Target Tracking and Ranging Based on Single Photon Detection

Li, Zhikang; Liu, Bo; Wang, Huachuang; Chen, Zhen; Zhang, Qun; Hua, Kangjian; Yang, Jing

doi:10.3390/photonics8070278

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…Combining filtering of invalid data with optimized clustering algorithms allows cluster centers to more accurately represent the features and distribution of valid data, reducing computational complexity while improving detection accuracy. This is crucial for practical object detection tasks as it enhances system performance and reliability 27 .…”

Section: Introductionmentioning

confidence: 99%

Image convolution techniques integrated with YOLOv3 algorithm in motion object data filtering and detection

Cheng,

Liu

2024

Sci Rep

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In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as “jogging”, “subway”, “video 1”, and “video 2”. Notably, the detection success rates for “jogging” and “video 1” consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for “Bolt” and “Walking2”, success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method’s tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance.

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Section: Introductionmentioning

confidence: 99%

Image convolution techniques integrated with YOLOv3 algorithm in motion object data filtering and detection

Cheng,

Liu

2024

Sci Rep

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“…The optimization of the fiber coupling structure design of the relay optical path of an APT system is becoming an important topic of research [4,5]. For a traditional APT system, the communication-receiving unit of the relay optical path usually uses a fast-steering mirror (FSM) and coarse-fine tracking cameras to realize the coupling of the signal light to the receiving fiber [6][7][8]. The basic principle is to compensate the FSM's deflection angle with adaptive feedback from the beacon and light signal position information captured by the coarse and fine tracking cameras, respectively [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation Analysis of Piezoelectric Ceramic Tube-Based Fiber Optic Nutator Applied to an Intersatellite Laser Communication System

et al. 2023

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The signal-receiving end of acquisition, pointing, and tracking (APT) systems applied to intersatellite laser communication terminals usually uses a fast-steering mirror (FSM) to control the fiber-coupling process, has a complex structural design, and induces large errors in the nonideal coaxial optical path. Herein, we propose a fiber-optic nutator using a piezoelectric ceramic tube (PCT) as the driving unit that allows scanning in the focal plane of the light signal to achieve active fiber coupling in the APT system. Specifically, this article describes the structural design principle of a PCT-based fiber optic nutator, establishes a simulation model of the mechanism, and proves the correctness of the simulation model by measuring the deflection angle of a PCT based on a parallel light collimator. The minimum accuracy of the designed nutator was 0.145 μm, the maximum nutation radius R was 20.09 μm, and the maximum nutation bandwidth was 20 kHz, as determined through simulation. Finally, the design parameters of the nutator were evaluated. The PCT-based fiber optic nutator, which met the design parameters, structurally replaced the fiber optic coupling component FSM and fine tracking camera in conventional APT systems successfully. Therefore, the PCT-based fiber optic nutator allows the active coupling control of signal light to a single-mode fiber (SMF) based on energy feedback on a theoretical basis and promotes the lightweight design of relay optical paths in APT systems. In addition, with future work in optimization of the nutation control algorithm, the scanning range and accuracy of the nutator can be improved.

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