Integrated Detection and Imaging Algorithm for Radar Sparse Targets via CFAR-ADMM

Li, Pucheng; Ding, Zegang; Zhang, Tianyi; Wei, Yangkai; Gao, Yongpeng

doi:10.1109/tgrs.2023.3251732

Cited by 3 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, there are two kinds of algorithms to solve the reconstruction problem of the CS signal, i.e., the greedy algorithm [57,58] and the convex optimization algorithm [59][60][61]. Among them, the greedy algorithm is generally a heuristic method, which gradually builds the global optimal solution by selecting the local optimal solution every time.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

ADMM-1DNet: Online Monitoring Method for Outdoor Mechanical Equipment Part Signals Based on Deep Learning and Compressed Sensing

Hu,

Guo,

Rui

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what differs from other unfolded networks is that ADMM-1DNet learns a redundant analysis operator, which can reduce the impact of outdoor high noise on reconstruction error by improving the signal sparse level. The implementation scheme includes the field operation of mechanical equipment and the operation of the data center. The empirical network trained by the local data center conducts an online reconstruction of the received outdoor vibration signal data. Experiments are conducted on two open-source bearing datasets, which verify that the proposed method outperforms the baseline method in terms of reconstruction accuracy and feature preservation, and the proposed implementation scheme can be adapted to the needs of different types of vibration signal reconstruction tasks.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

ADMM-1DNet: Online Monitoring Method for Outdoor Mechanical Equipment Part Signals Based on Deep Learning and Compressed Sensing

Hu,

Guo,

Rui

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…When the radar detects the ground target, in addition to the need to counter the noise, it also needs to deal with the interference from the strong ground clutter. In the process of target detection, long-distance targets with weak RCSs significantly reduce the signal-to-noise ratio of the target echo, which makes the traditional extended target detection algorithm based on radar one-dimensional range profile energy invalid [25,26]. Therefore, in order to solve this problem, it is necessary to study a new radar target detection algorithm to improve the signal-to-clutter ratio.…”

Section: Introductionmentioning

confidence: 99%

A New Extended Target Detection Method Based on the Maximum Eigenvalue of the Hermitian Matrix

Xu,

Zhu,

Song

2024

Remote Sensing

View full text Add to dashboard Cite

In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with weak radar cross sections (RCSs). In such cases, the signal-to-noise ratio (SNR) of the target echo is severely degraded, rendering the energy accumulation detection algorithm unreliable. To address this issue, this paper presents a new extended target detection method based on the maximum eigenvalue of the Hermitian matrix. This method utilizes a detection model that incorporates observed data and employs the likelihood ratio test (LRT) theory to derive the maximum eigenvalue detector at low SNR. Specifically, the detector constructs a matrix using a sliding window block with the available data and then computes the maximum eigenvalue of the covariance matrix. Subsequently, the maximum eigenvalue matrix is transformed into a one-dimensional eigenvalue image, enabling extended target detection through analogy with the energy accumulation detection method. Furthermore, this paper analyzes the proposed extended target detection method from both theoretical and experimental perspectives, validating it through field-measured data. The results obtained from the measured data demonstrate that the method effectively enhances the SNR in low SNR conditions, thereby improving target detection performance. Additionally, the method exhibits robustness across different scattering center targets.

show abstract

A Novel CFAR-Based Ship Detection Method Using Range-Compressed Data for Spaceborne SAR System

Wang,

Guo,

Song

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Integrated Detection and Imaging Algorithm for Radar Sparse Targets via CFAR-ADMM

Cited by 3 publications

References 44 publications

ADMM-1DNet: Online Monitoring Method for Outdoor Mechanical Equipment Part Signals Based on Deep Learning and Compressed Sensing

ADMM-1DNet: Online Monitoring Method for Outdoor Mechanical Equipment Part Signals Based on Deep Learning and Compressed Sensing

A New Extended Target Detection Method Based on the Maximum Eigenvalue of the Hermitian Matrix

A Novel CFAR-Based Ship Detection Method Using Range-Compressed Data for Spaceborne SAR System

Contact Info

Product

Resources

About