Symbol rate estimation method of discontinuous phase 2FSK signal based on FPGA

Abstract

The invention discloses a non-continuous phase 2FSK signal symbol rate estimation method based on an FPGA, and the method comprises the steps: obtaining non-continuous phase 2FSK baseband signal data, selecting a carrier frequency, and extracting a carrier frequency parameter; respectively carrying out secondary down-conversion on an I path and a Q path of discontinuous phase 2FSK baseband signal data through carrier frequency parameters by utilizing a DDS, an adder and a multiplier IP core of the FPGA to obtain corresponding output results; respectively performing low-pass filtering on the I-path output result and the Q-path output result by using a filter IP core of the FPGA; obtaining an envelope power spectrum of the discontinuous phase 2FSK baseband signal through calculation according to the I-path output result and the Q-path output result after low-pass filtering by means of an FFT IP core of the FPGA; performing spectral line highlighting processing on the envelope power spectrum of the discontinuous phase 2FSK baseband signal, extracting a symbol rate spectral line from a processing result according to a threshold value, and calculating the symbol rate of the symbol rate spectral line according to the current sampling rate and the current FFT point number. According to the invention, the complexity of symbol rate estimation is greatly reduced, and hardware resources of the FPGA can be saved.

Description

technical field [0001] The present invention relates to the field of electronic information technology, in particular to a method for estimating the symbol rate of an FPGA-based discontinuous phase 2FSK signal. Background technique [0002] The existing 2FSK modulated signal symbol rate estimation algorithms are mostly divided into two categories: the first category uses wavelet transform to extract the local frequency change moment in the signal through wavelet transform, so as to obtain the symbol rate and jump time. Some literature proposes to use two haar wavelet transforms to extract the symbol jump time, and then use Fourier transform to obtain the symbol rate, but the error is large under low signal-to-noise ratio, and the wavelet transform scale has a great influence on the result and is affected by frequency offset , symbol rate and other influences make it difficult to select the wavelet transform. On the basis of the above processing process, morlet wavelet is fu...

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Problems solved by technology

Some literature proposes to use two haar wavelet transforms to extract the symbol jump time, and then use Fourier transform to obtain the symbol rate, but the error is large under low signal-to-noise ratio, and the wavelet transform scale has a great influence on the result and is affected by frequency offset , symbol rate and other influences make it difficult to select the wavelet transform

On the basis of the above processing process, morlet wavelet is further used to extract the ridge line, and the short-time variance is calculated to extract the discrete spectral line, which improves the anti-noise performance of the algorithm to a certain extent, but there are still difficulties in parameter selection and anti-noise performance. Poor problem, and the implementation of the above algorithms on FPGA is relatively complicated, and consumes more hardware resources

The second category uses the cyclostationary statistical properties of digitally modulated signals and adopts cyclic autocorrelation processing to obtain the peak value containing symbol rate information, and obtain the symbol rate by detecting the peak value. The performance is not stable enough, and the algorithm complexity is very high, which is not suitable for FPGA implementation

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