Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

Liu, Xuhang; Liu, Xiaoxiong; Zhang, Weiguo; Yang, Yue

doi:10.1109/access.2020.2991032

Cited by 18 publications

(6 citation statements)

References 33 publications

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“…After that, the designed filter is implemented in interacting with multiple models (IMM). It is because the performance of the traditional single model KF, MCKF or MCStF deteriorates when the motion status is not single [13], [21], [31].…”

Section: Related Workmentioning

confidence: 99%

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

Candan,

Beke,

Mihaylova

2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

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Section: Related Workmentioning

confidence: 99%

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

Candan,

Beke,

Mihaylova

2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…We use the following formula to represent the system equation and observation equation calculated by the cubature point and spherical-radial rule, respectively. The specific algorithm implementation of cubature point generation and spherical-radial rule can be found in reference [25].…”

Section: Bias-free Kalman Filteringmentioning

confidence: 99%

Unmanned Aerial Vehicle Target Tracking Based on OTSCKF and Improved Coordinated Lateral Guidance Law

Wei

Lyu

Shi³

2022

IJGI

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This paper proposes an approach of target tracking of a ground target for UAVs using Optimal Two-Stage Cubature Kalman Filter and Improved Coordinated Lateral Guidance Law. Firstly, the Optimal Two-Stage Cubature Kalman Filter (OTSCKF) is proposed to estimate the target motion. The OTSCKF combines two-stage filtering technology with CKF to improve the estimation accuracy. Secondly, to keep a constant distance between the UAV and the target, a new guidance law based on the lateral turning equation is proposed and its asymptotic stability is proven. On this basis, a distributed tracking algorithm is designed to balance the phase difference and achieve cooperation among multi-UAVs. Thirdly, numerical experiments are performed for the tracking problems of moving targets and the results verify the effectiveness of the proposed guidance algorithm.

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“…For the UAV to fly steadily and perform tasks autonomously with increased efficiency and reduced human costs, providing accurate position information is crucial [3]- [5]. The Global Positioning System (GPS) is one of the most common solutions for estimating the position for UAV platforms [6]. However, GPS signals are not always available due to the possibility of being blocked by buildings, and large drifting errors affect the flying stability of the smallsized UAVs.…”

Section: Introductionmentioning

confidence: 99%

A Robust Learned Feature-Based Visual Odometry System for UAV Pose Estimation in Challenging Indoor Environments

Yang

et al. 2023

IEEE Trans. Instrum. Meas.

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Unmanned Aerial Vehicles (UAVs) are becoming popular nowadays due to their versatility and flexibility for indoor applications, such as the autonomous visual inspection for the inner surface of a pressure vessel. Nevertheless, robust and reliable position estimation is critical for completing these tasks. Visual Odometry (VO) and Visual Simultaneous Localisation and Mapping (VSLAM) allow the UAV to estimate its position in unknown environments. However, traditional feature-based VO/VSLAM systems struggle to deal with complex scenes such as low illumination and textureless environments. Replacing the traditional features with deep learning-based features provides the advantage of handling the challenging environment, but the efficiency is ignored. In this work, an efficient VO system based on a novel lightweight feature extraction network for UAV onboard platforms has been developed. The Deformable Convolution (DFConv) is utilised to improve the feature extraction capability. Owing to the limited onboard computing capability, the Depthwise Separable Convolution (DWConv) is adopted to calculate the offsets for the deformable convolution and construct the backbone network to improve the feature extraction efficiency. Experiments on public datasets indicate that the efficiency of the VO system is improved by 30.03% while preserving the accuracy on embedded platforms with the feature points and descriptors detected by the proposed Convolutional Neural Network (CNN). Moreover, the proposed VO system is verified through UAV flying tests in a real-world scenario. The results prove that the proposed VO system is able to handle the challenging environments where both the latest traditional and deep learning feature-based VO/VSLAM systems fail, and it is feasible for UAV self-localisation and autonomous navigation in the confined, low illumination and textureless indoor environment.

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Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

Cited by 18 publications

References 33 publications

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

Unmanned Aerial Vehicle Target Tracking Based on OTSCKF and Improved Coordinated Lateral Guidance Law

A Robust Learned Feature-Based Visual Odometry System for UAV Pose Estimation in Challenging Indoor Environments

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