Micro-Doppler Analysis of Target Based on the Clustering Prior to Solve the Block Sparse Forward and Backward TVAR Model

Micro-Doppler features generated by target micromotion are usually unique and can be used as an important basis for target recognition, which reflects the fine features of target. In this paper, we propose an algorithm based on cluster-structured prior to solve block sparse forward and backward time...

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Veröffentlicht in:Proceedings of the IEEE Radar Conference S. 1475 - 1478
Hauptverfasser: Jin, Shuojing, Hong, Ling, Dai, Fengzhou
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 15.12.2021
Schlagworte:
Analytical models
Bayes methods
block sparsity
cluster-structured prior
Clustering algorithms
EBSBL
Electromagnetics
forward and backward TVAR model
Micro-Doppler feature
Radar
Simulation
Target recognition
ISSN:2640-7736
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Zusammenfassung:Micro-Doppler features generated by target micromotion are usually unique and can be used as an important basis for target recognition, which reflects the fine features of target. In this paper, we propose an algorithm based on cluster-structured prior to solve block sparse forward and backward time-varying autoregressive (TVAR) model to perform Micro-Doppler analysis of the target. Firstly, convert the time-varying coefficients into the form of time-invariant coefficients. Then, an improved algorithm based on extended block sparse Bayesian learning (EBSBL) is used to solve the time-invariant block sparse coefficients by adopting a cluster-structured prior. At the same time, the priori information with known boundary is applied. Finally, the electromagnetic simulation results show that the proposed algorithm has advantages in Micro-Doppler analysis, which can not only give a clearer TF diagram than traditional methods, but also has a strong anti-noise performance.
ISSN:2640-7736
DOI:10.1109/Radar53847.2021.10028253