FHSS Signals Classification by Linear Discriminant in a Multi-signal Environment Muhammad Turyalai Khan, Ahmad Zuri Sha’ameri, Muhammad Mun’im Ahmad Zabidi, and Chun Choon Chia Abstract Frequency-hopping spread spectrum (FHSS) spreads the signal over a large bandwidth where the carrier frequencies change quickly according to a pseu- dorandom number making signal classification difficult. Furthermore, classification becomes more complex with the presence of additive white Gaussian noise (AWGN) and interference due to background signals. In this paper, a linear discriminant (LD) method based on the Euclidean distance is proposed for the classification of FHSS signals in the presence of AWGN and background signal. Probability of correct classification (PCC) of the FHSS signals is performed by the LD method for the signal-to-noise ratio (SNR) range of -6 to 15 dB. Results show that the proposed method has achieved 90% detection rate at the SNR range of -1.6 to 3.5 dB in the presence of AWGN only, while its performance is degraded to 0.9 to 12 dB when the background signal is present. Keywords Frequency-hopping spread spectrum · Linear discriminant · Probability of correct classification 1 Introduction Multi-signal environment comprises various kinds of wireless technologies which share a common frequency band [1]. For example, the sharing of the 2.4 GHz frequency between the Bluetooth and Wi-Fi [2]. Depending on the wireless tech- nology, the signals may have either a fixed or variable carrier frequency. Most impor- tant there should not be any overlap of carrier frequency between the various wire- less technologies which could cause interference between the various users. Thus, a spectrum monitoring system can be utilized to manage the use of carrier frequency between the various wireless technologies [3]. Frequency-hopping spread spectrum (FHSS) is a spread spectrum technique where the frequency changes randomly according to a pattern known to both the M. T. Khan (B ) · A. Z. Sha’ameri · M. M. A. Zabidi · C. C. Chia Division of Electronic and Computer Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 F. Thakkar et al. (eds.), Proceedings of the International e-Conference on Intelligent Systems and Signal Processing, Advances in Intelligent Systems and Computing 1370, https://doi.org/10.1007/978-981-16-2123-9_11 143