An Efficient Sparse Representation Method for Passive Radar

TY - JOUR

T1 - An Efficient Sparse Representation Method for Passive Radar

AU - Sun, Quande

AU - Feng, Yuan

AU - Shan, Tao

AU - Zhao, Juan

AU - Bai, Xia

AU - Wang, Tianrun

AU - Wang, Zhi

N1 - Publisher Copyright: © 2025 IEEE.

PY - 2025

Y1 - 2025

N2 - Passive radar (PR) commonly estimates target parameters by calculating the cross-ambiguity function (CAF), which is prone to generating a wider main lobe and higher sidelobes, leading to issues such as weak targets being masked and adjacent targets being difficult to distinguish. A parameter estimation method for PR based on sparse representation (SR) is proposed to address the above challenges. First, an SR model based on signal segmentation and Fourier transform is proposed to address the issue of excessively large dictionary matrix (DM) by using the fast Fourier transform (FFT). Then, an orthogonal matching pursuit (OMP) algorithm based on detection threshold (DT-OMP) is proposed to adaptively determine the number of atoms to be selected by a preset threshold. Furthermore, a model mismatch correction method for SR (MMC-SR) is proposed to achieve accurate estimation of target parameters in off-grid situations. Simulations and practical experiments have shown that the proposed method can effectively mitigate the influence of wider main lobe and higher sidelobes of CAF, thereby improving resolution and providing a refined estimation of target parameters, showcasing significant practical application value.

AB - Passive radar (PR) commonly estimates target parameters by calculating the cross-ambiguity function (CAF), which is prone to generating a wider main lobe and higher sidelobes, leading to issues such as weak targets being masked and adjacent targets being difficult to distinguish. A parameter estimation method for PR based on sparse representation (SR) is proposed to address the above challenges. First, an SR model based on signal segmentation and Fourier transform is proposed to address the issue of excessively large dictionary matrix (DM) by using the fast Fourier transform (FFT). Then, an orthogonal matching pursuit (OMP) algorithm based on detection threshold (DT-OMP) is proposed to adaptively determine the number of atoms to be selected by a preset threshold. Furthermore, a model mismatch correction method for SR (MMC-SR) is proposed to achieve accurate estimation of target parameters in off-grid situations. Simulations and practical experiments have shown that the proposed method can effectively mitigate the influence of wider main lobe and higher sidelobes of CAF, thereby improving resolution and providing a refined estimation of target parameters, showcasing significant practical application value.

KW - Cross-ambiguity function (CAF)

KW - off-grid

KW - orthogonal matching pursuit (OMP)

KW - passive radar (PR)

KW - sparse representation (SR)

UR - https://www.scopus.com/pages/publications/105007604589

U2 - 10.1109/JSEN.2025.3573568

DO - 10.1109/JSEN.2025.3573568

M3 - Article

AN - SCOPUS:105007604589

SN - 1530-437X

VL - 25

SP - 25288

EP - 25300

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

IS - 13

ER -