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Quantum Target Recognition Enhancement Algorithm for UAV Consumer Applications
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AI
DOI：10.1109/TCE.2024.3412968.png)
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Abstract
En 中文
In UAV Consumer Applications, the challenges and methods of current unmanned aerial vehicle (UAV) radar detection technology are examined. The quantum multi-pattern recognition network model and algorithm are analyzed, and the Quantum Multi-Pattern Recognition Algorithm based on Phase Rotation (PRQMPRA) is proposed according to Grover's algorithm optimization theory. The issue in the Redundancy Quantum Multi-Pattern Recognition Algorithm (RQMPRA), where a decrease in the probability of successful search can be caused by two phase rotations of $\pi $ each, is addressed by the optimization algorithm. The pattern recognition capabilities of Error Backpropagation Algorithm (EBPA), the Deep Autoencoder Learning Algorithm based on Cross-Entropy Function (CDAA), RQMPRA, and PRQMPRA are examined using three different datasets. The results indicate that a higher recognition rate and relatively faster processing speed are exhibited by PRQMPRA when error constraints are specified. To study the target detection problem in UAV consumer applications using a pattern classification approach, a radar target detection method based on the Quantum Multi-Pattern Recognition Algorithm is proposed. Experiments for UAV target detection is conducted with the four algorithms, and the research demonstrates that higher detection accuracy and a high discovery probability can be maintained in low signal-to-noise ratio conditions by PRQMPRA.
Keywords:
Pattern recognition
Autonomous aerial vehicles
Radar
Quantum computing
Radar detection
Object detection
Target recognition
Target detection
unmanned aerial vehicle
quantum computing
pattern recognition
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