Quantum Target Recognition Enhancement Algorithm for UAV Consumer Applications

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.