Aerospace 

About: Aerospace is an academic journal published by Multidisciplinary Digital Publishing Institute. The journal publishes majorly in the area(s): Computer science & Mechanics. It has an ISSN identifier of 2226-4310. It is also open access. Over the lifetime, 1400 publications have been published receiving 1886 citations.

Autonomous Trajectory Generation Algorithms for Spacecraft Slew Maneuvers

Abstract: Spacecraft need to be able to reliably slew quickly and rather than simply commanding a final angle, a trajectory calculated and known throughout a maneuver is preferred. A fully solved trajectory allows for control based off comparing current attitude to a time varying desired attitude, allowing for much better use of control effort and command over slew orientation. This manuscript introduces slew trajectories using sinusoidal functions compared to optimal trajectories using Pontryagin’s method. Use of Pontryagin’s method yields approximately 1.5% lower control effort compared to sinusoidal trajectories. Analysis of the simulated system response demonstrates that correct understanding of the effect of cross-coupling is necessary to avoid unwarranted control costs. Additionally, a combination of feedforward with proportional derivative control generates a system response with 3% reduction in control cost compared to a Feedforward with proportional integral derivative control architecture. Use of a calculated trajectory is shown to reduce control cost by five orders of magnitude and allows for raising of gains by an order of magnitude. When control gains are raised, an eight orders of magnitude lower error is achieved in the slew direction, and rather than an increase in control cost, a decrease by 11.7% is observed. This manuscript concludes that Pontryagin’s method for generating slew trajectories outperforms the use of sinusoidal trajectories and trajectory generation schemes are essential for efficient spacecraft maneuvering.

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Abstract: Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

Rocket Lab Mission to Venus

Abstract: Regular, low-cost Decadal-class science missions to planetary destinations will be enabled by high-ΔV small spacecraft, such as the high-energy Photon, and small launch vehicles, such as Electron, to support expanding opportunities for scientists and to increase the rate of science return. The Rocket Lab mission to Venus is a small direct entry probe planned for baseline launch in May 2023 with accommodation for a single ~1 kg instrument. A backup launch window is available in January 2025. The probe mission will spend about 5 min in the Venus cloud layers at 48–60 km altitude above the surface and collect in situ measurements. We have chosen a low-mass, low-cost autofluorescing nephelometer to search for organic molecules in the cloud particles and constrain the particle composition.

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Abstract: Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces the definition and basic process of trajectory prediction, including four modules: preparation, prediction, update, and output. In addition, the trajectory prediction methods are summarized into three types: the state estimation model, the Kinetic model, and the machine learning model, and in-depth analysis of various models is carried out. Further, the relevant databases required for the study are introduced, including the aircraft performance database, aircraft monitoring database, and meteorological database. Finally, challenges and future development directions of the current trajectory prediction problem are summarized.

Venus Life Finder Missions Motivation and Summary

Abstract: Finding evidence of extraterrestrial life would be one of the most profound scientific discoveries ever made, advancing humanity into a new epoch of cosmic awareness. The Venus Life Finder (VLF) missions feature a series of three direct atmospheric probes designed to assess the habitability of the Venusian clouds and search for signs of life and life itself. The VLF missions are an astrobiology-focused set of missions, and the first two out of three can be launched quickly and at a relatively low cost. The mission concepts come out of an 18-month study by an MIT-led worldwide consortium.