Two nanosatellites recently launched into space had issues with respect to its stabilization, power and orientation. The signals were intermittent, and amateur radio enthusiasts around the globe were requested to observe the satellites so as to get their health information. As decoding the received signals required proprietary hardware (that could not be sent to everyone), amateur radio receivers recorded the signal using Software Defined Radios (SDRs) and sub-sampled the carrier signals to make it easy to share. The captured signals, modulated using binary Frequency Shift Keying (FSK), included noise and more importantly the frequency shifts due to Doppler, caused by the speed of the satellites (of about 7.8 km/s), thus making decoding a major challenge even for the designated proprietary receivers (failed in some cases). As the existing FSK methods did not work effectively, we were motivated by this challenge to design an effective FSK decoder that works in the presence of Doppler and noise. In this paper, we propose Teager Energy Decoder (TED) based on Teager Energy Operator to decode such Doppler and noise influenced sub-sampled data. TED does not need any Doppler correction mechanisms and can dynamically adapt to the changing frequency shifts. We evaluate TED using simulation as well as from the signals from those two satellites. We show that TED performs better than COTS transceivers and available GNU-radio-based solutions using SDRs. TED is low-complexity algorithm, O(N2), and has been prototyped on a low-power microcontroller. TED can be easily adopted on satellites to decode signals for space Internet of Things applications.

Original languageEnglish
Title of host publicationIPSN'19
Subtitle of host publicationProceedings of the 2019 Information Processing in Sensor Networks
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Print)978-1-4503-6284-9
Publication statusPublished - 2019
Event18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019 - Montreal, Canada
Duration: 16 Apr 201918 Apr 2019


Conference18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019

ID: 55481859