A FIR filter assisted with the predictive model and ELM integrated for UWB-based quadrotor aircraft localization

Xu, Yuan; Dong, Wen‐Kui; Bi, Shuhui; Guo, Hang; Zhuang, Yuan

doi:10.1186/s43020-022-00091-1

Cited by 7 publications

(6 citation statements)

References 35 publications

(28 reference statements)

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“…The ELM method used in this work is similar to the method we proposed previously [42]. In this section, we introduce this method briefly.…”

Section: Elm Methodsmentioning

confidence: 99%

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Xu,

Gao,

Yang

et al. 2023

Micromachines

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To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method.

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“…The ELM method used in this work is similar to the method we proposed previously [42]. In this section, we introduce this method briefly.…”

Section: Elm Methodsmentioning

confidence: 99%

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Xu,

Gao,

Yang

et al. 2023

Micromachines

Self Cite

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“…[26] developed a maximum correlation Kalman filter (KF) (mcKF) assisted by a dual free-size least-squares support vector machine (fLS-SVM) for fusing INS and UWB data. A finite impulse response (FIR) filter that combined predictive models and extreme learning machines (ELMs) was proposed [27] to improve the accuracy of the quadcopter positioning based on the UWB.…”

Section: Introductionmentioning

confidence: 99%

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Zhao,

Shen,

Cao

et al. 2024

Micromachines

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The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system’s performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading’s angular increment, and to fit the integrated navigation system’s dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

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“…Three-dimensional reconstruction is well established and accepted in manufacturing, automobile and construction industries, and there is keen interest shown in investigating how this can be applied in practice [1]. One of the most interesting fields is the environment reconstruction of unmanned systems such as Micro Aerial Vehicles (MAVs) in unknown environments [2]. Environment reconstruction is significant for the navigation of MAVs in indoor environments [3].…”

Section: Introductionmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.

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A FIR filter assisted with the predictive model and ELM integrated for UWB-based quadrotor aircraft localization

Cited by 7 publications

References 35 publications

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

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