A Coverage-Based Cooperative Detection Method for CDUAV: Insights from Prediction Error Pipeline Modeling

To address the challenges of detection and acquisition caused by trajectory prediction errors during the midcourse–terminal guidance handover phase in cross-domain unmanned aerial vehicles (CDUAV), this study proposes a collaborative multi-interceptor detection coverage optimization method based on...

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Bibliographic Details
Published in:Drones (Basel) Vol. 9; no. 6; p. 397
Main Authors: Li, Jiong, Feng, Xianhai, He, Yangchao, Shao, Lei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2025
Subjects:
Accuracy
Algorithms
Collaboration
Computational efficiency
Confidence intervals
Control systems
cooperative detection
coverage optimization
cross-domain unmanned aerial vehicle
Deep learning
Drone aircraft
Error correction & detection
Error reduction
Interceptors
Mathematical optimization
Methods
midcourse–terminal guidance handover
Modelling
Monte Carlo simulation
Morphology
Optimization
prediction error
Statistical analysis
Statistical methods
Target acquisition
Terminal guidance
Unmanned aerial vehicles
Wireless sensor networks
China
ISSN:2504-446X, 2504-446X
Online Access:Get full text
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Summary:To address the challenges of detection and acquisition caused by trajectory prediction errors during the midcourse–terminal guidance handover phase in cross-domain unmanned aerial vehicles (CDUAV), this study proposes a collaborative multi-interceptor detection coverage optimization method based on predictive error pipeline modeling. Firstly, we employ nonlinear least squares to fit parameters for the motion model of CDUAV. By integrating error propagation theory, we derive a recursive expression for error pipelines under t-distribution and establish a parametric model for the target’s high-probability region (HPR). Next, we analyze target acquisition scenarios during guidance handover and reformulate the collaborative detection problem as a field-of-view (FOV) coverage optimization task on a two-dimensional detection plane. This framework incorporates the target HPR and the seeker detection FOV models, with an objective function defined for coverage optimization. Finally, inspired by wireless sensor network (WSN) coverage strategies, we implement the starfish optimization algorithm (SFOA) to enhance computational efficiency. Simulation results demonstrate that compared to Monte Carlo statistical methods, our parametric modeling approach reduces prediction error computation time from 15.82 s to 0.09 s while generating error pipeline envelopes with 99% confidence intervals, showing superior generalization capability. The proposed collaborative detection framework effectively resolves geometric coverage optimization challenges arising from mismatches between target HPR and FOV morphology, exhibiting rapid convergence and high computational efficiency.
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ISSN:2504-446X
2504-446X
DOI:10.3390/drones9060397