Compressive Sensing of Stepped-Frequency Radar Based on Transfer Learning

Abstract

It usually suffers from long observing time and interference sensitivity when a radar transmits the high-range-resolution stepped-frequency chirp signal. Motivated by this, only partial pulses of the stepped-frequency chirp are utilized. For the obtained incomplete frequency data, a Bayesian model based on transfer learning is proposed to reconstruct the corresponding full-band frequency data. In the training phase, a complex beta process factor analysis (CBPFA) model is utilized to statistically model each aspect-frame from a set of given full-band frequency data, whose probability density function (pdf) can be learned from this CBPFA model. It is important to note that the numbers of factors and dictionaries are automatically learned from the data. The inference of CBPFA can be performed via the variational Bayesian (VB) method. In the reconstruction phase for the incomplete frequency data that "related" to the training samples, its corresponding full-band frequency data can be analytically reconstructed via the compressive sensing (CS) method and Bayesian criterion based on the transfer knowledge of the previous pdfs learned from the training phase. About the "relatedness" between each training frame and the incomplete test frequency data, we utilize the frame condition distribution of incomplete test frequency data to represent. The proposed method is validated on the measured high range resolution (HRR) data.Index Terms-Bayesian, complex beta process factor analysis, compressive sensing, stepped-frequency, transfer learning, variational inference. 1053-587X

The optimally designed autoencoder network for compressed sensing

“…The above paragraph summarizes the advantages of our work, but there are still shortcomings in our work, mainly focusing on accurately reconstructing signals with a few measurements, which requires lots of time and data for training. In further work, transfer learning, which is a convenient alternative for leveraging existing models and updating them on smaller computational platforms and target data sets [33], could be taken into account to address this issue. Additionally, there still exist some compressed sensing problems of big-size nature images; it is worthy to develop convolutional method [34] for sense images, so as to reduce the memory of measurement matrix.…”

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

“…The use of online bootstrapping machine learning tools to improve target detection rate of radar signals is also one major research area [10]. Radar data can be analyzed using the concepts of transfer learning since often we have only a small number of labelled data available while the majority of signals captured are unlabelled (nonannotated) [11]. Other works focus on modeling of ionospheric disturbances on spaceborne interferometric synthetic aperture radar (SAR) via Echo-State Networks [12, 13] or ensemble classifiers [14].…”

“…This way, we can find patterns distributed on space and time domain to improve targets detection efficiency. These methodologies can be extended to the analysis of synthetic aperture radar (SAR) images [11], or by incorporating sparsity-based signal analysis [18]. A neural network based scheme for detecting salient objects in SAR images is recently presented [19].…”

Underwater Target Signal Classification Using the Hybrid Routing Neural Network

“…The characteristic selecting modes more or less result in the missing details of the raw signal; this determines how effective underwater acoustic identification algorithms will be for particular sound data. The recognition classifiers span traditional machine learning [ 5 , 6 , 7 ], the statistic approximation method [ 8 ], and matched field [ 9 ], which depend on critical prior knowledge and professional feature design, resulting in a dilemma in higher classification precision and greater operational efficiency.…”