Pub Date : 2023-08-22DOI: 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.
{"title":"Actuator Fault Detection of T–S Fuzzy Hypersonic Flight Vehicle Model: A T–S Fuzzy-Based H∞ Sliding Mode Observer Approach","authors":"Meijie Liu;Ping Luo;Changhua Hu;Rui Guo;Xiaoxiang Hu","doi":"10.1109/JMASS.2023.3278654","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3278654","url":null,"abstract":"A T–S fuzzy-based \u0000<inline-formula> <tex-math>$Hinfty $ </tex-math></inline-formula>\u0000 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 \u0000<inline-formula> <tex-math>$Hinfty $ </tex-math></inline-formula>\u0000 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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"274-282"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 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.
{"title":"A Novel High Dynamic Image Fusion Method via an Unsupervised End-to-End Framework","authors":"Xinglin Hou;Jiayi Yan;Tao Sun;Huannan Qi;Wen Sun","doi":"10.1109/JMASS.2023.3305241","DOIUrl":"10.1109/JMASS.2023.3305241","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"400-407"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77454037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 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.
{"title":"An Adaptive Parameter Estimation Algorithm of Radar Linear Frequency Modulation Signal Based on Nonlinear Transform Under Different Alpha Stable Distribution Noise Environments","authors":"Yuhong Zhang;Yixin Zhang","doi":"10.1109/JMASS.2023.3304139","DOIUrl":"10.1109/JMASS.2023.3304139","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"389-399"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79153719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A Novel Two-Step DInSAR Phase Unwrapping for Large Gradient Mining Deformation","authors":"Yandong Gao;Nanshan Zheng;Shijin Li;Yansuo Zhang;Qiuzhao Zhang;Shubi Zhang","doi":"10.1109/JMASS.2023.3305242","DOIUrl":"10.1109/JMASS.2023.3305242","url":null,"abstract":"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 \u0000<inline-formula> <tex-math>$L^{1}$ </tex-math></inline-formula>\u0000-norm and \u0000<inline-formula> <tex-math>$L^{2}$ </tex-math></inline-formula>\u0000-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 \u0000<inline-formula> <tex-math>$L^{2}$ </tex-math></inline-formula>\u0000-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 \u0000<inline-formula> <tex-math>$L^{1}$ </tex-math></inline-formula>\u0000-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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 1","pages":"2-8"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76317421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 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.
{"title":"Electronics Design and Testing of the KREPE Atmospheric Entry Capsule Avionics","authors":"Matthew P. Ruffner;John D. Schmidt;Isaac S. Rowe;Ryan D. Nolin;William Smith;Alexandre Martin","doi":"10.1109/JMASS.2023.3303042","DOIUrl":"10.1109/JMASS.2023.3303042","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"381-388"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86583918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-02DOI: 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.
{"title":"A Novel Non-Local Denoising Filter Based on Multibaseline InSAR","authors":"Xue Li;Taoli Yang","doi":"10.1109/JMASS.2023.3301216","DOIUrl":"10.1109/JMASS.2023.3301216","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"376-380"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77969384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 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.
{"title":"HRSF-Net: A High-Resolution Strong Fusion Network for Pixel-Level Classification of the Thin-Stripped Target for Remote Sensing System","authors":"Lifan Zhou;Wenjie Xing;Jie Zhu;Yu Xia;Shan Zhong;Shengrong Gong","doi":"10.1109/JMASS.2023.3299330","DOIUrl":"10.1109/JMASS.2023.3299330","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"368-375"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74977486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction","authors":"Zeyu Tang;Daying Quan;Xiaofeng Wang;Ning Jin;Dongping Zhang","doi":"10.1109/JMASS.2023.3299159","DOIUrl":"10.1109/JMASS.2023.3299159","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"358-367"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10195159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76093604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-18DOI: 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.
{"title":"Sliding Mode Controller Applied to Autonomous UAV Operation in Marine Small Cargo Transport","authors":"Guilherme F. Carvalho;Fabio A. A. Andrade;Gabryel S. Ramos;Alessandro R. L. Zachi;Ana L. F. de Barros;Milena F. Pinto","doi":"10.1109/JMASS.2023.3296433","DOIUrl":"10.1109/JMASS.2023.3296433","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"345-357"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10185965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82649124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-11DOI: 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.
{"title":"A Modeling and Computational Analysis Method for Multichip DDR Microsystem","authors":"Bo Wen;Guoyao Xiao;Zongzheng Sun;Guisheng Liao;Fei Xie;Yinghui Quan","doi":"10.1109/JMASS.2023.3293861","DOIUrl":"10.1109/JMASS.2023.3293861","url":null,"abstract":"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.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"336-344"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72972464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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