Deep Q-Network Based Target Detection for Anti-Ship Missile Systems

In anti-ship missile systems, one of the biggest challenges is accurately identifying the real target in complex and noisy sea environments, where false targets(decoys) and distorted signals can makes things difficult. To overcome this problem , we used an advanced learning technique called Deep – Q- Network (DQN), which is specially trained to recognize the target. These algorithm confirm accurate target identification even in worst situations by transforming seeker data through rigid and non rigid transformations. The methodology involves aligning real-time seeker data with reference data to enable adaptive learning. Three distinct scenarios—Rigid, Distortion, and Distortion with Decoys are simulated to evaluate the system’s robustness. Results showed that the proposed method maintains high matching efficiency and accuracy , effectively identifying the designated target in varying formations and difficult environments.