FRI sparse sampling core function building method and circuit

Abstract

The invention discloses an FRI sparse sampling core function building method and an FRI sparse sampling core function building circuit. According to the method, the condition that a sampling core needs to meet is determined according to the features of an analog input signal and a subsequent parameter estimation algorithm, a frequency response function of a Fourier series coefficient screening circuit is designed, a performance parameter of the sampling core response function is determined, and a sampling core function is acquired after correction. The circuit is achieved by a Fourier series coefficient screening module and a phase correction module. The Fourier series coefficient screening module adopts a Chebyshev II type lowpass filtering circuit, and the phase correction module adopts all-pass filtering circuit. Signals can be directly sparsely sampled according to the information innovation rate after passing through the sampling core circuit, and original feature parameters can be accurately recovered via a parameter estimation algorithm after sparse data is acquired. The limited innovation rate sparse sampling core provided by the invention is particularly applicable to an FRI sparse sampling system for pulse flow signals, a sampling rate is far lower than a general Nyquist sampling rate, and a data collection amount is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of signal sparse sampling, and in particular relates to a method for constructing a kernel function of a sampling kernel in FRI sparse sampling of a pulse stream signal and a hardware circuit implementation. Background technique [0002] Finite Rate of Innovation (FRI) sampling theory is a new sparse sampling method proposed by Vetterli et al. in 2002. The sampling theory performs sparse sampling on the FRI signal at a rate much lower than the Nyquist sampling frequency, and can accurately reconstruct the original signal. When this method was first proposed, it theoretically solved the sparse sampling problem of four types of non-band-limited signals: Dirac flow signal, differential Dirac flow, non-uniform spline, and piecewise polynomial. It performs sparse sampling, and then estimates the signal amplitude and delay parameters through the spectrum analysis algorithm, and finally reconstructs the time domai...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the FRI sparse sampling core of the pulse stream signal has no physical realization, the present invention provides a physical realization method and circuit

Images