Backtracking Dichotomous-Least Squares-Based Sidelobe Suppression Algorithm for Multiple Targets With Straddling

Chen Ning; Jing Tian; Yanjun Zhang; Xiang Gen Xia; Wei Cui; Siliang Wu

doi:10.1109/TAES.2025.3576079

Backtracking Dichotomous-Least Squares-Based Sidelobe Suppression Algorithm for Multiple Targets With Straddling

Chen Ning
, Jing Tian^*
, Yanjun Zhang
, Xiang Gen Xia
, Wei Cui
, Siliang Wu

^*此作品的通讯作者

Beijing Institute of Technology
Southeast University, Nanjing
University of Delaware

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

The performance of sidelobe suppression methods based on on-grid signal models deteriorates significantly when target parameters deviate from the predefined discrete parameter grids. To address this issue, we propose a backtracking dichotomous-least squares (BD-LS) algorithm based on the minimum residual energy criterion. BD-LS iteratively estimates the parameters of targets whose powers are higher than the threshold used for stopping the iteration, in the descending order of the target powers. During each iteration, the coarse position of the strongest target is first estimated based on the 2-D matched filtering results of the residual signal, and then the straddling offset of the target is estimated according to the minimum residual energy criterion and the dichotomous iterative strategy. After the straddling offsets of all targets are accurately estimated, the complex amplitudes of targets are simultaneously estimated by using the LS algorithm. To mitigate the impact of target interference on the estimation of straddling offsets, multiple backtracking refinements are adopted. In each backtracking refinement processing, the straddling offsets of all the targets above a given threshold are sequentially refined by using the obtained straddling offset estimates of targets as prior information. Simulation results show that BD-LS can effectively suppress the sidelobes of targets with range–Doppler-straddling and accurately estimate the straddling offsets and powers of targets. Compared to the straddling-robust iterative adaptive filtering algorithm, BD-LS provides better sidelobe suppression performance and more accurate estimation of the straddling offsets and powers of targets with lower computational complexity in multitarget scenarios. The effectiveness of the proposed method is demonstrated by both simulated and real data.

源语言	英语
页（从-至）	12678-12701
页数	24
期刊	IEEE Transactions on Aerospace and Electronic Systems
卷	61
期	5
DOI	https://doi.org/10.1109/TAES.2025.3576079
出版状态	已出版 - 10月 2025
已对外发布	是

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10.1109/TAES.2025.3576079

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@article{18a3a99dae5e43a0a6d8b22399d81ba2,

title = "Backtracking Dichotomous-Least Squares-Based Sidelobe Suppression Algorithm for Multiple Targets With Straddling",

abstract = "The performance of sidelobe suppression methods based on on-grid signal models deteriorates significantly when target parameters deviate from the predefined discrete parameter grids. To address this issue, we propose a backtracking dichotomous-least squares (BD-LS) algorithm based on the minimum residual energy criterion. BD-LS iteratively estimates the parameters of targets whose powers are higher than the threshold used for stopping the iteration, in the descending order of the target powers. During each iteration, the coarse position of the strongest target is first estimated based on the 2-D matched filtering results of the residual signal, and then the straddling offset of the target is estimated according to the minimum residual energy criterion and the dichotomous iterative strategy. After the straddling offsets of all targets are accurately estimated, the complex amplitudes of targets are simultaneously estimated by using the LS algorithm. To mitigate the impact of target interference on the estimation of straddling offsets, multiple backtracking refinements are adopted. In each backtracking refinement processing, the straddling offsets of all the targets above a given threshold are sequentially refined by using the obtained straddling offset estimates of targets as prior information. Simulation results show that BD-LS can effectively suppress the sidelobes of targets with range–Doppler-straddling and accurately estimate the straddling offsets and powers of targets. Compared to the straddling-robust iterative adaptive filtering algorithm, BD-LS provides better sidelobe suppression performance and more accurate estimation of the straddling offsets and powers of targets with lower computational complexity in multitarget scenarios. The effectiveness of the proposed method is demonstrated by both simulated and real data.",

keywords = "Dichotomous iterative strategy, least squares, sidelobe suppression, straddling effect",

author = "Chen Ning and Jing Tian and Yanjun Zhang and Xia, \{Xiang Gen\} and Wei Cui and Siliang Wu",

note = "Publisher Copyright: {\textcopyright} 1965-2011 IEEE.",

year = "2025",

month = oct,

doi = "10.1109/TAES.2025.3576079",

language = "English",

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T1 - Backtracking Dichotomous-Least Squares-Based Sidelobe Suppression Algorithm for Multiple Targets With Straddling

AU - Ning, Chen

AU - Tian, Jing

AU - Zhang, Yanjun

AU - Xia, Xiang Gen

AU - Cui, Wei

AU - Wu, Siliang

PY - 2025/10

Y1 - 2025/10

N2 - The performance of sidelobe suppression methods based on on-grid signal models deteriorates significantly when target parameters deviate from the predefined discrete parameter grids. To address this issue, we propose a backtracking dichotomous-least squares (BD-LS) algorithm based on the minimum residual energy criterion. BD-LS iteratively estimates the parameters of targets whose powers are higher than the threshold used for stopping the iteration, in the descending order of the target powers. During each iteration, the coarse position of the strongest target is first estimated based on the 2-D matched filtering results of the residual signal, and then the straddling offset of the target is estimated according to the minimum residual energy criterion and the dichotomous iterative strategy. After the straddling offsets of all targets are accurately estimated, the complex amplitudes of targets are simultaneously estimated by using the LS algorithm. To mitigate the impact of target interference on the estimation of straddling offsets, multiple backtracking refinements are adopted. In each backtracking refinement processing, the straddling offsets of all the targets above a given threshold are sequentially refined by using the obtained straddling offset estimates of targets as prior information. Simulation results show that BD-LS can effectively suppress the sidelobes of targets with range–Doppler-straddling and accurately estimate the straddling offsets and powers of targets. Compared to the straddling-robust iterative adaptive filtering algorithm, BD-LS provides better sidelobe suppression performance and more accurate estimation of the straddling offsets and powers of targets with lower computational complexity in multitarget scenarios. The effectiveness of the proposed method is demonstrated by both simulated and real data.

AB - The performance of sidelobe suppression methods based on on-grid signal models deteriorates significantly when target parameters deviate from the predefined discrete parameter grids. To address this issue, we propose a backtracking dichotomous-least squares (BD-LS) algorithm based on the minimum residual energy criterion. BD-LS iteratively estimates the parameters of targets whose powers are higher than the threshold used for stopping the iteration, in the descending order of the target powers. During each iteration, the coarse position of the strongest target is first estimated based on the 2-D matched filtering results of the residual signal, and then the straddling offset of the target is estimated according to the minimum residual energy criterion and the dichotomous iterative strategy. After the straddling offsets of all targets are accurately estimated, the complex amplitudes of targets are simultaneously estimated by using the LS algorithm. To mitigate the impact of target interference on the estimation of straddling offsets, multiple backtracking refinements are adopted. In each backtracking refinement processing, the straddling offsets of all the targets above a given threshold are sequentially refined by using the obtained straddling offset estimates of targets as prior information. Simulation results show that BD-LS can effectively suppress the sidelobes of targets with range–Doppler-straddling and accurately estimate the straddling offsets and powers of targets. Compared to the straddling-robust iterative adaptive filtering algorithm, BD-LS provides better sidelobe suppression performance and more accurate estimation of the straddling offsets and powers of targets with lower computational complexity in multitarget scenarios. The effectiveness of the proposed method is demonstrated by both simulated and real data.

KW - Dichotomous iterative strategy

KW - least squares

KW - sidelobe suppression

KW - straddling effect

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JO - IEEE Transactions on Aerospace and Electronic Systems

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ER -

Backtracking Dichotomous-Least Squares-Based Sidelobe Suppression Algorithm for Multiple Targets With Straddling

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