Collaborative Integrated Navigation for Unmanned Aerial Vehicle Swarms Under Multiple Uncertainties.

UAV swarms possess unique advantages in performing various tasks and have been successfully applied across multiple scenarios. Accurate navigation serves as the foundation and prerequisite for executing these tasks. Unlike single UAV localization, swarms enable the sharing and propagation of precise positioning information, which enhances overall swarm localization accuracy but also introduces the issue of uncertainty propagation.

An ultra lightweight neural network for automatic modulation classification in drone communications.

Unmanned aerial vehicle (UAV)-assisted communication based on automatic modulation classification (AMC) technology is considered an effective solution to improve the transmission efficiency of wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment. However, many existing deep learning (DL)-based AMC methods cannot be directly applied to UAV platform with limited computing power and storage space, because of the contradiction between accuracy and efficiency. This paper mainly studies the lightweight of DL-based AMC networks to improve adaptability in resource-constrained scenarios.

A triangular-trapezoidal dual-channel shaping algorithm for resistive anode readout systems and its FPGA implementation.

This paper introduces a novel digital triangular-trapezoidal double-channel shaping algorithm to enhance the counting rate of resistive anode detectors. The algorithm is based on the trapezoidal shaping algorithm and improves it. At the extreme counting rate, the trapezoidal shaping algorithm cannot alleviate the pulse pileup, so the counting rate cannot meet the requirements of a high performance detector.

Detection and identification of oil spill species based on polarization information.

Aiming at the problem of poor oil identification accuracy in existing oil spill detection technologies, the polarization degree model of oil spill on rough sea surface under different azimuths and zenith angles was established based on Fresnel theory. The analytical expressions of visible light polarization degree in calm and fluctuating water surface were derived respectively, and the polarization degree model of oil spill in reflection space was constructed. The effectiveness of the method and its influence on the polarization distribution of oil spill were analyzed by simulation.

An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching.

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching.

Improvement of coated aluminum sheet pBRDF model based on scattering and phase function optimization.

The pBRDF model is able to relate the properties of target materials to the polarization information of incident and reflected light, and is an important basis for obtaining polarization information of targets in space. It is an important basis for obtaining target polarization information and polarization detection of space targets. P-G model is the first strictly pBRDF model officially released, but there are still deficiencies.

Performance analysis of the BDSBAS-B1C message in trial operation stage.

BeiDou Satellite-based Augmentation System (BDSBAS) has come into the trial operation stage since July, 2020. To evaluate the characteristic of the augmentation message in BDSBAS-B1C signal, the effectiveness of the message content was firstly analyzed, and then the validity of the broadcasting strategy was estimated. Finally, the accuracy of the user equivalent ranging error (UERE) and the single frequency positioning error with different correction parameters in BDSBAS-B1C message was evaluated.

Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing.

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT).

Dynamic evolutionary metamodel analysis of the vulnerability of complex systems.

Because the collapse of complex systems can have severe consequences, vulnerability is often seen as the core problem of complex systems. Multilayer networks are powerful tools to analyze complex systems, but complex networks may not be the best choice to mimic subsystems. In this work, a cellular graph (CG) model is proposed within the framework of multilayer networks to analyze the vulnerability of complex systems.

Comparison of Ultra-Rapid Orbit Prediction Strategies for GPS, GLONASS, Galileo and BeiDou.

Currently, ultra-rapid orbits play an important role in the high-speed development of global navigation satellite system (GNSS) real-time applications. This contribution focuses on the impact of the fitting arc length of observed orbits and solar radiation pressure (SRP) on the orbit prediction performance for GPS, GLONASS, Galileo and BeiDou. One full year's precise ephemerides during 2015 were used as fitted observed orbits and then as references to be compared with predicted orbits, together with known earth rotation parameters.

Active disturbance rejection control based robust output feedback autopilot design for airbreathing hypersonic vehicles.

Since motion control plant (y=f(⋅)+d) was repeatedly used to exemplify how active disturbance rejection control (ADRC) works when it was proposed, the integral chain system subject to matched disturbances is always regarded as a canonical form and even misconstrued as the only form that ADRC is applicable to. In this paper, a systematic approach is first presented to apply ADRC to a generic nonlinear uncertain system with mismatched disturbances and a robust output feedback autopilot for an airbreathing hypersonic vehicle (AHV) is devised based on that. The key idea is to employ the feedback linearization (FL) and equivalent input disturbance (EID) technique to decouple nonlinear uncertain system into several subsystems in canonical form, thus it would be much easy to directly design classical/improved linear/nonlinear ADRC controller for each subsystem.

Ground-to-satellite quantum teleportation.

An arbitrary unknown quantum state cannot be measured precisely or replicated perfectly. However, quantum teleportation enables unknown quantum states to be transferred reliably from one object to another over long distances, without physical travelling of the object itself. Long-distance teleportation is a fundamental element of protocols such as large-scale quantum networks and distributed quantum computation.

Low-Frequency Error Extraction and Compensation for Attitude Measurements from STECE Star Tracker.

The low frequency errors (LFE) of star trackers are the most penalizing errors for high-accuracy satellite attitude determination. Two test star trackers- have been mounted on the Space Technology Experiment and Climate Exploration (STECE) satellite, a small satellite mission developed by China. To extract and compensate the LFE of the attitude measurements for the two test star trackers, a new approach, called Fourier analysis, combined with the Vondrak filter method (FAVF) is proposed in this paper.