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UAV Swarm with high dynamic configuration at a large scale requires a high-precision mathematical model to fully exploit its boundary performance. In order to instruct the engineering application with high confidence, uncertainties induced from either systematic measurement or the environment cannot be ignored. This paper investigates the I t o ^ stochastic model of the UAV Swarm system with multiplicative noises. By combining the cooperative kinematic model with a simplified individual dynamic model of fixed-wing-aircraft for the first time, the configuration control model is derived. Considering the uncertainties in actual flight, multiplicative noises are introduced to complete the I t o ^ stochastic model. Following that, the estimator and controller are designed to control the formation. The mean-square uniform boundedness condition of the proposed stochastic system is presented for the closed-loop system. In the simulation, the stochastic robustness analysis and design (SRAD) method is used to optimize the properties of the formation. More importantly, the effectiveness of the proposed model is also verified using real data of five unmanned aircrafts collected in outfield formation flight experiments.
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http://dx.doi.org/10.3390/s19153278 | DOI Listing |
Chaos
September 2025
Department of Physics and Astronomy, Tokyo University of Science, Noda, Chiba 278-8510, Japan.
We introduce randomness to Pomeau-Manneville (PM) maps by incorporating dichotomous multiplicative noise that alternates between dynamics with an attracting and a repelling fixed point. We characterize the dynamical behavior by measuring the separation of two nearby orbits. Controlling the probability of selecting the repelling PM map, we find two noise-induced transitions.
View Article and Find Full Text PDFMath Biosci Eng
June 2025
Computer Sciences and Mathematics Division, Oak Ridge National Laboratory, PO Box 2008, 37831-6013, TN, USA.
We consider the Gause predator-prey with general bounded or sub‑linear functional responses, - which includes those of Holling types Ⅰ-Ⅳ. - and multiplicative Gaussian noise. In contrast to previous studies, the prey in our model follows logistic dynamics while the predator's population is solely regulated by consumption of the prey.
View Article and Find Full Text PDFPLoS One
September 2025
School of Electrical and Information Technology, Yunnan Minzu University, Kunming, China.
Side-scan sonar image (SSI) are often affected by a combination of multiplicative speckle noise and additive noise, which degrades image quality and hinders target recognition and scene interpretation. To address this problem, this paper proposes a denoising algorithm that integrates non-local similar block clustering with Bayesian sparse coding. The proposed method leverages cross-scale structural features and noise statistical properties of image patches, and employs a similarity metric based on the Equivalent Number of Looks (ENL) along with an improved K-means clustering algorithm to achieve accurate classification and enhance intra-class noise consistency.
View Article and Find Full Text PDFHearing loss is increasingly prevalent and poses a significant public health concern. While both aging and occupational noise exposure are recognized contributors, their interactive effects and gender-specific patterns remain underexplored. This cross-sectional study analyzed data from 135,251 employees in Jiangsu Province, China.
View Article and Find Full Text PDFChaos
August 2025
Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea.
Removing noise from a signal without knowing the characteristics of the noise is a challenging task. This paper introduces a signal-noise separation method based on time-series prediction. We use Reservoir Computing (RC) to extract the maximum portion of "predictable information" from a given signal.
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