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In 2021, the world’s major military powers actively promoted the development of aerial bombs. This paper summarized the development trend of the United States, Russia, European countries and India in the field of aerial bombs. In order to deal with the conflict between great powers in the future, the United States has comprehensively promoted the construction of aerial guided bomb combat capability and the R&D and application of advanced technology.Russia focuses on the development of glide guided bombs and unmanned airborne munitions.European countries actively promote the research of unmanned airborne ammunition and the independent control of guided bomb technology.India focuses on developing long-range aerial bombs which can be launched outside the defense zone.Aerial bombs will mainly develop in the direction of autonomous networking and coordination, standoff attack, miniaturization, UAV carrying and large equivalent deterrence.

The loop closure detection (LCD) methods in Unmanned Aerial Vehicle (UAV) Visual Inertial Navigation System (VINS) are often affected by issues such as insufficient image texture information and limited observational perspectives, resulting in constrained UAV positioning accuracy and reduced capability to perform complex tasks. This study proposes a Bag-of-Words (BoW) LCD method based on Neural Radiance Field (NeRF), which estimates camera poses from existing images and achieves rapid scene reconstruction through NeRF. A method is designed to select virtual viewpoints and render images along the flight trajectory using a specific sampling approach to expand the limited observational angles, mitigating the impact of image blur and insufficient texture information at specific viewpoints while enlarging the loop closure candidate frames to improve the accuracy and success rate of LCD. Additionally, a BoW vector construction method that incorporates the importance of similar visual words and an adapted virtual image filtering and comprehensive scoring calculation method are designed to determine loop closures. Applied to VINS-Mono and ORB-SLAM3, and compared with the advanced BoW model LCDs of the two systems, results indicate that the NeRF-based BoW LCD method can detect more than 48% additional accurate loop closures, while the system's navigation positioning error mean is reduced by over 46%, validating the effectiveness and superiority of the proposed method and demonstrating its significant importance for improving the navigation accuracy of VINS. [ABSTRACT FROM AUTHOR]

This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization–back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal. [ABSTRACT FROM AUTHOR]

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h. [ABSTRACT FROM AUTHOR]

In this work, based on the sol-gel method combined with spray granulation technology, a novel process method to prepare Yttria stabilized ZrO2 beads with 5–50 µm diameter, 123 nm grains size, few pores, solid internal structure and well spherical type is proposed. These advantages can greatly improve the beads density, and decline the sintering temperature. The optimum preparation parameters, including sol and spray parameters, baking and sintering conditions, are obtained through the investigation of experimental conditions. The SEM test results of the surface and cross section morphologies show that the beads have solid structure and there is good consistency between the surface and interior of the beads. The average grain size of test results is about 123 nm. The XRD test results demonstrate that no monoclinic phase exists in the beads. The sintering temperature of the new process is only 1200 °C. Highlights: A novel method to prepare Yttria stabilized ZrO2 beads with 5–50 µm diameter is proposed. The optimum parameters of experiment conditions are studied and obtained. The beads have characteristics of few pores, solid internal structure and well spherical type. This process has low sintering temperature that is only 1200 °C. [ABSTRACT FROM AUTHOR]

In this work, based on the electrospinning method, pure Co 3 O 4 , pure MnO 2 , and Co 3 O 4 composite nano-fiber materials doped with different ratios of Mn 4+ were prepared. XRD, XPS, BET and SEM tests were used to characterize the composition, structure and morphology of the materials. An electrochemical workstation was used to test the electrochemical performance of the materials. The results showed that the material properties had greatly improved on doping Mn 4+ in Co 3 O 4 nano-fibers. The relationship between the amount of Mn 4+ doped in the Co 3 O 4 composite nano-fiber material and its electrochemical performance was also tested and is discussed in this report. The results show that when n Co  :  n Mn = 20 : 2, the Co 3 O 4 composite nano-fiber material had a specific surface area of 68 m 2 g -1 . Under the current density of 1 A g -1 , the 20 : 2 sample had the maximum capacitance of 585 F g -1 , which was obviously larger than that of pure Co 3 O 4 nano-fibers (416 F g -1 ). After 2000 cycles of charging/discharging, the specific capacitance of the 20 : 2 sample was 85.9%, while that of the pure Co 3 O 4 nano-fiber material was only 76.4%. The mechanism of performance improvement in the composite fibers was analyzed, which demonstrated concrete results., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)