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

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In order to achieve the major strategic goal of carbon peaking by 2030 and carbon neutralization by 2060, a new power system with new energy should be built to create a clean, low-carbon, safe and efficient energy system, and further promote the construction of the power market. It is necessary to build a peaking capacity market to ensure the safe and stable operation of the power grid. In the paper, a peaking capacity market mechanism is designed to encourage thermal power units to strengthen equipment transformation, operation and maintenance, and reduce the unscheduled shutdown of thermal power units. Key processes of the market including market demand evaluation, bidding, clearing, settlement are introduced. Based on the market operation, the power generation capacity of thermal power resources is expected to be fully enhanced during peaking periods of power system to ensure the system-wise capacity adequacy.