基于卡尔曼滤波融合算法的深松耕深检测装置研究
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国家重点研发计划项目(2017YFD0701103)


Study of Tillage Depth Detecting Device Based on Kalman Filter and Fusion Algorithm
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    摘要:

    为提高实时检测耕深的准确性,设计了基于超声波传感器和红外传感器以及卡尔曼滤波融合算法的耕深检测装置,采用超声波传感器通过渡越时间法测量耕深,采用红外传感器通过三角测距法测量耕深,通过卡尔曼滤波融合算法滤除两传感器检测数据中的杂波,并进行融合。室内试验表明,在平整地面,红外传感器检测效果优于超声波传感器;在秸秆覆盖地面,超声波传感器检测效果优于红外传感器。经卡尔曼滤波融合后的数据能充分利用两传感器在不同环境中检测的有效数据。在设定耕深为30cm和40cm的田间试验中,超声波传感器滤波数据的平均值分别为29.51cm和38.79cm,深松深度变异系数分别为2.51%和3.10%;红外传感器滤波数据的平均耕深分别为32.06cm和41.52cm,深松深度变异系数分别为2.41%和2.76%;而经卡尔曼滤波融合后的数据平均耕深分别为30.06cm和39.95cm,深松深度变异系数分别为1.07%和1.00%,说明采用滤波融合后的检测数据比单个传感器更能准确检测耕深和反映耕深变化趋势。

    Abstract:

    The tillage depth had a significant influence on root growth of crops, energy consumption of agricultural implements and tillage quality in soil tillage. It was extremely difficult to measure tillage depth accurately in real time because of straw mulching and roughness in the field as well as the vibration of tractor. A tillage depth detecting device based on Kalman filter and data fusion was designed in order to improve the accuracy of tillage depth detecting in real time. An ultrasonic sensor and an infrared sensor detecting tillage depth were used by time of flight and triangulation measurement, respectively. The data from two sensors were filtered and fused through Kalamn filter and data fusion algorithm. Besides, capacitance touch screen and memory chip were added in this device for inputting and outputting data expediently and further research. The detecting results of the infrared sensor were superior to that of ultrasonic sensor in even ground and the results were contrary in straw mulching ground. The data filtered and fused by Kalman filter could make full advantage of the useful data of two sensors in varied environments. The averages of ultrasonic sensor filtered data were 29.51cm and 38.79cm, the coefficients of variations of tillage depth were 2.51% and 3.10% in field experiment when the standard tillage depth were 30cm and 40cm. The averages of infrared sensor filtered data were 32.06cm and 41.52cm, the coefficients of variation of tillage depth were 2.41% and 2.76% under the same conditions. The averages of Kalman filtered and fused data were 30.06cm and 39.95cm, the coefficients of variation of tillage depth were 1.07% and 1.00%. The device used filtered and fused detecting data had better performances in detecting tillage depth accurately and its trend than that of single sensor.

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蒋啸虎,佟金,马云海,李金光,吴宝广,孙霁宇.基于卡尔曼滤波融合算法的深松耕深检测装置研究[J].农业机械学报,2020,51(9):53-60. JIANG Xiaohu, TONG Jin, MA Yunhai, LI Jinguang, WU Baoguang, SUN Jiyu. Study of Tillage Depth Detecting Device Based on Kalman Filter and Fusion Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(9):53-60.

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  • 收稿日期:2019-12-22
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  • 在线发布日期: 2020-09-10
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