Automatic Modulation Recognition Based on Morphological Operations
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Automatic Modulation Recognition Based on Morphological Operations Yuan Zhang · Xiurong Ma · Duo Cao
Received: 10 October 2012 / Revised: 12 March 2013 © Springer Science+Business Media New York 2013
Abstract Automatic modulation recognition under negative signal-to-noise ratio (SNR) environment is a challenging topic. In this paper, we propose the method consisting of two main steps: constructing a template library and recognition. The former extracts the morphological envelope of each signal power spectrum by using the morphological close–open operation to construct the template library. The latter can further be divided into two sub steps. The first sub-step is to calculate the similarities between the morphological envelope of received signal power spectrum and each template in the template library. The second one is to determine the modulation type of received signal based on the maximum of the similarities which are more than the threshold. Simulation results demonstrate that the correct recognition rate (CRR) can increase by 0.84 % and 9.38 % with hierarchical method and ZAM-GTFR method at SNR = −4 dB, respectively. The proposed method can reduce the computational complexity by about 97 % compared with the ZAM-GTFR method when N ≤ 4096. The results prove the method has the advantage of high CRR and the less computation. Keywords Automatic modulation recognition · Morphological operation · Similarity · Correct recognition rate
Y. Zhang () · X. Ma · D. Cao School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China e-mail: [email protected] X. Ma e-mail: [email protected] D. Cao e-mail: [email protected]
Circuits Syst Signal Process
1 Introduction Automatic modulation identification plays an important role in both military and civilian purposes. The role of modulation recognition is to determine the modulation type of the received signal without a priori information. Due to the increasing usage of modulation signals in novel technologies, the recent researches have been focused on identifying these modulation types. Modulation recognition techniques, generally, are divided into two categories. One is the decision theoretic approach and the other is pattern recognition. Decision theoretic approaches use probabilistic and hypothesis testing arguments to formulate the recognition problem. These methods suffer from their too high computational complexity, lack of robustness to the model mismatch as well as careful analysis that are required to set the correct threshold values [6, 10, 14, 18]. Pattern recognition approaches, however, do not need such careful treatment. The recognition system is composed of two subsystems [1–5, 7–9, 17]. The first subsystem is the feature extraction subsystem, which extracts predefined features from the incoming signal. The second one is the pattern recognition subsystem, which finds the modulation type of the received signal. Selection of both classifier and features are the most serious problem in modulation classificatio
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