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In this paper, a new-brand feature-based detector via an improved concave hull classifier (FB-ICHC) is proposed to detect marine small targets. The dimension of feature space is suggested to be three, making a compromise between high detection accuracy and low computational cost. The main contributions are in the following two aspects. On the one hand, three features are well-designed from time series and Doppler spectrum, called relative phase zero ratio (RPZR), relative variation coefficient (RCV), and whitened peak height ratio (WPHR). RPZR can measure the pseudo-period properties in phase time series, insensitive to SCRs. In the Doppler spectrum, RCV reflects fluctuation variation in high SCR cases and WPHR describes the intensity property after clutter suppression in low SCR cases. On the other hand, in 3D feature space, an improved concave hull classifier is developed to further shrink the decision region, where a fast two-stage parameter search is designed for low computational cost and accurate control of false alarm rate. Finally, experimental results using open-recognized datasets show that the proposed FB-ICHC detector can improve detection performance by over 20% and reduce runtime by over 49%, compared with existing feature-based detectors with three features. Full article

Addressing the challenge of radar detection of small targets under sea clutter, target detection methods based on a three-dimensional feature space have shown effectiveness. However, their application has revealed several problems, including high dependency on linear relationships between features for dimensionality reduction, unclear reduction objectives, and spatial divergence of target samples, which limit detection performance. To mitigate these challenges, we constructed a feature density distance metric employing copula functions to quantitatively describe the classification capability of multidimensional features to distinguish targets from sea clutter. On the basis of this, a lightweight nonlinear dimensionality reduction network utilizing a self-attention mechanism was developed, optimally re-expressing multidimensional features into a three-dimensional feature space. Additionally, a concave hull classifier using feature sample distance was proposed to mitigate the negative impact of target sample divergence in the feature space. Furthermore, multivariate autoregressive prediction was used to optimize features, reducing erroneous decisions caused by anomalous feature samples. Experimental results using the measured data from the SDRDSP public dataset demonstrated that the proposed detection method achieved a detection probability more than 4% higher than comparative methods under Sea State 5, was less affected by false alarm rates, and exhibited superior detection performance under different false alarm probabilities from 10⁻³ to 10⁻¹. Full article