 Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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- The Journal of Miniaturized Air and Space Systems
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Pages: C2 - C2
Abstract: null
PubDate:
TUE, 28 NOV 2023 09:22:11 -04
Issue No: Vol. 4, No. 4 (2023)
- An On-Board Imaging Processing Algorithm for Stripmap Mode of Azimuth
Multichannel Spaceborne SAR-
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Authors:
Yanbin Liu;Dongxu Chen;Wenjie Xing;Xuan Zhou;Guang-Cai Sun;Jiarong Xiao;Yue Cao;Shuai Jiang;Shuchen Guo;Zhongjun Yu;Mengdao Xing;
Pages: 330 - 335
Abstract: In the traditional processing methods of azimuth multichannel spaceborne synthetic aperture radar (SAR), the azimuth spectrum reconstruction and subsequent azimuth focusing are always via full-aperture processing. However, if the multichannel full-aperture echo data are stored on the satellite, and then the full-aperture algorithms are used for the on-board imaging processing, the huge amount of echo data will require more on-board storage resources and computing resources, and the imaging processing time will become longer. To solve the above problems, a novel on-board imaging processing algorithm via the idea that the data acquisition and the on-board imaging processing of the subaperture data are carried out simultaneously is proposed in this article. In the algorithm, the azimuth spectrum ambiguity is eliminated by the subaperture azimuth spectrum reconstruction. Then, the range cell migration correction (RCMC) and the range compression for the unambiguous subaperture signals are accomplished by the chirp scaling algorithm (CSA). After that, the low-resolution subaperture images are got via the subaperture focusing. By coherently combining all subaperture images, the final result with high resolution of all echo data can be obtained. Finally, the simulation for the point targets is given to verify the effectiveness of the proposed algorithm.
PubDate:
MON, 22 MAY 2023 10:03:37 -04
Issue No: Vol. 4, No. 4 (2023)
- A Modeling and Computational Analysis Method for Multichip DDR Microsystem
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Authors:
Bo Wen;Guoyao Xiao;Zongzheng Sun;Guisheng Liao;Fei Xie;Yinghui Quan;
Pages: 336 - 344
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.
PubDate:
TUE, 11 JUL 2023 10:01:43 -04
Issue No: Vol. 4, No. 4 (2023)
- Sliding Mode Controller Applied to Autonomous UAV Operation in Marine
Small Cargo Transport-
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Authors:
Guilherme F. Carvalho;Fabio A. A. Andrade;Gabryel S. Ramos;Alessandro R. L. Zachi;Ana L. F. de Barros;Milena F. Pinto;
Pages: 345 - 357
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.
PubDate:
TUE, 18 JUL 2023 10:02:33 -04
Issue No: Vol. 4, No. 4 (2023)
- Radar Signal Recognition Based on Dual-Channel Model With HOG Feature
Extraction-
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Authors:
Zeyu Tang;Daying Quan;Xiaofeng Wang;Ning Jin;Dongping Zhang;
Pages: 358 - 367
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.
PubDate:
WED, 26 JUL 2023 10:01:58 -04
Issue No: Vol. 4, No. 4 (2023)
- HRSF-Net: A High-Resolution Strong Fusion Network for Pixel-Level
Classification of the Thin-Stripped Target for Remote Sensing System-
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Authors:
Lifan Zhou;Wenjie Xing;Jie Zhu;Yu Xia;Shan Zhong;Shengrong Gong;
Pages: 368 - 375
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.
PubDate:
THU, 27 JUL 2023 10:02:35 -04
Issue No: Vol. 4, No. 4 (2023)
- A Novel Non-Local Denoising Filter Based on Multibaseline InSAR
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Authors:
Xue Li;Taoli Yang;
Pages: 376 - 380
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.
PubDate:
WED, 02 AUG 2023 10:03:48 -04
Issue No: Vol. 4, No. 4 (2023)
- Electronics Design and Testing of the KREPE Atmospheric Entry Capsule
Avionics-
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Authors:
Matthew P. Ruffner;John D. Schmidt;Isaac S. Rowe;Ryan D. Nolin;William Smith;Alexandre Martin;
Pages: 381 - 388
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.
PubDate:
TUE, 08 AUG 2023 10:02:36 -04
Issue No: Vol. 4, No. 4 (2023)
- An Adaptive Parameter Estimation Algorithm of Radar Linear Frequency
Modulation Signal Based on Nonlinear Transform Under Different Alpha
Stable Distribution Noise Environments-
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Authors:
Yuhong Zhang;Yixin Zhang;
Pages: 389 - 399
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.
PubDate:
MON, 14 AUG 2023 10:03:24 -04
Issue No: Vol. 4, No. 4 (2023)
- A Novel High Dynamic Image Fusion Method via an Unsupervised End-to-End
Framework-
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Authors:
Xinglin Hou;Jiayi Yan;Tao Sun;Huannan Qi;Wen Sun;
Pages: 400 - 407
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.
PubDate:
MON, 14 AUG 2023 10:03:24 -04
Issue No: Vol. 4, No. 4 (2023)
- Single-Length and On-Wafer Probe-Based Broadband and Rapid
Characterization of Substrate Dielectric Constant for Aerospace
Applications-
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Authors:
Longzhu Cai;Xin Xu;Gang Xu;
Pages: 408 - 415
Abstract: The space environment can exert an influence on the dielectric properties of dielectric substrates, and potential alterations in substrate dielectric constant could significantly impact the performance and reliability of spaceborne devices and systems, which might lead to mission failure. This work presents a technique for rapid and broadband characterization of substrate dielectric constant by performing only a single measurement of a single transmission line based on the ground–signal–ground (GSG) on-wafer probe for aerospace applications. Another extraction technique by the use of welding microwave connector is also discussed for comparison. Unlike previously reported techniques that require two or more transmission lines and welding connectors, our method owns the merits of avoiding connector repeatability and additional parasitic elements, easy and fast to implement without prior knowledge of substrate dielectric constant, low analysis complexity, less fabrication efforts, and being applicable to most dielectric substrates. This study offers valuable insights for airborne and spaceborne platforms with limited space, simultaneously mitigating costs and complexity, rendering it an appealing proposition for aerospace applications.
PubDate:
WED, 13 SEP 2023 10:01:53 -04
Issue No: Vol. 4, No. 4 (2023)
- Pan Evaporation Prediction Using LSTM Models Based on PCA Factor Reduction
and Firefly Optimization Algorithm-
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Authors:
Chuanli Wang;Tianyu Li;Dongjun Xin;Qian Wang;Ran Chen;Chaoyi Cao;
Pages: 416 - 422
Abstract: Evaporation is an important part of the moisture exchange between the earth and the air. Understanding the trend of pan evaporation can help to reveal the status of actual evaporation, which is very useful for the allocation of regional water resources. However, long short-term memory (LSTM) has become a mainstream algorithm for predicting pan evaporation, there are two issues worth considering. One of the issues is how to automatically find the optimal hyperparameters, the other is how to eliminate the correlation between prediction factors to improve prediction performance. To address the two issues, this article proposes LSTM models based on principal component analysis (PCA) factor reduction and firefly optimization algorithm. In the proposed model, fire-fly algorithm can find the optimal hyperparameters, and PCA can eliminate the correlation between prediction factors. Xiangjiang River Basin, an important Basin for China’s water resource management, is selected as a study area, the experimental results are evaluated by root mean square error (RMSE) and the coefficient of determination ( $R^{2}$ ). The results show that the proposed models can successfully predict daily pan evaporation of the study area.
PubDate:
TUE, 26 SEP 2023 09:16:55 -04
Issue No: Vol. 4, No. 4 (2023)
- Reinforcement Learning-Based 3-D Sliding Mode Interception Guidance via
Proximal Policy Optimization-
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Authors:
Jianguo Guo;Mengxuan Li;Zongyi Guo;Zhiyong She;
Pages: 423 - 430
Abstract: This article proposes a novel 3-D sliding mode interception guidance law for maneuvering targets, which explores the potential of reinforcement learning (RL) techniques to enhance guidance accuracy and reduce chattering. The guidance problem of intercepting maneuvering targets is abstracted into a Markov decision process whose reward function is established to estimate the off-target amount and line-of-sight angular rate chattering. Importantly, a design framework of reward function suitable for general guidance problems based on RL can be proposed. Then, the proximal policy optimization algorithm with a satisfactory training performance is introduced to learn an action policy which represents the observed engagements states to sliding mode interception guidance. Finally, numerical simulations and comparisons are conducted to demonstrate the effectiveness of the proposed guidance law.
PubDate:
TUE, 17 OCT 2023 09:17:51 -04
Issue No: Vol. 4, No. 4 (2023)
- Integrated Convolution Network for ISAR Imaging and Target Recognition
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Authors:
Haoze Du;Peishuang Ni;Jianlai Chen;Shuai Ma;Hui Zhang;Gang Xu;
Pages: 431 - 437
Abstract: Recently, inverse synthetic aperture radar (ISAR) image recognition using deep learning (DL) technology is developed rapidly. However, the imaging and recognition processing is independent of each other, and the recognition network cannot fully capture target features from the radar data. Accordingly, this article proposes an integrated convolution network for ISAR imaging and target recognition, named IITR-Net. In the scheme, a DL imaging module is designed for ISAR imaging instead of using the traditional imaging algorithms, which can be cascaded with the recognition network. Thus, the proposed IITR-Net can realize the end-to-end training using the echo data as input. Moreover, the joint backpropagation process is derived for learnable parameters of the imaging module. In the experimental analysis, the proposed IITR-Net can achieve higher classification accuracy than current recognition frameworks. It implies that the IITR-Net can learn more deep features of the target, which improves the performance of recognition.
PubDate:
WED, 18 OCT 2023 09:17:36 -04
Issue No: Vol. 4, No. 4 (2023)
- 2023 Index IEEE Journal on Miniaturization for Air and Space Systems Vol.
4-
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Pages: 438 - 449
Abstract: null
PubDate:
MON, 04 DEC 2023 09:18:11 -04
Issue No: Vol. 4, No. 4 (2023)
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Hybrid journal (It can contain Open Access articles)
