Dense-Frequency Signal-Detection Based on the Primal–Dual Splitting Method

Abstract

Aiming to solve the problem of dense-frequency signals in the power system caused by the growing proportion of new energy, this paper proposes a dense-frequency signal-detection method based on the primal–dual splitting method. After establishing the Taylor–Fourier model of the signal, the proposed method uses the sparse property of the coefficient matrix to obtain the convex optimization form of the model. Then, the optimal solution of the estimated phasor is obtained by iterating over the fixed-point equation, finally acquiring the optimal estimation result for the dense signal. When representing the Taylor–Fourier model as a convex optimization form, the introduction of measuring-error entropy makes the solution of the model more rigorous. It can be further verified through simulation experiments that the estimation accuracy of the primal–dual splitting method proposed in this paper for dense signals can meet the M-class PMU accuracy requirements.

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

“…The non-cooperative detection of the FHSS signal is the first step of the entire signal interception procedure [2]. Although various methods has been rendered since the 1990s (e.g., methods based on time-frequency analysis [3][4][5][6][7][8], wavelet analysis [4,[9][10][11][12][13], auto-correlation analysis [9,[14][15][16], likelihood analysis [17][18][19][20][21], etc. ), energy thresholding is the most commonly used in FHSS signal detection [22][23][24].…”