Target Detection in Maritime Radar Tracking Based on Spatial Image Gradients

This article presents a target detection algorithm based on spatial gradient image features to enhance radar detection performance in low signal-to-noise ratio (SNR) environments. The proposed spatial-gradient-based detector (SGBD) exploits the characteristic appearance patterns of targets in radar imagery without requiring specific target feature functions. The algorithm calculates intensity variations across the image and imposes a smoothing constraint to account for target features without knowing the specific target model. The discrete implementation uses a convolution kernel to approximate gradients and Laplacians alongside an iterative solution to the Euler–Lagrange equation. The performance is evaluated against standard constant false alarm rate (CFAR) detectors using Monte Carlo simulations, highlighting the advantages of the proposed approach. The simulations further address the impact of heavy-tailed clutter distributions and spatially correlated clutter, as commonly encountered in maritime scenarios. In addition, real-world maritime radar data results are shown to validate SGBD’s effectiveness.