Modeling of three-dimensional radar tracking system and its estimation using Extended Kalman Filter

Arif, Didik Khusnul; Adzkiya, Dieky; Aditya, Prima; Winata, Feri; Agustin, Diah; Habibi, Milad; Ririsati, Alfiana; Prasyanto, Ramadhani

doi:10.1109/ica.2017.8068412

Cited by 1 publication

(1 citation statement)

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“…A linear dynamic Kalman filter applying an expectation maximization algorithm along with an automatic regression algorithm was proposed by Soubdhan T. et al in 2016 for the prediction the photovoltaic-aided energy generation according to initialization and probability models [38]. D. K. Arif et al, 2017 applied an extended Kalman filter in a nonlinear model to estimate the measurements of a 3D radar monitoring system [39]. Leleux D. 2002 demonstrated the effectiveness of the application of Kalman filtering for realtime gas pollutant measurements, using diode-laser overtone spectroscopy [40].…”

Section: Related Workmentioning

confidence: 99%

A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements

Christakis,

Tsakiridis,

Kandris

et al. 2023

Electronics

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Air pollution, which is mainly caused by industrialization, intensive transportation, and the heating of buildings, is one of the most important problems in large cities because it seriously harms the health and the quality of life of their citizens. This is why air quality is monitored not only by governmental organizations and official research institutions through the use of sophisticated monitoring systems but also by citizens through the use of low-cost air quality measurement devices. However, the reliability of the measurements derived from low-cost sensors is questionable, so the measurement errors must be eliminated. This study experimentally investigated the impact of the use of a Kalman filter on the accuracy of the measurements of low-cost air quality sensors. Specifically, measurements of air pollutant gases were carried out in the field in real ambient air conditions. This study demonstrates not only the optimization of the measurements through the application of a Kalman filter but also the behavior of the filter coefficients and their impact on the predicted values.

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Section: Related Workmentioning

confidence: 99%