Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation. Consequently, the proposed method can effectively improve target tracking precision. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xingkui Xu [verfasserIn] Qingyu Hou [verfasserIn] Chunfeng Wu [verfasserIn] Zhigang Fan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	GSO system identification multi-modal functions multi-target tracking anti-UAVs

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 119609-119623
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:119609-119623

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2986492

Katalog-ID:	DOAJ047683759

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callnumber-first	T - Technology
author	Xingkui Xu
spellingShingle	Xingkui Xu misc TK1-9971 misc GSO misc system identification misc multi-modal functions misc multi-target tracking misc anti-UAVs misc Electrical engineering. Electronics. Nuclear engineering Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field
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topic_title	TK1-9971 Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field GSO system identification multi-modal functions multi-target tracking anti-UAVs
topic	misc TK1-9971 misc GSO misc system identification misc multi-modal functions misc multi-target tracking misc anti-UAVs misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc GSO misc system identification misc multi-modal functions misc multi-target tracking misc anti-UAVs misc Electrical engineering. Electronics. Nuclear engineering
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title	Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field
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title_sort	improved gso algorithms and their applications in multi-target detection and tracking field
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title_auth	Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field
abstract	Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation. Consequently, the proposed method can effectively improve target tracking precision.
abstractGer	Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation. Consequently, the proposed method can effectively improve target tracking precision.
abstract_unstemmed	Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation. Consequently, the proposed method can effectively improve target tracking precision.
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title_short	Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field
url	https://doi.org/10.1109/ACCESS.2020.2986492 https://doaj.org/article/6d7547fce1014209a74dc5e5cdb88c93 https://ieeexplore.ieee.org/document/9060969/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T13:30:18.147Z
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Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

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