A Multi-Frame Superposition Detection Method for Dim-Weak Point Targets Based on Optimized Clustering Algorithm

Long-range surveillance and early warning of space targets are significant factors in security. Under remote observation conditions, the energy performance target is weak subject to environmental imaging process contamination. Most detection methods aimed at with a high signal-to-noise ratio (SNR) or local contrast, for dim-weak small poor; therefore, signal often enhanced by accumulation. However, owing interference caused instability system, accumulation errors occur target, resulting dispersion energy, making challenge. To solve above problem, this study proposed multi-frame superposition method point based on an optimized clustering algorithm combining inherent features using difference between noise distribution detection. First, we simulated post-disturbance established optical system model target. Subsequently, used data dimension reduction outlier removal extract potential area. Finally, were sent calculation judgment. Given that accuracy rate reaches 87.1% when SNR 1 dB, experimental results show paper can effectively detect low SNR. In addition, there improvement receiver characteristic curve compared other algorithms real scene, which further proves superiority paper.