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 - http://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 -