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六自由度无人机姿态跟踪中无运动无积分技术与卡尔曼滤波器融合在噪声优化中的应用

Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking.

机构信息

Department of Engineering and Architecture, University of Parma, 43124 Parma, PR, Italy.

Department of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, Italy.

出版信息

Sensors (Basel). 2023 Jun 15;23(12):5603. doi: 10.3390/s23125603.

DOI:10.3390/s23125603
PMID:37420768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10304859/
Abstract

The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone's roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°.

摘要

本文研究了将非零运动不积分滤波器 (NMNI) 和卡尔曼滤波器 (KF) 相结合，以优化无人机在运行过程中的定向角的振动控制。分析了在噪声影响下，仅使用加速度计和陀螺仪的无人机滚转、俯仰和偏航运动。使用带有 Matlab/Simulink 软件包的六自由度 (DoF) Parrot Mambo 无人机对 NMNI 与 KF 融合前后的改进进行了验证。通过控制无人机螺旋桨电机以适当的速度水平，使无人机保持在零倾斜地面上，以验证角度误差。实验结果表明，KF 可以成功地最小化倾斜变化，但仍需要 NMNI 的支持，以提高在噪声消除方面的性能，误差仅约为 0.02°。此外，NMNI 算法成功地防止了由于零值积分而导致的偏航/航向陀螺仪漂移，最大误差为 0.03°。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/c4e9c38b50f1/sensors-23-05603-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/bbce9f36ce7d/sensors-23-05603-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/97825b5c1f21/sensors-23-05603-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/9563a7ff51c1/sensors-23-05603-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/31b706c89760/sensors-23-05603-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/eb1cdccaaf14/sensors-23-05603-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/ab77ce141364/sensors-23-05603-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/7bcfce11b199/sensors-23-05603-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/9fcad4c1670d/sensors-23-05603-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/c4e9c38b50f1/sensors-23-05603-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/bbce9f36ce7d/sensors-23-05603-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/674959510188/sensors-23-05603-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/e200cac894f8/sensors-23-05603-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/bdc85ea86acb/sensors-23-05603-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/6861ea1c9ad5/sensors-23-05603-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/97825b5c1f21/sensors-23-05603-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/9563a7ff51c1/sensors-23-05603-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/31b706c89760/sensors-23-05603-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/eb1cdccaaf14/sensors-23-05603-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/ab77ce141364/sensors-23-05603-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/7bcfce11b199/sensors-23-05603-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/9fcad4c1670d/sensors-23-05603-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79b8/10304859/c4e9c38b50f1/sensors-23-05603-g014.jpg

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