Purpose

The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the estimator of the quadrotor in the presence of unknown measurement noise statistics.

Design/methodology/approach

Six degree-of-freedom mathematical model of the quadrotor is derived. Position controller for the quadrotor is designed. Kalman filter-based estimation algorithm is implemented in the sensor feedback loop. Artificial bee colony-based hybrid algorithm is used as an optimization method to handle the unknown noise statistics. Existing objective function is extended with a penalty term. Mathematical proof of the extended objective function is derived. Results of the proposed algorithm is compared with de facto genetic algorithm-based Kalman filter.

Findings

Artificial bee colony algorithm-based Kalman filter and extended objective function duo are able to optimize the measurement noise covariance matrix with an absolute error as low as 0.001 [m²]. Proposed method and function is capable of reducing the noise from 2 to 0.09 [m] for x-axis, 3.4 to 0.14 [m] for y-axis and 3.7 to 0.2 [m] for z-axis, respectively.

Originality/value

The motivation behind this paper is to bring a novel optimization-based solution for the estimation problem of the quadrotor when the measurement noise statistics are unknown along with an extended objective function to prevent the infeasible solutions with mathematical convergence analysis.

中文翻译：

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基于人工蜂群的卡尔曼滤波杂交技术在四旋翼飞机三维位置估计中的应用

目的

本文的目的是介绍一种基于人工蜂群的卡尔曼滤波算法以及扩展的目标函数，以在存在未知测量噪声统计数据的情况下确保四旋翼飞行器估计器的最优性。

设计/方法/方法

推导了四旋翼的六自由度数学模型。设计了四旋翼的位置控制器。在传感器反馈回路中实现了基于卡尔曼滤波器的估计算法。基于人工蜂群的混合算法被用作处理未知噪声统计数据的一种优化方法。现有的目标函数用惩罚项扩展。得出了扩展目标函数的数学证明。将该算法的结果与基于事实遗传算法的卡尔曼滤波器进行了比较。

发现

基于人工蜂群算法的卡尔曼滤波器和扩展目标函数对，可以优化测量噪声协方差矩阵，其绝对误差低至0.001 [m ² ]。提出的方法和功能能够降低噪声从2到0.09 [米]为X轴，3.4〜0.14 [米]为ÿ -轴和3.7至0.2 [M]为Ž -轴，分别。

创意/价值

本文的目的是在测量噪声统计信息未知的情况下，为四旋翼的估计问题提供一种基于优化的新颖解决方案，并通过扩展的目标函数来防止数学收敛分析带来的不可行解决方案。