Research on Trenching Data Correction Method Based on Wavelet Denoising-Kalman Filtering Algorithm

To realize monitoring of trenching depth, this paper built an online monitoring system and measured depth at three levels of 25 cm, 30 cm, and 35 cm. The test results show that the measured data have large fluctuations and low precision and are smaller than the actual value. For the above problems, this paper proposes a comprehensive noise reduction filtering algorithm based on wavelet noise reduction and Kalman filtering, in which the output of the preprocessing part of the wavelet noise reduction is used as the input of the postprocessing part of the Kalman filter. This article first selects the optimal combination of wavelet threshold denoising parameters such as wavelet function, decomposition level, and threshold denoising rule. Then the optimal Q/R value and the combination of Q and R parameters for the Kalman filter reprocessing part are explored. Finally, the comprehensive filtering correction algorithm and Kalman filtering algorithm are used to process the above-measured data. By comparing the results of the two algorithms, we found that for the local data, the variance decreased by 0.0207, 0.0398, and 0.0609, and the mean square deviation decreased by 0.0609, 0.0854, and 0.0457. The mean values of the two algorithms are very close. Similarly, for the global data, the variance decreased by 0.0182, 0.0549, 0.0428, and the mean square deviation decreased by 0.0359, 0.041, 0.0323. There is no significant difference in means between the two algorithms. The results show that the comprehensive algorithm improves the quality of trenching depth data and has certain significance for precision agriculture.