication with the original interferometric phase to obtain the residual phase. Moreover, the residual phase is unwrapped by the $L^{1}$ -norm method to obtain the second-step unwrapped phase. Finally, the final unwrapped phase is obtained by summing the first-step and second-step unwrapping results. Experiments are conducted with simulated and GaoFen-3 SAR data sets. To compare against the representative PhU method, the proposed method can effectively solve the problem of PhU in the large gradient deformation of the mining areas.
采煤沉陷中心大梯度变形的相位解缠(PhU)一直是差分干涉合成孔径雷达(DInSAR)数据处理的主要问题。PhU的精度将直接影响采煤沉陷中心的变形结果。针对这一问题,本文提出了一种结合$L^{1}$正演和$L^{2}$正演的两步 PhU 方法。该方法可以有效地获得采矿沉陷中心大梯度变形的 PhU 结果。首先,利用$L^{2}$正则对滤波后的 DInSAR 干涉测量相位进行解包,得到第一步解包相位。然后,对第一步解包相位进行重包,与原始干涉相位进行共轭相乘,得到残差相位。然后,用 $L^{1}$ -norm方法对残差相位进行解包,得到第二步解包相位。最后,将第一步和第二步的解包结果相加,得到最终的解包相位。实验是通过模拟和高分三号合成孔径雷达数据集进行的。与具有代表性的 PhU 方法相比,所提出的方法能有效解决矿区大梯度变形中的 PhU 问题。
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引用次数: 0
Electronics Design and Testing of the KREPE Atmospheric Entry Capsule Avionics KREPE入气舱航空电子设备的电子设计与测试
Pub Date : 2023-08-08 DOI: 10.1109/JMASS.2023.3303042
Matthew P. Ruffner;John D. Schmidt;Isaac S. Rowe;Ryan D. Nolin;William Smith;Alexandre Martin
Atmospheric entry flight tests are one of the best ways to evaluate the performance of new thermal protective materials, however, at full scale, they are infrequent and expensive. The Kentucky re-entry universal payload system (KRUPS) provides a low-cost solution for such evaluative missions. This work concerns electronics design, firmware implementation, and hardware integration performed for the most recent mission: Kentucky re-entry probe experiment (KREPE). KREPE avionics and electrical hardware were designed to meet operational, environmental, and safety requirements imposed by the ISS and Northrop Grumman (NG), as well as physical constraints due to capsule size. KREPE system firmware was designed to meet the communication uncertainties and operational constraints of a re-entry mission while maximizing the amount of scientific data produced by each capsule. Functional verification and environmental certification prior to the mission indicated that all three capsules would function as expected and all three were delivered to the ISS aboard the NG resupply vehicle NG-16. The mission was a success and three KREPE capsules de-orbited into the South Pacific Ocean on December 2021, transmitting back heating data from two capsules. The success of the two capsules verified the electrical hardware design, software implementation, and build workmanship. Receiving in-flight heating data is of importance for materials modeling to further validate their computational models.
进入大气层的飞行试验是评估新型热防护材料性能的最佳方法之一,然而,在全尺寸的情况下,这种试验很少而且昂贵。肯塔基再入通用有效载荷系统(KRUPS)为这种评估任务提供了一种低成本的解决方案。这项工作涉及电子设计、固件实现和硬件集成,用于最近的任务:肯塔基再入探测实验(KREPE)。KREPE航空电子设备和电气硬件的设计满足了国际空间站和诺斯罗普·格鲁曼公司(NG)施加的操作、环境和安全要求,以及由于太空舱尺寸的物理限制。KREPE系统固件的设计是为了满足再入任务的通信不确定性和操作限制,同时最大限度地提高每个太空舱产生的科学数据量。任务前的功能验证和环境认证表明,所有三个太空舱都将按预期运行,并且所有三个太空舱都由NG-16号补给车运送到国际空间站。这次任务取得了成功,2021年12月,三个KREPE太空舱脱离轨道进入南太平洋,从两个太空舱传回了加热数据。两个胶囊的成功验证了电气硬件设计,软件实现和构建工艺。接收飞行中加热数据对于材料建模进一步验证其计算模型具有重要意义。
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引用次数: 0
A Novel Non-Local Denoising Filter Based on Multibaseline InSAR 一种基于多基线InSAR的非局部去噪滤波器
Pub Date : 2023-08-02 DOI: 10.1109/JMASS.2023.3301216
Xue Li;Taoli Yang
Denoising filtering is one of the most critical steps in interferometric synthetic aperture radar (InSAR) data processing. There are many denoising filtering algorithms, which are suitable for different specific scenarios. However, there is a contradiction between detail retaining and noise reduction at the same time, especially for areas with large terrain fluctuations. In order to solve such a contradiction, an improved nonlocal denoising filtering algorithm based on the multibaseline InSAR is proposed in this article. Based on the relationship between interferometric phases with the multiple baselines, we calculated the joint probability by a nonlocal probability density function (PDF) to effectively preserve fringes, especially for the interferogram with a large baseline. Combined with the PDF obtained by machine learning, we got more satisfactory results with better continuity of fringes and the details of the interferograms as well as maximizing noise reduction.
噪声滤波是干涉合成孔径雷达(InSAR)数据处理的关键步骤之一。有许多去噪滤波算法,它们适用于不同的具体场景。然而,细节保留与降噪同时存在矛盾,特别是对于地形波动较大的地区。为了解决这一矛盾,本文提出了一种改进的基于多基线InSAR的非局部去噪滤波算法。根据干涉相位与多个基线的关系,采用非局部概率密度函数(PDF)计算联合概率,以有效地保留条纹,特别是对于大基线干涉图。结合机器学习得到的PDF,我们得到了更令人满意的结果,条纹和干涉图细节的连续性更好,并且最大限度地降低了噪声。
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引用次数: 0
HRSF-Net: A High-Resolution Strong Fusion Network for Pixel-Level Classification of the Thin-Stripped Target for Remote Sensing System HRSF-NetNet:遥感系统薄剥离目标像素级分类的高分辨率强融合网络
Pub Date : 2023-07-27 DOI: 10.1109/JMASS.2023.3299330
Lifan Zhou;Wenjie Xing;Jie Zhu;Yu Xia;Shan Zhong;Shengrong Gong
High-resolution pixel-level classification of the roads and rivers in the remote sensing system has extremely important application value and has been a research focus which is received extensive attention from the remote sensing society. In recent years, deep convolutional neural networks (DCNNs) have been used in the pixel-level classification of remote sensing images, which has shown extraordinary performance. However, the traditional DCNNs mostly produce discontinuous and incomplete pixel-level classification results when dealing with thin-stripped roads and rivers. To solve the above problem, we put forward a high-resolution strong fusion network (abbreviated as HRSF-Net) which can keep the feature map at high resolution and minimize the texture information loss of the thin-stripped target caused by multiple downsampling operations. In addition, a pixel relationship enhancement and dual-channel attention (PRE-DCA) module is proposed to fully explore the strong correlation between the thin-stripped target pixels, and a hetero-resolution fusion (HRF) module is also proposed to better fuse the feature maps with different resolutions. The proposed HRSF-Net is examined on the two public remote sensing datasets. The ablation experimental result verifies the effectiveness of each module of the HRSF-Net. The comparative experimental result shows that the HRSF-Net has achieved mIoU of 79.05% and 64.46% on the two datasets, respectively, which both outperform some advanced pixel-level classification methods.
遥感系统中道路和河流的高分辨率像素级分类具有极其重要的应用价值,一直是遥感界广泛关注的研究热点。近年来,深度卷积神经网络(deep convolutional neural network, DCNNs)被应用于遥感图像的像素级分类,并显示出非凡的性能。然而,传统的DCNNs在处理薄条路面和河流时,大多产生不连续和不完整的像素级分类结果。为了解决上述问题,我们提出了一种高分辨率强融合网络(简称HRSF-Net),该网络既能保持特征图的高分辨率,又能最大限度地减少多次降采样操作造成的薄剥离目标纹理信息损失。此外,提出了像素关系增强和双通道关注(PRE-DCA)模块,以充分挖掘薄剥离目标像素之间的强相关性,并提出了异分辨率融合(HRF)模块,以更好地融合不同分辨率的特征图。在两个公共遥感数据集上对所提出的HRSF-Net进行了检验。烧蚀实验结果验证了HRSF-Net各模块的有效性。对比实验结果表明,HRSF-Net在两个数据集上的mIoU分别达到79.05%和64.46%,均优于一些先进的像素级分类方法。
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引用次数: 0
Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction 基于HOG特征提取的双通道雷达信号识别
Pub Date : 2023-07-26 DOI: 10.1109/JMASS.2023.3299159
Zeyu Tang;Daying Quan;Xiaofeng Wang;Ning Jin;Dongping Zhang
Objectives: To improve the recognition accuracy of radar signals under a low signal-to-noise ratio (SNR). Technology or Method: We propose a novel radar signal recognition method based on a dual-channel model with the histogram of oriented gradients (HOG) feature extraction. Specifically, multisynchrosqueezing transform (MSST) and Choi–Williams distribution (CWD) transform are adopted individually to obtain the time–frequency distribution images of radar signals, and HOG feature extraction is performed on the preprocessed time–frequency images of each channel, respectively. Then, the features of the two channels are fused and dimensionally reduced by the principal component analysis (PCA). Finally, the compact feature parameters are fed to the support vector machine (SVM) classifier to identify radar signals. Clinical or Biological Impact: The experimental results demonstrate that the proposed model achieves a high recognition performance with a small computational complexity, especially in low SNR. When the SNR is −12 dB, the recognition accuracy can reach more than 92%, which is over 6% higher than that of single-channel models and related convolutional neural network-based models.
目的:提高低信噪比条件下雷达信号的识别精度。技术或方法:提出了一种基于定向梯度直方图(HOG)特征提取的双通道雷达信号识别方法。具体而言,分别采用多同步压缩变换(MSST)和Choi-Williams分布(CWD)变换获得雷达信号时频分布图像,并分别对各通道预处理后的时频图像进行HOG特征提取。然后,通过主成分分析(PCA)对两个信道的特征进行融合和降维。最后,将压缩后的特征参数输入到支持向量机(SVM)分类器中进行雷达信号识别。临床或生物学影响:实验结果表明,该模型在计算复杂度较小的情况下具有较高的识别性能,特别是在低信噪比的情况下。当信噪比为−12 dB时,识别准确率可达92%以上,比单通道模型和相关的基于卷积神经网络的模型提高6%以上。
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引用次数: 0
Sliding Mode Controller Applied to Autonomous UAV Operation in Marine Small Cargo Transport 滑模控制器在自主无人机海上小货运输中的应用
Pub Date : 2023-07-18 DOI: 10.1109/JMASS.2023.3296433
Guilherme F. Carvalho;Fabio A. A. Andrade;Gabryel S. Ramos;Alessandro R. L. Zachi;Ana L. F. de Barros;Milena F. Pinto
Unmanned aerial vehicles (UAVs) have been used in different applications due to their flexibility in maneuvering and performing missions. However, they can face external disturbances, such as wind, which can cause physical instability of the platform. Usually, UAVs commonly use a classical PID controller due to their simple structure and less dependence on the model. However, this classical controller requires expertise from the operator to adjust the parameters when dealing with nonlinearities. Therefore, this work proposes the integration of a slide mode control (SMC) controller into a PX4 flight control unit (FCU) and combining it with computer vision techniques and sensor data fusion to enable autonomous UAV offshore cargo tasks for the Oil & Gas sector. The controller was evaluated in a software in the loop (SITL) simulation performed in the robot operating system (ROS), demonstrating its robustness and potential for small marine cargo transportation using UAVs.
无人驾驶飞行器(uav)由于其机动和执行任务的灵活性而被用于不同的应用领域。然而,它们可能面临外部干扰,如风,这可能导致平台的物理不稳定。由于传统的PID控制器结构简单,对模型的依赖性较小,因此无人机通常采用经典的PID控制器。然而,这种经典控制器在处理非线性时需要操作员的专业知识来调整参数。因此,这项工作提出将滑模控制(SMC)控制器集成到PX4飞行控制单元(FCU)中,并将其与计算机视觉技术和传感器数据融合相结合,以实现石油和天然气部门的自主无人机海上货物任务。在机器人操作系统(ROS)中进行的软件在环(SITL)仿真中对该控制器进行了评估,证明了其鲁棒性和使用无人机进行小型海上货物运输的潜力。
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引用次数: 0
A Modeling and Computational Analysis Method for Multichip DDR Microsystem 多芯片DDR微系统封装建模与计算分析方法
Pub Date : 2023-07-11 DOI: 10.1109/JMASS.2023.3293861
Bo Wen;Guoyao Xiao;Zongzheng Sun;Guisheng Liao;Fei Xie;Yinghui Quan
The miniaturization of memory systems is of great significance to the miniaturization of aerospace electronic systems, and double data rate (DDR) memory is prone to serious signal integrity (SI) problems due to its high-frequency and high-speed characteristics. Eye simulation analysis is often time-consuming and does not provide insightful guidance for link optimization and requires further circuit modeling and mathematical analysis. Based on a multichip DDR microsystem design, this article proposes a circuit model of links under different topologies by taking a representative multilevel bonding interconnection structure as an example and establishes a mathematical model of DDR received signal through theoretical calculation. At the same time, we summarize the quantitative relationship between the bonding wire parameters and the related SI problems by substituting the actual circuit parameters into the mathematical model formula. Finally, the theoretical analysis results and simulation results are compared and verified through circuit simulation, and the error is analyzed. The results show that the circuit model and theoretical analysis method can quantitatively analyze the SI problem from a mathematical perspective within a certain error range, and the method and conclusion can be used to guide the early design and later optimization of the DDR memory microsystem.
存储系统的小型化对航空航天电子系统的小型化具有重要意义,双数据速率(DDR)存储器由于其高频、高速的特性,容易出现严重的信号完整性问题。眼动仿真分析往往耗时,不能为链路优化提供有见地的指导,需要进一步的电路建模和数学分析。本文以多芯片DDR微系统设计为基础,以具有代表性的多层键合互连结构为例,提出了不同拓扑下链路的电路模型,并通过理论计算建立了DDR接收信号的数学模型。同时,将实际电路参数代入数学模型公式,总结了键合线参数与相关SI问题之间的定量关系。最后,通过电路仿真对理论分析结果与仿真结果进行了对比验证,并对误差进行了分析。结果表明,电路模型和理论分析方法可以在一定误差范围内从数学角度定量分析SI问题,该方法和结论可用于指导DDR存储微系统的前期设计和后期优化。
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引用次数: 0
期刊
IEEE Journal on Miniaturization for Air and Space Systems
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