SAR Image Generation Method Using DH-GAN for Automatic Target Recognition.

In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs). However, acquiring SAR images requires significant resources, both in terms of time and cost. Moreover, due to the inherent properties of radar sensors, SAR images are often marred by speckle noise, a form of high-frequency noise. To address this issue, we introduce a Generative Adversarial Network (GAN) with a dual discriminator and high-frequency pass filter, named DH-GAN, specifically designed for generating simulated images. DH-GAN produces images that emulate the high-frequency characteristics of real SAR images. Through power spectral density (PSD) analysis and experiments, we demonstrate the validity of the DH-GAN approach. The experimental results show that not only do the SAR image generated using DH-GAN closely resemble the high-frequency component of real SAR images, but the proficiency of CNNs in target recognition, when trained with these simulated images, is also notably enhanced.

GMM-Based Adaptive Extended Kalman Filter Design for Satellite Attitude Estimation under Thruster-Induced Disturbances.

Star images from star trackers are usually defocused to capture stars over an exposure time for better centroid measurements. While a satellite is maneuvering, the star point on the screen of the camera is affected by the satellite, which results in the degradation of centroid measurement accuracy. Additionally, this could result in a worse star vector outcome. For geostationary satellites, onboard thrusters are used to maintain or change orbit parameters under orbit disturbances. Since there is misalignment in the thruster and torque is generated by an impulsive shape signal from the torque command, it is difficult to generate target torque; in addition, it also impacts the star image because the impulsive torque creates a sudden change in the angular velocity in the satellite dynamics. This makes the noise of the star image non-Gaussian, which may require introducing a method for dealing with non-Gaussian measurement noise. To meet this goal, in this study, an adaptive extended Kalman filter is implemented to predict measurement vectors with predicted states. The GMM (Gaussian mixture model) is connected in this sequence, giving weighting parameters to each Gaussian density and resulting in the better prediction of measurement vectors. Simulation results show that the GMM-EKF exhibits a better performance than the EKF for attitude estimation, with 30% improvement in performance. Therefore, the GMM-EKF could be a more attractive approach for use with geostationary satellites during station-keeping maneuvers.

Monocular Pose Estimation of an Uncooperative Spacecraft Using Convexity Defect Features.

Spacecraft relative pose estimation for an uncooperative spacecraft is challenging because the target spacecraft neither provides sensor information to a chaser spacecraft nor contains markers that assist vision-based navigation. Moreover, the chaser does not have prior pose estimates when initiating the pose estimation. This paper proposes a new monocular pose estimation algorithm that addresses these issues in pose initialization situations for a known but uncooperative target spacecraft. The proposed algorithm finds convexity defect features from a target image and uses them as cues for matching feature points on the image to the points on the known target model. Based on this novel method for model matching, it estimates a pose by solving the PnP problem. Pose estimation simulations are carried out in three test scenarios, and each assesses the estimation accuracy and initialization performance by varying relative attitudes and distances. The simulation results show that the algorithm can estimate the poses of spacecraft models when a solar panel length and the number of solar panels are changed. Furthermore, a scenario considering the surface property of the spacecraft emphasizes that robust feature detection is essential for accurate pose estimation. This algorithm can be used for proximity operations with a known but uncooperative target spacecraft. Specifically, one of the main applications is relative navigation for on-orbit servicing.

Fractal Texture Enhancement of Simulated Infrared Images Using a CNN-Based Neural Style Transfer Algorithm with a Histogram Matching Technique.

Here, we propose a CNN-based infrared image enhancement method to transform pseudo-realistic regions of simulation-based infrared images into real infrared texture. The proposed algorithm consists of the following three steps. First, target infrared features based on a real infrared image are extracted through pretrained VGG-19 networks. Next, by implementing a neural style-transfer algorithm to a simulated infrared image, fractal nature features from the real infrared image are progressively applied to the image. Therefore, the fractal characteristics of the simulated image are improved. Finally, based on the results of fractal analysis, peak signal-to-noise (PSNR), structural similarity index measure (SSIM), and natural image quality evaluator (NIQE) texture evaluations are performed to know how the simulated infrared image is properly transformed as it contains the real infrared fractal features. We verified the proposed methodology using a simulation with three different simulation conditions with a real mid-wave infrared (MWIR) image. As a result, the enhanced simulated infrared images based on the proposed algorithm have better NIQE and SSIM score values in both brightness and fractal characteristics, indicating the closest similarity to the given actual infrared image. The proposed image fractal feature analysis technique can be widely used not only for the simulated infrared images but also for general synthetic images.

Energy-Efficient Swarming Flight Formation Transitions Using the Improved Fair Hungarian Algorithm.

Recently, drone shows have impressed many people through a convergence of technology and art. However, these demonstrations have limited operating hours based on the battery life. Thus, it is important to minimize the unnecessary transition time between scenes without collision to increase operating time. This paper proposes a fast and energy-efficient scene transition algorithm that minimizes the transition times between scenes. This algorithm reduces the maximum drone movement distance to increase the operating time and exploits a multilayer method to avoid collisions between drones. In addition, a swarming flight system including robust communication and position estimation is presented as a concrete experimental system. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.

Design and Simulation of a High-Speed Star Tracker for Direct Optical Feedback Control in ADCS.

Star Trackers are often the most accurate instrument in an Attitude Determination and Control Systems, but often present a slow update rate, requiring additional sensor and sensor fusion algorithms to provide a smoother and faster output. However, the available rate gyros are either noisy, or expensive and heavy. The proposed work investigates the feasibility of high-speed star trackers with modern optics, sensors, and computing systems. Firstly, we investigate the sensitivity of an optoelectrical acquisition system stimulated by dim stars, secondly, we propose and evaluate an algorithm designed to operate at high speed and to be compatible with an Field-Programmable Gate Array implementation, before evaluating the performance of the implementation on FPGA. Finally, we debate the usability of such a system, both in terms of compatibility with a mission and CubeSat ecosystems, and in terms of performance. As a result, aside from removing the need for a rate gyro, Attitude Determination and Control Systems overall pointing performances can be increased. The proposed attitude determination system achieved a 0.001° accuracy, with a 99.1% sky coverage and an ability to reject false-positive while performing a single-frame lost-in-space star identification at a 50 Hz update rate with a total delay of 19 ms, including 13 ms.

Algorithm with Patterned Singular Value Approach for Highly Reliable Autonomous Star Identification.

In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely used solutions to handle the dynamic conditions, they require prior information about the initial orientation. Therefore, the tracking methods may not be adequate for autonomy of attitude and control systems. In this paper a novel autonomous identification method for dynamic conditions is proposed. Additional constraints are taken into account that can significantly decrease the number of stars imaged and the centroid accuracy. A strategy combining two existing classes for star pattern identification is proposed. The new approach is intended to provide a unique way to determine the identity of stars that promises robustness against noise and rapid identification. Moreover, representative algorithms implemented in actual space applications were utilized as counterparts to analyze the performance of the proposed method in various scenarios. Numerical simulations show that the proposed method is not only highly robust against positional noise and false stars, but also guarantees fast run-time, which is appropriate for high-speed applications.

A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks.

Terrain-aided navigation (TAN) is a technology that estimates the position of the vehicle by comparing the altitude measured by an altimeter and height from the digital elevation model (DEM). The particle filter (PF)-based TAN has been commonly used to obtain stable real-time navigation solutions in cases where the unmanned aerial vehicle (UAV) operates at a high altitude. Even though TAN performs well on rough and unique terrains, its performance degrades in flat and repetitive terrains. In particular, in the case of PF-based TAN, there has been no verified technique for deciding its terrain validity. Therefore, this study designed a Rao-Blackwellized PF (RBPF)-based TAN, used long short-term memory (LSTM) networks to endure flat and repetitive terrains, and trained the noise covariances and measurement model of RBPF. LSTM is a modified recurrent neural network (RNN), which is an artificial neural network that recognizes patterns from time series data. Using this, this study tuned the noise covariances and measurement model of RBPF to minimize the navigation errors in various flight trajectories. This paper designed a TAN algorithm based on combining RBPF and LSTM and confirmed that it can enable a more precise navigation performance than conventional RBPF based TAN through simulations.

High resolution three-dimensional flash LIDAR system using a polarization modulating Pockels cell and a micro-polarizer CCD camera.

An innovative flash LIDAR (light detection and ranging) system with high spatial resolution and high range precision is proposed in this paper. The proposed system consists of a polarization modulating Pockels cell (PMPC) and a micro-polarizer CCD camera (MCCD). The Pockels cell changes its polarization state with respect to time after a laser pulse is emitted from the system. The polarization state of the laser-return pulse depends on the arrival time. The MCCD measures the intensity of the returning laser pulse to calculate the polarization state, which gives the range. A spatial resolution and range precision of 0.12 mrad and 5.2 mm at 16 m were obtained, respectively, in this experiment.