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- The Journal of Miniaturized Air and Space Systems
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Pages: C2 - C2
Abstract: null
PubDate:
THU, 22 AUG 2024 09:16:55 -04
Issue No: Vol. 5, No. 3 (2024)
- Decision-Making Method of Multi-UAV Cooperate Air Combat Under Uncertain
Environment-
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Authors:
Jialong Jian;Yong Chen;Qiuni Li;Hongbo Li;Xiaokang Zheng;Chongchong Han;
Pages: 138 - 148
Abstract: Multi-UAV cooperative air combat has attracted wide attention from relative scholars. However, the decision-making problem of UAV swarm confrontation under uncertain conditions makes it more difficult. In this article, a two-layer decision-making method, containing dynamic target assignment and distributed Monte Carlo tree search (MCTS), is proposed to address this issue. Additionally, the possibility degree function method of interval gray number is combined with a genetic algorithm to deal with uncertain information in an air combat environment. Specifically, considering the actual air combat scene, the target value factor is introduced in the target allocation process, and the dynamic target allocation mechanism is established to adjust the cluster combat strategy in real time. The experiments show that the proposed two-level decision-making method can effectively deal with the swarm air combat problem under uncertain environments. First, the improved genetic algorithm can solve the problem of target allocation in an uncertain environment and give the target allocation scheme in the current state. Moreover, the establishment of the dynamic target allocation mechanism makes the cooperative behavior of UAVs emerge in the swarm, which fully reflects the adversarial air combat.
PubDate:
MON, 18 MAR 2024 09:43:41 -04
Issue No: Vol. 5, No. 3 (2024)
- Mining-Related Subsidence Measurements Using a Robust Multitemporal InSAR
Method and Logistic Model-
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Authors:
Peifeng Ma;Chang Yu;Zherong Wu;Zhanze Wang;Jiehong Chen;
Pages: 149 - 155
Abstract: Ground subsidence is a representative geohazard in mining areas that threatens human safety and infrastructure. Interferometric synthetic aperture radar (InSAR) was used to measure ground subsidence related to mining activities. However, mining areas are often subjected to severe temporal and geometric decorrelation problems, resulting in sparse persistent scatterers (PSs) and lower measurement accuracy. To improve deformation measurements, a robust multitemporal InSAR (MT-InSAR) method that jointly detects PS and distributed scatterers (DSs) in a two-tier network was utilized here. To solve the mismatch in the traditional linear velocity model, a logistic model was introduced for MT-InSAR processing. Forty-four Sentinel-1A SAR images acquired between 1 January 2020 and 30 June 2021 were used to measure ground subsidence in Zhoutaizi Village, Chengde City, Hebei Province, China, which endured geohazards induced and exacerbated by mining activities. We observed that more measurement points were produced using the logistic model (11 607) compared with the constant velocity model (10 980) in the mining areas with an increase of 5.7%, while the mean value of the standard deviation of the estimated residuals reduced from 1.45 to 1.13 with a decrease of 22%. Results are beneficial for geohazard assessment and management in mining areas.
PubDate:
WED, 27 MAR 2024 09:17:28 -04
Issue No: Vol. 5, No. 3 (2024)
- Multibaseline Phase Unwrapping With a Refined Parametric Pure Integer
Programming for Noise Suppression-
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Authors:
Jiawei Yue;Qihuan Huang;Hui Liu;Ziqi He;Hanwen Zhang;
Pages: 156 - 164
Abstract: Multibaseline phase unwrapping (MBPU) is a key procedure of interferometric synthetic aperture radar (InSAR). However, phase noise is a factor still challenging the MBPU accuracy. This article presents a refined pure integer programming (RPIP)-based MBPU method. In this method, a new parameter is introduced through considering the statistical information of the interferometric phase, which is adopted to improve the tolerance of phase noise. We also provide an effective path for searching of the ambiguity set. Theoretical analysis and experimental results show that, compared with the PIP method, unwrapping errors of the RPIP method is reduced by 60%.
PubDate:
THU, 04 APR 2024 09:22:27 -04
Issue No: Vol. 5, No. 3 (2024)
- Design of Active V-Dipole Antenna on UAS for Receiving NOAA Polar
Satellite Imagery-
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Authors:
Curtis Manore;Alan J. Fenn;Hanumant Singh;
Pages: 165 - 174
Abstract: In suboptimal environments for satellite reception, an unmanned aerial system (UAS) can navigate to a higher vantage point to receive better quality satellite broadcasts. Small UAS platforms are constrained by weight and size, making VHF antenna implementation difficult for satellite reception onboard a UAS. This research designs, simulates, and implements a small form factor V-dipole antenna with matching circuit and low-noise amplifiers to receive high-quality National Oceanic and Atmospheric Administration (NOAA) satellite imagery and weather data from a custom DJI Matrice 100 UAS platform. A software-defined radio was used to filter and demodulate VHF satellite signals, and an Nvidia TX2-embedded computer processed the satellite images onboard the UAS. Performance was evaluated by the quality of the image reception and practicality of the antenna design in flight.
PubDate:
TUE, 16 APR 2024 09:16:24 -04
Issue No: Vol. 5, No. 3 (2024)
- Discrete-Time Estimation/Approximation-Avoidance Control With Prescribed
Performance-
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Authors:
Xiangwei Bu;Ruining Luo;Humin Lei;
Pages: 175 - 181
Abstract: We address the problem of tracking control for uncertain discrete-time systems with unknown and unavailable plant dynamics, aiming to achieve prescribed performance within a preset convergence time for tracking errors. Our proposed control protocol is independent of the knowledge of system dynamics or the utilization of approximators/estimators. Instead, we employ transformed errors to develop novel nonlinear functions for control feedback. Consequently, we establish a new estimation/approximation-free indirect stabilization framework that serves as a standard paradigm for discrete-time prescribed performance control synthesis. Finally, simulation results applied to the missile seeker stabilized platform demonstrate the effectiveness of our approach.
PubDate:
FRI, 03 MAY 2024 09:19:11 -04
Issue No: Vol. 5, No. 3 (2024)
- Attitude Determination and Control in Small Satellites: A Review
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Authors:
Mariana Londoño Orozco;Belarmino Segura Giraldo;
Pages: 182 - 186
Abstract: Small satellites are becoming a significant part of the space industry and educational field. Small satellite development has increased significantly during the past decades due to their low-cost development and construction facility. One of the essential parts of a satellite is the attitude determination and control system (ADCS) which dictates and controls the orientation of the satellite in space and makes the control maneuver. Still, it is also one of the systems that present more issues and that can cause a mission failure. For developing an ADCS, simulation and testing are important before implementation. This article reviews the approaches for small satellite dynamics, types of control that can be implemented in small satellites, and the devices that can be used in the ADCS, mentioning the advantages and disadvantages. Explanations about classical and modern control algorithms that are currently used for small satellites are presented to show the latest advances in the field.
PubDate:
MON, 20 MAY 2024 09:16:35 -04
Issue No: Vol. 5, No. 3 (2024)
- Enhanced Low-Rank Matrix Decomposition for High-Resolution UAV-SAR Imagery
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Authors:
Bin Gao;Anna Song;Hanwen Xu;Zenan Zhang;Wenhui Lian;Lei Yang;
Pages: 187 - 199
Abstract: Low-rank matrix decomposition is effective for sparse recovery. However, the conventions are limited in accuracy for high-resolution synthetic aperture radar (SAR) imagery due to the shrinkage effect in the cost function, which leads to biased estimates. To this end, an enhanced-low rank matrix decomposition (E-LRMD) SAR imaging algorithm is proposed, which employs a factor group-sparse regularization (FGSR) to approximate the intended cost function, so that the low-rank features can be represented. Since, the constructed regularization function is factorized, the singular value decomposition is avoided, and the computational burden can be reduced accordingly. Furthermore, $\ell _{1}$ -norm is incorporated to encode the sparse feature. To incorporate with the enhancement of multiple features, the alternating direction method of multipliers (ADMM) framework is utilized. Therefore, both the low-rank and sparse features can be accurately recovered and enhanced, cooperatively, where the error propagation between the enhancement of multiple features is minimized. In the experiments, the effectiveness and robustness of the algorithm are verified by the simulated data and practical UAV-SAR data, respectively. Also, a phase transition diagram (PTD) experiment is carried out to analyse the advantages of the proposed algorithm in terms of quantitative aspects compared with the conventional methods.
PubDate:
WED, 29 MAY 2024 09:16:35 -04
Issue No: Vol. 5, No. 3 (2024)
- Flight Conflict Resolution Simulation Study Based on the Improved Fruit
Fly Optimization Algorithm-
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Authors:
Yulong Sun;Guoshen Ding;Yandong Zhao;Renchi Zhang;Wenjun Wang;
Pages: 200 - 209
Abstract: Due to the increasingly widespread application of unmanned aerial vehicle (UAV), the study of flight conflict resolution can effectively avoid the collision of different UAVs. First, describe flight conflict resolution as an optimization problem. Second, the improved fruit fly optimization algorithm (IFOA) is proposed. The smell concentration judgment is equal to the coordinate instead of the reciprocal of the distance in order to make the variable accessible to be negative and occur with equal probability in the defined domain. Next, introduce the limited number of searches of the Artificial Bee Colony Algorithm to avoid falling into the local optimum. Meanwhile, generate a direction and distance of the fruit fly individual through roulette. Finally, the effectiveness of the algorithm is demonstrated by computational experiments on 18 benchmark functions and the simulation of the flight conflict resolution of two and four UAVs. The results show that compared with the standard fruit fly optimization algorithm, the IFOA has superior global convergence ability and effectively reduces the delay distance, which has important potential in flight conflict resolution.
PubDate:
WED, 17 JUL 2024 09:16:35 -04
Issue No: Vol. 5, No. 3 (2024)
- Above Ground Level Estimation for Radar Altimetry Using Proximal
Hamiltonian Monte Carlo-
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Authors:
Muxin Guo;Bo Huang;Lei Yang;Ge Jiang;
Pages: 128 - 137
Abstract: The parameter estimation of conventional radar altimetry waveform often suffers from overfitting due to the high dimensionality on a succession of echoes. To this end, a novel proximal Hamiltonian Monte Carlo (PHMC) algorithm is proposed in this article to estimate the altitude in a statistical manner. More specifically, the Laplace distribution is used to encode the nonsmoothness in the estimation of the elevation parameter of the detection area. However, as the nonconjugation between the sparse prior and Gaussian-likelihood function, the hierarchical Bayesian strategy is employed for the closed-form posterior solution. To overcome the difficulty of fully Bayesian inference on high-dimensional posterior, the PHMC is utilized. Specifically, in order to obtain an available gradient of the nondifferentiable potential energy, the proximal operator is adopted to provide the subgradient to estimate parameters. Both the results using simulation and practical data demonstrate the superiority of the proposed PHMC over other conventional algorithms.
PubDate:
TUE, 26 DEC 2023 09:17:36 -04
Issue No: Vol. 5, No. 3 (2023)
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