Adaptive window size and stepped frequency scan spectrogram analysis for drone signal detection in multi-signal environment

• Main Authors: Chia, Chun Choon, Sha’ameri, Ahmad Zuri
• Format: Article
• Published: Science and Technology Research Institute for Defence 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/91311/; https://www.scopus.com/record/display.uri?eid=2-s2.0-85087093211&origin=resultslist&sort=plf-f&src=s&sid=9ce8c19095d83006900e26fe85d803d7&sot=b&sdt=b&sl=138&s=

In this paper, a spectrogram based on stepped frequency scanning and adaptive window size algorithm is proposed to detect drone signals that operate at the 2.4 and 5.8 GHz Industrial, Scientific and Medical (ISM) bands in a multi-signal environment. In this algorithm, the received signal is divided into multiple sub-bands and scanned through a large analysis bandwidth. The window size is automatically adjusted by balancing the time and frequency resolution. The adaptive stepped frequency scan spectrogram (ASFSS) is then implemented to obtain the time-frequency representation (TFR). From the TFR, signal parameters, such as the hop duration, bandwidth, and instantaneous frequency (IF), are estimated. Three possible drone signal types are used in the study: fast frequency hopping spread spectrum (FHSS), slow FHSS, and hybrid spread spectrum (HSS). The performance of ASFSS is verified using Monte-Carlo simulation with 20 realisations at signal-to-noise ratio (SNR) range from-16 to 12 dB. In the presence of additive white Gaussian noise (AWGN), the detection cut-off point is-12 dB for fast and slow FHSS and-5 dB for HSS. Additional environment signals, such as direct sequence spread spectrum (DSSS) and WiFi, increase the cut-off point to 5 dB for fast FHSS, 7 dB for slow FHSS and 8 dB for HSS.