Collection of Correlated Information from Superimposed Multiple Chirp Signals

Wireless Sensor Networks (WSNs) based on Long-Range Wide Area Networks (LoRaWAN) are being used in various applications. In WSNs, in addition to periodic packet transmission, sensor nodes transmit packets when they detect an event. This action enables real-time information aggregation. When multiple sensor nodes detect an event at the same time, they send signals simultaneously, which may result in signal collisions at the receiver. This paper proposes a symbol detection method that allows information aggregation even when receiving superimposed chirp signals. Furthermore, this paper introduces an event source estimation method as a use case for collecting aggregate correlated information. Computer simulations show that the proposed method increases the probability of achieving the absolute source estimation error of 10 [deg.] by 11 points compared to conventional methods.

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Publication Details

Published
2024-12-03
DOI
https://doi.org/10.1109/apsipaasc63619.2025.10849298
Primary Topic
Blind Source Separation Techniques
Type
article
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article

Collection of Correlated Information from Superimposed Multiple Chirp Signals

Koichi Adachi, Koki Aoyama
Blind Source Separation Techniques
article

Collection of Correlated Information from Superimposed Multiple Chirp Signals

Koichi Adachi, Koki Aoyama
article en

Abstract

Wireless Sensor Networks (WSNs) based on Long-Range Wide Area Networks (LoRaWAN) are being used in various applications. In WSNs, in addition to periodic packet transmission, sensor nodes transmit packets when they detect an event. This action enables real-time information aggregation. When multiple sensor nodes detect an event at the same time, they send signals simultaneously, which may result in signal collisions at the receiver. This paper proposes a symbol detection method that allows information aggregation even when receiving superimposed chirp signals. Furthermore, this paper introduces an event source estimation method as a use case for collecting aggregate correlated information. Computer simulations show that the proposed method increases the probability of achieving the absolute source estimation error of 10 [deg.] by 11 points compared to conventional methods.

University of Electro-Communications (JP)
Openalex Percentile: Top 71%
Blind Source Separation Techniques
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Collection of Correlated Information from Superimposed Multiple Chirp Signals — Koichi Adachi, Koki Aoyama · (2024) | TGRS Research Map | TGRS