Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Urrea, Claudio; Agramonte, Rayko

doi:10.1155/2021/9674015

Cited by 76 publications

(28 citation statements)

References 80 publications

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“…This method also helps in reducing unwanted noise by inaccurate detections. The current position of the object can be predicted by its previous moment position [32].…”

Section: Object Detection Tracking Techniquesmentioning

confidence: 99%

SSDT: Distance Tracking Model Based on Deep Learning

Gupta

Gill

Gulia

2022

Int. j. electr. comput. eng. syst. (Online)

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Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster- RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms.

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“…This method also helps in reducing unwanted noise by inaccurate detections. The current position of the object can be predicted by its previous moment position [32].…”

Section: Object Detection Tracking Techniquesmentioning

confidence: 99%

SSDT: Distance Tracking Model Based on Deep Learning

Gupta

Gill

Gulia

2022

Int. j. electr. comput. eng. syst. (Online)

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“…Фільтр Калмана є фільтром з нескінченною імпульсною характеристикою та містить два етапи -прогнозування та корекцію [15]. Рівняння прогнозування -прогнозує стан інформації, що надійде до входу фільтру в момент часу t наведено у формулі:…”

Section: дослідження методів фільтрації даних з тензометричного датчикаunclassified

System of Remote Weight Monitoring for Beekeeping

Shylov

Yamnenko

2022

Microsyst., Electron. & Acoust.

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Existing remote weight monitoring systems in beekeeping have disadvantages: lack of scaling of the system within the apiary, limited use in conditions of poor mobile communication. The use of LoRa energy-efficient long-distance radio communication technologies is proposed. The architecture of the developed remote weight monitoring system for beekeeping has a "star" structure and consists of two types of devices: a master station, an end device with two-way data exchange. The master station - provides communication between the end devices of the system and the user through the mobile network. The end device - provides collection of data on the weight of the hive, ambient air temperature, data processing, data transmission to the master station. The parameters of the LoRa radio communication network are selected: propagation coefficient, coding speed, bandwidth frequencies of the communication channel. The time required for the transfer of one data packet between the end device and the master station is calculated. The results of the study of methods of filtering continuous noise and impulse noise of strain gauge weight sensor data using the Python programming language are presented: moving average filter, median filter, exponential average filter, Kalman filter. According to the results of the research of digital filtering methods, the final device of the remote weight monitoring system for beekeeping uses an exponential average filter with effective filtering of continuous noise and impulse noise. The schematic diagrams of the master station and the final device are presented. The hardware part of the master station consists of three functional blocks: The main functional block is a microcontroller; the communication unit consists of a mobile network modem and a LoRa radio communication modem; the functional power supply is represented by a lithium-ion battery and a solar panel. The final device is built using a microcontroller, an external analog-to-digital converter, a high-precision strain-gauge weight sensor, and a LoRa radio modem. The final device is powered by a lithium-ion battery and a solar panel. The devices of the remote weight monitoring system for beekeeping are built according to the modular and hierarchical principle.

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“…In order to develop an effective computational algorithm that provides a real-time adaptive assessment of the system state under the uncertainty of the measurement matrix, two assumptions are made. The first is on the interval between accurate measurements, the error δH in determining the measurement matrix is constant, and the second is that its variations of the smallness second-order δ (2) H can be neglected. These assumptions allow us to use for the development of the desired algorithm the method of studying disturbed multidimensional linear systems described by Chernov and Yastrebov [35].…”

Section: Task Definitionmentioning

confidence: 99%

“…To assess the stochastic systems state, a significant number of different algorithms and techniques [1] have been developed, among which one of the most effective is the Kalman filter [2]. However, often, a problem arises with the practical application of the Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

et al. 2021

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Currently, one of the most effective algorithms for state estimation of stochastic systems is a Kalman filter. This filter provides an optimal root-mean-square error in state vector estimation only when the parameters of the dynamic system and its observer are precisely known. In real conditions, the observer’s parameters are often inaccurately known; moreover, they change randomly over time. This in turn leads to the divergence of the Kalman estimation process. The problem is currently being solved in a variety of ways. They include the use of interval observers, the use of an extended Kalman filter, the introduction of an additional evaluating observer by nonlinear programming methods, robust scaling of the observer’s transmission coefficient, etc. At the same time, it should be borne in mind that, firstly, all of the above ways are focused on application in specific technical systems and complexes, and secondly, they fundamentally do not allow estimating errors in determining the parameters of the observer themselves in order to compensate them for further improving the accuracy and stability of the filtration process of the state vector. To solve this problem, this paper proposes the use of accurate observations that are irregularly received in a complex measuring system (for example, navigation) for adaptive evaluation of the observer’s true parameters of the stochastic system state vector. The development of the proposed algorithm is based on the analytical dependence of the Kalman estimate variation on the observer’s parameters disturbances obtained using the mathematical apparatus for the study of perturbed multidimensional dynamical systems. The developed algorithm for observer’s parameters adaptive estimation makes it possible to significantly increase the accuracy and stability of the stochastic estimation process as a whole in the time intervals between accurate observations, which is illustrated by the corresponding numerical example.

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Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Cited by 76 publications

References 80 publications

SSDT: Distance Tracking Model Based on Deep Learning

SSDT: Distance Tracking Model Based on Deep Learning

System of Remote Weight Monitoring for Beekeeping

Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

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