Application of Particle Filter in Path-loss Modelling

• Autorzy: Wójcicki Piotr , Zientarski Tomasz , Przyłucki Sławomir

Identyfikatory

DOI

10.12913/22998624/172407

• Języki publikacji: EN

Abstrakty

EN

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

Słowa kluczowe

EN

received signal strength; Bayesian filtering; particle filter; path-loss exponent

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 5
• Strony: 337--349
• Opis fizyczny: Bibliogr. 58 poz., fig, tab.

Twórcy

autor

Wójcicki Piotr

• Department of Computer Science, Lublin University of Technology, u. Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-0522-6223

autor

Zientarski Tomasz

• Department of Computer Science, Lublin University of Technology, u. Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-1693-5316

autor

Przyłucki Sławomir

• Department of Computer Science, Lublin University of Technology, u. Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0001-9565-3802

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Uwagi

Opracowanie reOpracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).