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2021
DOI: 10.3390/s21155057
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A Spectrum Correction Algorithm Based on Beat Signal of FMCW Laser Ranging System

Abstract: The accuracy of target distance obtained by a frequency modulated continuous wave (FMCW) laser ranging system is often affected by factors such as white Gaussian noise (WGN), spectrum leakage, and the picket fence effect. There are some traditional spectrum correction algorithms to solve the problem above, but the results are unsatisfactory. In this article, a decomposition filtering-based dual-window correction (DFBDWC) algorithm is proposed to alleviate the problem caused by these factors. This algorithm red… Show more

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Cited by 6 publications
(2 citation statements)
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“…However, these methods are difficult to realize and take up a lot of resources. In addition, based on discrete wavelet packet transformation [ 21 ], all-phase [ 22 ], decomposition filtering-based dual-window correction algorithms [ 23 ], autocorrelation [ 24 ], and neural network methods [ 25 ], the frequency estimation can be realized. The influence of white noise on frequency estimation has also been analyzed [ 26 , 27 ].…”
Section: Introductionmentioning
confidence: 99%
“…However, these methods are difficult to realize and take up a lot of resources. In addition, based on discrete wavelet packet transformation [ 21 ], all-phase [ 22 ], decomposition filtering-based dual-window correction algorithms [ 23 ], autocorrelation [ 24 ], and neural network methods [ 25 ], the frequency estimation can be realized. The influence of white noise on frequency estimation has also been analyzed [ 26 , 27 ].…”
Section: Introductionmentioning
confidence: 99%
“…To date, a 100 kHz-level measuring rate has been realized with micrometer-scale resolution [8]. Reliable precision is guaranteed by more accurate system calibration [18] and more effective signal analysis algorithms [19,20]. Additionally, the use of silicon photonics chips and embedded digital signal processors makes compatible and real-time measurements possible [21][22][23].…”
Section: Introductionmentioning
confidence: 99%