Browsing by Author "Luo, Qinghua"

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    Online dynamic working-state recognition through uncertain data classification
    (Elsevier, 2020-11-28) Yan, Xiaozhen; Luo, Qinghua; Sun, Jianyu; Luo, Zhenhua; Chen, Yunsai
    The satellite must continue working properly under different working environments and working loads. The power system is an essential component. Due to different working tasks, loads, and attitudes, a power system has many diverse working states. Therefore, it is necessary to accurately recognize the working state online for fault diagnostics and health management. However, under different working loads, measurement errors, environmental noise, environmental interference, and other uncertain factors, the output voltage value of a satellite power system has different levels of uncertainties. If these uncertainties and various working states are not considered, the recognition results can be of low quality. To address this problem and the uncertainty factors, we present an online dynamic working-state recognition system for satellite power systems based on uncertain data classification. In the system, we first explore the uncertain-data clustering center to model the working state. Then, with a slide-window processing strategy, we compute the distances between the uncertain cluster centers and the uncertain voltage data for the satellite power system online. Thus, we can obtain more accurate dynamic working-state recognition results. The evaluation results of real data demonstrate that the presented system is valid for working-state recognition and can be applied to a satellite power system.
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    An ultra-short baseline underwater positioning system with Kalman filtering
    (MDPI, 2020-12-28) Luo, Qinghua; Yan, Xiaozhen; Ju, Chunyu; Chen, Yunsai; Luo, Zhenhua
    The ultra-short baseline underwater positioning is one of the most widely applied methods in underwater positioning and navigation due to its simplicity, efficiency, low cost, and accuracy. However, there exists environmental noise, which has negative impacts on the positioning accuracy during the ultra-short baseline (USBL) positioning process, which results in a large positioning error. The positioning result may lead to wrong decision-making in the latter processing. So, it is necessary to consider the error sources, and take effective measurements to minimize the negative impact of the noise. In our work, we propose a USBL positioning system with Kalman filtering to improve the positioning accuracy. In this system, we first explore a new kind of element array to accurately capture the acoustic signals from the object. We then organically combine the Kalman filters with the array elements to filter the acoustic signals, using the minimum mean-square error rule to obtain accurate acoustic signals. We got the high-precision phase difference information based on the non-equidistant quaternary original array and the phase difference acquisition mechanism. Finally, on account of the obtained accurate phase difference information and position calculation, we determined the coordinates of the underwater target. Comprehensive evaluation results demonstrate that our proposed USBL positioning method based on the Kalman filter algorithm can effectively enhance the positioning accuracy