Work

A Training Sample Selection Method With Fusing GIP Statistic and Geographic Information for Airborne Radar

Year: 2025

Type: article

Abstract: ABSTRACT A training sample selection method is proposed by fusing the generalized inner product (GIP) statistic with geographic information to construct the fused metric of the sample set. Based on this metric, more suitable training samples are selected. The experimental results demonstrate that the proposed method exhibits excellent robustness in different clutter environments, particularly in complex ground environments containing discrete moving scatterers, where its clutter suppression performance is superior to that of both GIP and knowledge‐aided space‐time adaptive processing (KA‐STAP) methods.

Source: Electronics Letters

DOI: 10.1049/ell2.70345

Authors: Chenran Gao, Wenchong Xie, Yuanyi Xiong, Wei Chen, Buqiu Tian

Language: English

Topic: Radar Systems and Signal Processing

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hubei Province

Awards: G3898977648, G7544909220