Global Positioning System Data Processing Improvement for Blind Tracker Device Based Using Moving Average Filter

Purnama, Sevia Indah; Afandi, Mas Aly; Purba, Egya Vernando

doi:10.1007/978-981-19-1804-9_14

Cited by 1 publication

(1 citation statement)

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“…Despite its limitations, such as its inability to preserve sudden transitions in the signal, this filter is frequently used due to its simplicity, effectiveness in reasonable data volumes and ease of implementation and comprehension (Hidayat et al, 2015;Thinh et al, 2018;Purnama et al, 2022;Ahn & Ko, 2009). The time complexity of this algorithm is linear and it is a robust and effective noise removal tool, with a reduced sensitivity to data anomalies and sudden transitions.…”

Section: Simple Moving Average Filtermentioning

confidence: 99%

Filtering Strategies for Smartphone Emitted Digital Signals

OBRETIN,

CORNEA

2024

Appl. Comput. Sci.

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In today's digitalized and technology-driven society, where the number of IoT devices and the volume of collected data is exponentially increasing, the use of sensor data has become a necessity in certain fields of activity. This paper presents a concise history of sensor evolution and specialization, with a focus on the sensors used for localization, particularly those present in microelectromechanical systems (MEMS) that make up inertial measurement units. The study starts with a general overview and progresses towards a more specific analysis of data sets collected from an accelerometer. In the materials and methods section, it emphasizes the imperfect nature of sensor measurements and the need to filter digital signals. Three different digital signal filtering techniques belonging to distinct filter categories are comparatively analyzed, with each technique having its own particularities, advantages and disadvantages. The analysis considers the effectiveness in reducing measurement errors, the impact of the filtering process on the original signal, the ability to highlight the underlying phenomenon, as well as the performance of the analyzed algorithms. The ultimate purpose of this article is to determine which filtration method is best suited for the signal at hand in the context of an indoor localization application.

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Section: Simple Moving Average Filtermentioning

confidence: 99%