使用混合无香味卡尔曼-连续蚁群滤波器(HUK-CACF)追踪入侵无人机。

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

摘要

无人驾驶飞行器(UAV)在人口稠密地区的广泛应用引起了人们的关注。在这些地区,需要适当的跟踪系统来跟踪未经授权/入侵者的无人机,以确保安全。无人驾驶飞行器安装了可适应雷达天线阵列(UAVMARAA),可以灵活地到达无法到达的地方。为此,我们提出了一种混合无cented Kalman-Continuous Ant Colony Filter(HUK-CACF)来有效估计入侵无人机的位置。仿真结果表明,与现有滤波器相比,所提出的滤波器在成功率方面更高效、更稳健。此外,针对均匀线性阵列(ULA)、均匀矩形阵列(URA)和均匀圆形阵列(UCA)等各种自适应雷达天线阵列(ARAA)模式,通过考虑晃动和相互耦合(MC)效应,对各种参数(如方位、多普勒频移、测距和雷达截面(RCS))的实际跟踪效果进行了分析。结果表明,所提出的滤波器在所有情况下都表现出色。在各种 ARAA 中,URA 的性能优于其他配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tracking of invader drone using hybrid unscented Kalman-Continuous Ant Colony Filter (HUK-CACF)

Splendid Unmanned Aerial Vehicle (UAV) applications upshot its enormous use in densely inhabited areas, which is a matter of concern. In such areas, a proper tracking system is required to track an unauthorized/invader drone to ensure safety. With the flexibility of reaching inaccessible places, an Unmanned Aerial Vehicle Mounted Adaptable Radar Antenna Array (UAVMARAA) could be used. In this regard, a Hybrid Unscented Kalman-Continuous Ant Colony Filter (HUK-CACF) is proposed to estimate the position of the invader drone efficiently. Simulation results demonstrate the efficiency and robustness of the proposed filter for tracking system compared to the existing filters in terms of success rate. Further, for various Adaptable Radar Antenna Array (ARAA) patterns such as Uniform Linear Array (ULA), Uniform Rectangular Array (URA), and Uniform Circular Array (UCA), analysis is done for pertaining actual tracking effect for various parameters such as bearing, Doppler shift, ranging, and Radar Cross Section (RCS) by considering wobbling and mutual coupling (MC) effect. The result shows that the proposed filter outperforms in all the scenarios. Among the various ARAA, URA performs better than the other configurations.

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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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