This these explores automatic modulation recognition as applied to PCM/FM, SOQPSK- TG and ARTM CPM. It found that the likelihood based approach is intractable. The statistical features of the amplitude, phase and frequency are ineffective at distinguishing these modulation types. A method based on the phase changes between symbols is developed and shows that as long as symbol timing is established, this method can effectively distinguish PCM/FM, SOQPSK-TG and ARTM CPM for signal-to-noise ratios above 30 dB. Another method, the Bianchi-Loubaton- Sirven technique, was able to distinguish PCM/FM and SOQPSK-TG but was unable to distinguish ARTM CPM. A happy byproduct of this classification algorithm is a reasonably accurate estimate of the bit rate. Simulation results show that this classifier works essentially error-free for signal- to-noise ratios above 20 dB and for sufficiently high resolution in the search algorithms required by the maximizations.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / An algorithm for automatic modulation recognition of ASK, PSK and FSK was developed by Azzouz and Nandi. The algorithm has some serious problems at SNR of 10 dB and below. This paper describes a modification to the algorithm that significantly improves the performance for detection of ASK and FSK at moderate and low SNR.

No description available.

To many, high-frequency (HF) radio communications is obsolete in this age of long distance satellite communications and undersea optical fiber. Yet despite this, the HF band is used by defence agencies for backup communications and spectrum surveillance, and is monitored by spectrum management organizations to enforce licensing. Such activity usually requires systems capable of locating distant transmitters, separating valid signals from interference and noise, and recognizing signal modulation. Research presented here targets the latter issue. The ultimate aim is to develop robust algorithms for automatic modulation recognition of real HF signals, where real means signals propagating by multiple ionospheric modes with co-channel signals and non- Gaussian noise. However, many researchers adopt Gaussian noise models for signals for the sake of convenience at the cost of accuracy. Furthermore, literature describing the probability density function (PDF) of HF noise does not abound. So an additional aim of this research is measurement of the PDF of HF noise. A simple empirical technique, not found in the literature, is described that supports the hypothesis that HF noise is generally not Gaussian. In fact, the probability density function varies with the time of day, electromagnetic environment, and state of the ionosphere. Key contributions of this work relate to the statistics of HF noise and the discrimination of real HF signals via three signal features. Through two unique experiments, the density function of natural HF noise is found to closely follow a Bi-Kappa distribution. This distribution can model natural and man-made HF noise through a single control parameter. Regarding signal features, the coherence function is found to be a brute-force technique suitable only for hard (not soft) decisions. A novel application of an entropic distance measure proves able to separate four real HF signals based on their modulation types. And, an estimator for signal-to-noise (SNR) ratio is shown to provide reasonable measures of SNR for the same real HF signals. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1330848 / Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2008

In this thesis study, a new method for automatic recognition of intrapulse modulations has been proposed. This new method deals the problem of modulation recognition with a feature-based approach. The features used to recognize the modulation type are Instantaneous Frequency, Instantaneous Bandwidth, Amplitude Modulation Depth, Box Dimension and Information Dimension. Instantaneous Bandwidth and Instantaneous Frequency features are extracted via Autoregressive Spectrum Modeling. Amplitude Modulation Depth is used to express the depth of amplitude change on the signal. The other features, Box Dimension and Information Dimension, are extracted using Fractal Theory in order to classify the modulations on signals depending on their shapes. A modulation database is used in association with Fractal Theory to decide on the modulation type of the analyzed signal, by means of a distance metric among fractal dimensions. Utilizing these features in a hierarchical flow, the new modulation recognition method is achieved. The proposed method has been tested for various intrapulse modulation types. It has been observed that the method has acceptably good performance even for low SNR cases and for signals with small PW.

In this thesis work, automatic recognition algorithms for digital modulated signals are surveyed. Feature extraction and classification algorithm stages are the main parts of a modulation recognition system. Performance of the modulation recognition system mainly depends on the prior knowledge of some of the signal parameters, selection of the key features and classification algorithm selection. Unfortunately, most of the features require some of the signal parameters such as carrier frequency, pulse shape, time of arrival, initial phase, symbol rate, signal to noise ratio, to be known or to be extracted. Thus, in this thesis, features which do not require prior knowledge of the signal parameters, such as the number of the peaks in the envelope histogram and the locations of these peaks, the number of peaks in the frequency histogram, higher order moments of the signal are considered. Particularly, symbol rate and signal to noise ratio estimation methods are surveyed. A method based on the cyclostationarity analysis is used for symbol rate estimation and a method based on the eigenvector decomposition is used for the estimation of signal to noise ratio. Also, estimated signal to noise ratio is used to improve the performance of the classification algorithm. Two methods are proposed for modulation recognition: 1) Decision tree based method 2) Bayesian based classification method A method to estimate the symbol rate and carrier frequency offset of minimum-shift keying (MSK) signal is also investigated.

The aim of master’s thesis is a classification of digital modulation type. The interest in modulation classification has been growing for last years. It has several possible roles in both civilian and military applications such as spectrum sensing, signal confirmation, interference identification, monitoring and so on. Modulation classification is an intermediate step between signal detection and successful demodulation. Therefore the known methods are based on different statistics obtained from received signals. These statistics can be derived from continuous time signals and they hold for sampled signals.

This master’s thesis is about automatic digital modulation recognition using artificial neural networks. The paper briefly describes the issue and existing algorithms for solving the problem of modulation recognition. It was found that the best results are achieved when using the feature-recognition methods and artificial neural networks. The digital modulations that were chosen for recognition are described theoretically and they are ASK, FSK, BPSK, QPSK and 16QAM. These modulations are most commonly used today. Later was briefly described theory of neural networks. In another part was given to the characteristic features of modulation for modulation recognition using artificial neural networks. The penultimate part describes the parameters for signal simulation in Matlab, how to create the key features in Matlab and results after experimental simulation. The last part contains neural network optimization experiments.

Today, there is a demand for reliable ways to perform automatic modulation recognition of Low Probability of Intercept (LPI) radar signals, not least in the defense industry. This study explores the possibility of performing automatic modulation recognition on these signals through clustering and more specifically how to learn representations of input signals for this task. A semi-supervised approach using a bootstrapped convolutional neural network classifier for representation learning is proposed. A comparison is made between training the representation learner on raw time-series and on spectral representations of the input signals. It is concluded that, overall, the system trained on spectral representations performs better, though both approaches show promise and should be explored further. The proposed system is tested both on known modulation types and on previously unseen modulation types in the task of novelty detection. The results show that the system can successfully identify known modulation types with adjusted mutual information of 0.86 for signal-to-noise ratios ranging from -10 dB to 10 dB. When introducing previously unseen modulations, up to six modulations can be identified with adjusted mutual information above 0.85. Furthermore, it is shown that the system can learn to separate LPI radar signals from telecom signals which are present in most signal environments. / Idag finns ett behov av pålitlig automatiserad modulationsigenkänning (AMR) av Low Probability of Inercept (LPI)-radarsignaler, inte minst hos försvarsindustrin. Denna studie utforskar möjligheten att utföra AMR av dessa signaler genom klustring och mer specifikt hur man bör lära in representationer av signalerna i detta syfte. En halvövervakad inlärningsmetod som använder en klassificerare baserad på faltningsnätverk föreslås. En jämförelse görs mellan ett system som tränar för representationsinlärning på råa tidsserier och ett system som tränar på spektrala representationer av signalerna. Resultaten visar att systemet tränat på spektrala representationer på det stora hela presterar bättre, men båda metoderna visar lovande resultat och bör utforskas vidare. Systemet testas på signaler från både kända och för systemet tidigare okända modulationer i syfte att pröva förmågan att upptäcka nya typer av modulationer. Systemet identifierar kända modulationer med adjusted mutual information på 0.86 i brusnivåer från -10 dB till 10 dB. När tidigare okända modulationer introduceras till systemet ligger adjusted mutual information över 0.85 för upp till sex modulationer. Studien visar dessutom att systemet kan lära sig skilja LPI-radarsignaler från telekommunikationssignaler som är vanliga i de flesta signalmiljöer.

This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.