> 99 < 1 Abstract— A non cooperative communication environment such as in the HF (High Frequency) spectrum is when the signals present are unknown in nature. This is essentially true spectrum monitoring that is an activity in spectrum management and intelligence gathering. An instrument that is used for this purpose is a spectrum surveillance system whose features are: the measurement of signal strength and carrier frequency, the location of transmitters, estimation of modulation parameters and the classifications of signals. This paper describes the design and implement a system to analyze and classify the basic types of digital modulation signals such as Amplitude Shift-Keying (ASK), Frequency Shift-Keying (FSK) and Phase Shift-Keying (PSK). Analysis method is based on the spectrogram time frequency analysis and a rules based approach is used as a classifier. From the time-frequency representation, the instantaneous frequency is estimated which is then used to estimate the modulation type and its parameters. This information is further used as input to the rules based classifier. The robustness of the system is tested in the presence of additive white Gaussian noise. On the average, the classification accuracy is 90 percent for signal-to-noise ratio (SNR) of 2 dB. Thus, the results show that the system gives reliable analysis and classification of signals in an uncooperative communication environment even if the received signal is weak. Index Terms—digital modulation signals, signal classification, spectrogram, time-frequency analysis. I. INTRODUCTION ommunication in the HF (High Frequency) spectrum is non cooperative in nature since the communication signals are unknown in nature. Sky wave propagation allows signal to be received within and outside a country national boundaries. The spectrum is used by the aircrafts, ships, broadcasting services, amateur radio, foreign services and military for long range communications. Besides voice and telegraphy, services available today include email, telemetry, short messaging and facsimile. For regulatory organization, monitoring of the spectrum is important to ensure conformance Manuscript received January 23, 2007. This work was supported in part by the Agilent Foundation. Ahmad Zuri b. Sha’ameri is with Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, 81300 Johor, Malaysia. (phone: 607- 553-5416; fax: 607-556-6272; e-mail: ahmadzs@yahoo.com). Tan Jo Lynn is with the Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, 81300 Johor, Malaysia. (e-mail: tjolynn@yahoo.co.uk). to frequency planning. Spectrum monitoring for the military is part intelligence gathering. A spectrum monitoring system is used for this purpose and its features includes the measurement of signal strength and carrier frequency, the location of transmitters, estimation of modulation parameters and the classifications of signals. The two general methods to analyze and classify digital modulation signals are the likelihood or decision-theoretic method [1][2] and the pattern recognition method [3]-[5]. The decision- theoretic method uses the likelihood function conditioned to a known candidate signals to classify an unknown signal. In the pattern recognition method, the estimated modulation parameters of the signal and are matched to its corresponding type by a classifier such as a linear discriminant function or artificial neural network. Examples of analysis methods used for estimating the modulation parameters includes fractal domain representation [3], wavelets [4] and time-frequency analysis [5]. Unlike decision theoretic approach, the exact signal form is not required and is suitable for a non cooperative environment. This paper describes the design and implementation of a system for analysis and classification of the class of digital communication systems such as ASK (Amplitude Shift- Keying), FSK (Frequency Shift-Keying) and PSK (Phase Shift-Keying). The pattern recognition approach is adopted: the analysis method is the spectrogram time-frequency analysis and the classifier is the rules based method. II. SIGNAL MODEL The received signal when expressed in discrete-time form is defined as follows ) ( ) ( ) ( n w n x n y + = (1) where x(n) is the signal of interest and w(n) is the interference due to additive white Gaussian noise with zero mean and variance 2 w σ . The signal of interest is a digital modulation signal that can be modelled as a time-varying signal ∑ −∞ = + = n i n f n a n x λ φ λ π )) ( ) ( 2 cos( ) ( ) ( (2) Spectrogram Time-Frequency Analysis and Classification of Digital Modulation Signals Ahmad Zuri bin Sha’ameri, Member, IEEE and Tan Jo Lynn, Student Member, IEEE C 113 Proceedings of the 2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, 14-17 May 2007, Penang, Malaysia 1-4244-1094-0/07/$25.00 ©2007 IEEE. Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on January 1, 2009 at 20:54 from IEEE Xplore. Restrictions apply.