A High-Precision Frequency Parameter Estimation Method Based on Time Shift and Phase Difference
A parameter estimation and phase difference technology, applied in the field of signal processing, can solve the problems of reduced calibration accuracy and not applicable to all frequencies, and achieve good anti-noise performance
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
example 1
[0137] In practical engineering applications, signals are often disturbed by noise. In order to evaluate the impact of noise on the improved algorithm, the theoretical signal with additive Gaussian white noise is given by formula (1). Amplitude A 0 Set to 1, the sampling frequency is 256Hz, and the total data length is 256 points, so the frequency resolution is 1Hz. The frequency changes with a step distance of 0.1 within the interval -0.5 to 0.5. For each frequency step, the phase is scanned in the range -π~π with a step distance of π / 36. The signal-to-noise ratio (SNR) changes with a step distance of 5dB within the range of 0dB to 40dB. The root mean square error (RMSE) is used to evaluate the improved algorithm, and it is compared with the Cramereau lower bound (CRLB) of frequency estimation root mean square error. figure 1 Shown is the RMSE value of the improved algorithm based on the Hanning window, where s a =s 2 -s 1 .
[0138] As shown in the figure, overall the...
example 2
[0140] In order to verify the applicability of the improved algorithm, image 3 Shown is the RMSE simulation result based on the improved algorithm of rectangular window. The parameters of the simulated signal are the same as those in Example 1.
[0141] From image 3 It can be seen that with s 1 and s a The correction accuracy is improved with the increase of , and generally the simulation results based on the rectangular window are better than those based on the Hanning window. But when the SNR is greater than 30, the correction effect is not ideal.
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


