Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

Bi, Shuhui; Ma, Liyao; Shen, Tao; Xu, Yuan; Li, Fukun

doi:10.7717/peerj-cs.630

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…ii jj ij ji (10) Based on ( 8)- (10), it is possible to calculate the actual x and y coordinates, which is graphically displayed in Fig. 1.…”

Section: R R H H H H H H Ij Jimentioning

confidence: 99%

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FANET management process simulation at the deployment and operation stage

Bieliakov,

Fesenko

2023

TAPR

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The object of the study is the process of managing the air network of air communication platforms of the FANET class (Flying Ad-Hoc Network), which is a component of the ground-air communication network, and which is performed on rotary unmanned aerial vehicles (UAVs) of the mini class, at the stage of deployment and operational management. The scientific research is aimed at the managing process formalization of aerial communication platforms of the air communication network in the implementation of two classes of management tasks – the class of traffic management tasks and the class of communication tasks. The analysis of this subject area showed that the management tasks at the stage of deployment and operational management of the air subnet are a multi-parameter optimization task and require the formation of control solutions at the OSI physical, channel and network levels, open systems interaction model. Tasks related to the adaptive management of radio coverage in zones (geographic areas of the area), including the clustering of terrestrial subscribers (communication nodes), were not considered, and relate to processes at the transport and application levels. At the same time, the article shows the mathematical apparatus of the approach to the compensation of the deviations of the trajectory of an unmanned aerial vehicle (UAV) in the conditions of a directional obstacle, which will allow the formation of control solutions for adaptive control, directional patterns at the output of the transmission path. Such compensation is carried out using methods of algorithmic exchange of probes (messages) between the mobile base station and communication platforms with a certain periodicity – solutions at the channel and network levels, as well as the use of Multi User MIMO technologies. This technology allows for information exchange with several client devices at the same time, and not sequentially, sending probes to several spacecraft on one channel, using several transmitting and receiving antennas, and the calculation of channel coefficients allows to estimate the azimuthal angle of deviation and the angle of elevation.

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“…ii jj ij ji (10) Based on ( 8)- (10), it is possible to calculate the actual x and y coordinates, which is graphically displayed in Fig. 1.…”

Section: R R H H H H H H Ij Jimentioning

confidence: 99%

“…Articles [9,10] show the use of neural network algorithms to increase the accuracy and speed of operation of the intelligent flight control system developed on the basis of Kalman filtering algorithms.…”

Section: Introductionmentioning

confidence: 99%

FANET management process simulation at the deployment and operation stage

Bieliakov,

Fesenko

2023

TAPR

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“…However, studies like [33] predict absolute state vectors instead of vector increments using NN, which increases model complexity and requires a more extensive training process. Studies from [29,[32][33][34][35][36][37][38][39][40][41][42][43][44][45] adopted vector increments of the sensor observations and predictions during KF prediction, whilst most of the work only works on GNSS/INS navigation during GNSS outages, aiming for improving INS efficiency INS in urban settings and situations [31,38,39,41,42,46].…”

Section: Hybrid Fusion Enhanced By Aimentioning

confidence: 99%

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

Tabassum,

Xu,

Petrunin

et al. 2023

Aerospace

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To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.

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“…To address the limitations of standalone INS and overcome the data gaps in Kinect measurements [23,24], a previous study proposed the use of the extreme learning machine (ELM) algorithm to establish new signals through mapping when UWB signals are interrupted [25]. This allows the entire system to properly function.…”

Section: Introductionmentioning

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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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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Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

Cited by 10 publications

References 23 publications

FANET management process simulation at the deployment and operation stage

FANET management process simulation at the deployment and operation stage

Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments

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

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