An Improving position method using Extended Kalman filter

Malki, Hanan H. Al; Moustafa, Abdellatif I.; Sinky, Mohammad H.

doi:10.1016/j.procs.2021.02.005

Cited by 9 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example in [18] the use of 5G cellular system is proposed as the technology used by different combinations of sensors inside the vehicle that can work together with existing positioning and mapping systems to obtain accurate positioning for different ranges of weather conditions. The authors in [19] propose an extended version of a Kalman filter for enhancing vehicles positioning and predicting their future locations. In [20] a strategy, based on the communication between vehicles and RSUs and on the elaboration of the average RSS, is proposed for the cases when the traditional satellite techniques are not able to work.…”

Section: A Positioning Techniques In Vehicular Networkmentioning

confidence: 99%

See 1 more Smart Citation

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

D’Alfonso,

De Rango,

Fedele

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

In this paper, a Cooperative Multi-technology Simultaneous Localization and Signal Mapping (CM-SLASM) technique is proposed to improve the signal map accuracy and to build on-thefly a signal map to be used also in indoor environments where conditions can change over time. Moreover, the CM-SLASM combines WiFi, Ultra WideBand (UWB) and LIght Detection And Ranging (LIDAR) signals to improve positioning estimation by sharing information and cooperation among vehicles through Vehicle-To-Vehicle (V2V) communication links. In particular, LIDAR-based distance between vehicles is shared among neighbor vehicles to improve the vehicle positioning estimated by an Extended Kalman Filter (EKF) where WiFi fingerprinting is combined with UWB multilateration. The overall solution where EKF estimation allows to build more precise signal MAP is validated by simulation in a defined indoor scenario where vehicles equipped with different percentages of LIDAR, and different quantities of UWB and WiFi emitters have been considered. The proposed strategy has been validated through an extensive simulation campaign in various scenarios of interest and through a real-world experiment conducted in a laboratory test environment.

show abstract

Section: A Positioning Techniques In Vehicular Networkmentioning

confidence: 99%

“…75 (19) where E indicates the generator of Erlang-distributed samples, and the P ois random variable extracts the number of shot events occurring at sample k. Clearly, all this is valid for a sampling rate of 75 Hz, changing it the parameters of the distributions should be scaled accordingly.…”

Section: Lidar Error Modelmentioning

confidence: 99%

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

D’Alfonso,

De Rango,

Fedele

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…Deepsort [11] is for multi-target tracking algorithm and its core algorithm is divided into Kalman Filter [12] and Hungarian Algorithm [13] . Kalman filter is used to predict the motion trajectory of the detected object and the Hungarian technique is used to match the target with the newly detected target.…”

Section: Deepsort-based Target Tracking Modulementioning

confidence: 99%

Design and implementation of indoor smokers detection and alarm system based YOLOv5

Liu

Lin

et al. 2022

Second International Symposium on Computer Technology and Information Science (ISCTIS 2022)

View full text Add to dashboard Cite

In order to solve the automatic detection and alarm of indoor smokers, this paper designs and implements an indoor smoker detection and alarm system based on YOLOv5 model. This paper uses YOLOv5 as the target recognition model to complete the recognition of cigarettes and human objects, uses Deepsort target tracking algorithm to achieve the determination of smoking action, and combines Facenet face recognition algorithm and sklearn framework and other technologies to achieve face recognition and alarm function. The test experiment proves that the accuracy rate of the smoking person detection and alarm system based on the method of this paper is high and has certain practical and promotion prospects.

show abstract

“…In these papers, was found that China is the country that most studies the algorithms for mobile robots and the timeline is shown in figure 1. The most used algorithm is the EKF, used in 50% of the cases, so the objective of this research is to use the EKF to make a mathematical model for the mobilization of a robot [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical model simulation based on EKF for mobile robots

Almendares

Ordonez-Avila

Магомедов

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the technological advance grows the need for optimization in the tasks performed by mobile robots, these must be endowed with autonomy and other decisions, this is because the operating environments become more complex and with obstacles and objectives that change position, adding to this the interferences of the workspace such as disturbances in the environment and other system noises. To address this problem, the use of navigation algorithms, route creation, position estimation, path tracking, object recognition and others are used. In the present study, a spiral methodology was used with which a mathematical model based on the extended Kalman filter (EKF) was developed for trajectory tracking and position estimation of a differential robot using its kinematics. For the validation of the mathematical model Matlab was used together with CoppeliaSim where the different test scenarios were carried out, later the results of EKF are compared with those of Kevin Passino’s algorithm, finding superior in most aspects to the EKF except in the time when Kevin Passino’s algorithm achieves a shorter simulation time.

show abstract

An Improving position method using Extended Kalman filter

Cited by 9 publications

References 24 publications

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

Design and implementation of indoor smokers detection and alarm system based YOLOv5

Hierarchical model simulation based on EKF for mobile robots

Contact Info

Product

Resources

About