Beuchert, Jonas and Rogers, Alex
2021.
SnapperGPS: algorithms for energy-efficient low-cost location estimation using GNSS signal snapshots.
Presented at: SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems,
Coimbra, Portugal,
November 15 - 17 2021.
SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems.
New York:
Association for Computing Machinery,
pp. 165-177.
10.1145/3485730.3485931
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Abstract
Snapshot GNSS is a more energy-efficient approach to location estimation than traditional GNSS positioning methods. This is beneficial for applications with long deployments on battery such as wildlife tracking. However, only a few snapshot GNSS implementations have been presented so far and all have disadvantages. Most significantly, they typically require the GNSS signals to be captured with a certain minimum resolution, which demands complex receiver hardware capable of capturing multi-bit data at sampling rates of 16 MHz and more. By contrast, we develop fast algorithms that reliably estimate locations from twelve-millisecond signals that are sampled at just 4 MHz and quantised with only a single bit per sample. This allows us to build a snapshot receiver at an unmatched low cost of $14, which can acquire one position per hour for a year. On a challenging public dataset with thousands of snapshots from real-world scenarios, our system achieves 97% reliability and 11 m median accuracy, comparable to existing solutions with more complex and expensive hardware and higher energy consumption. We provide an open implementation of the algorithms as well as a public web service for cloud-based location estimation from low-quality GNSS signal snapshots.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computing Machinery |
| ISBN: | 9781450390972 |
| Date of First Compliant Deposit: | 24 June 2026 |
| Last Modified: | 02 Aug 2026 12:20 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/173416 |
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