Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning
Year: 2025
Type: article
Abstract: Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering vectors corresponding to atoms with high power values. Then, a small-scale auxiliary dictionary is constructed with a stepwise search approach to calibrate the uniformly discretized dictionary. In this way, the atoms of the auxiliary dictionary can converge to the clutter ridge adaptively when off-grid occurs. The clutter plus noise covariance matrix is estimated via SBL combined with the updated dictionary. The experimental results demonstrate that the proposed algorithm can effectively suppress the clutter ridge expansion caused by the off-grid problem while reducing the computation burden significantly compared with the existing methods.
Source: Electronics
Authors: Zhiqi Gao, Na Yang, Pingping Huang, Wei Xu, Weixian Tan, Zhixia Wu
Institution: Inner Mongolia University of Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4411155937