Work

False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier

Year: 2025

Type: article

Abstract: 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.

Source: Remote Sensing

DOI: 10.3390/rs17111808

Authors: Sainan Shi, Jiajun Wang, Jiajun Wang, Jie Wang, Jie Wang, Tao Li

Institutions: Nanjing University of Information Science and Technology, Hangzhou Dianzi University

Language: English

Topic: Radar Systems and Signal Processing

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: National Natural Science Foundation of China