Dynamic Target Search of UAV Swarm Based on Improved Pigeon-Inspired Optimization

Jiafeng, Lu; Jiang, Ju; Han, Bing; Liu, Jicheng; Lu, Xiong

doi:10.1007/978-981-19-3998-3_35

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The waveform of the sound source arriving at the microphone array at this point is considered to be a spherical waveform, which is consistent with the near-field model, and vice versa, for a plane wave, which is consistent with the far-field model, when L < 2D 2 λ m . The near-field model is suitable for close sound sources, but considering the amplitude difference and phase difference in sound waves, it can more accurately describe the characteristics of sound wave propagation, thus improving the positioning accuracy, while the far-field model is the opposite [4,5]. Therefore, the near-field model is chosen in this paper, and its applicable area is improved by increasing the array aperture.…”

Section: Delineation Of Near-field and Far-field Modelsmentioning

confidence: 99%

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Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition

Chen,

Yu,

Yang

2024

Sensors

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To address the challenge of accurately locating unmanned aerial vehicles (UAVs) in situations where radar tracking is not feasible and visual observation is difficult, this paper proposes an innovative acoustic source localization method based on improved Empirical Mode Decomposition (EMD) within an adaptive frequency window. In this study, the collected flight signals of UAVs undergo smoothing filtering. Additionally, Robust Empirical Mode Decomposition (REMD) is applied to decompose the signals into Intrinsic Mode Function (IMF) components for spectrum analysis. We introduce a sliding frequency window with adjustable bandwidth, which is automatically determined using a Grey Wolf Optimizer (GWO) with a sliding index. This window is used to lock and extract specific frequencies from the IMFs. Based on predefined criteria, the extracted IMF components are reconstructed, and trigger signal times are analyzed and recorded from these reconstructed IMFs. The time differences between sensor receptions are then calculated. Furthermore, this study introduces the Chan–Taylor localization algorithm based on weighted least squares. This advanced algorithm takes sensor time delay parameters as input and solves a set of nonlinear equations to determine the target’s location. Simulations and real-world signal tests are used to validate the robustness and performance of the proposed method. The results indicate that the localization error remains below 5% within a 15 m × 15 m measurement area. This provides an efficient and real-time method for detecting the location of small UAVs.

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Section: Delineation Of Near-field and Far-field Modelsmentioning

confidence: 99%

“…far-field model is the opposite [4,5]. Therefore, the near-field model is chosen in this paper, and its applicable area is improved by increasing the array aperture.…”

Section: Delineation Of Near-field and Far-field Modelsmentioning

confidence: 99%

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition

Chen,

Yu,

Yang

2024

Sensors

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“…Lu et al [88] proposed an improved Pigeon-Inspired Optimization algorithm (IPIO) based on natural selection and Gauss-Cauchy mutation, established an environment-aware map, and designed an integer encoding method. A discrete compass operator, discrete landmark operators, Gaussian mutation and Cauchy mutation operators are introduced to break away from local optimum.…”

Section: Pigeon-inspired Optimizationmentioning

confidence: 99%

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

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Dynamic Target Search of UAV Swarm Based on Improved Pigeon-Inspired Optimization

Cited by 2 publications

References 6 publications

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition

UAV Formation Trajectory Planning Algorithms: A Review

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