Classification of digital modulation types in multipath environments
Young, Andrew F.
Fargues, Monique P.
MetadataShow full item record
As the digital communications industry continues to grow and evolve, the applications of this discipline continue to grow as well. This growth, in turn, has spawned an increasing need to seek automated methods of classifying digital modulation types. This research is a revision of previous work, using the latest mathematical software including MATLAB version 7 and Simulink Â®. The program considers the classification of nine different modulation types. Specifically, the classification scheme can differentiate between 2, 4, and 8 PSK, 256-QAM from other types of M-QAM signals, and also M-FSK signals from PSK and QAM signals in various types of propagation channels, including multipath fading and a variety of signal-to-noise levels. This method successfully identifies these modulation types without the benefit of a priori information. Higher-order statistical parameters are selected as class features and are tested in a classifier for their ability to identify the above modulation types. This study considers the effects due to realistic multipath propagation channels and additive white Gaussian noise. Using these features, and considering all fading conditions, it was determined that the classifier was correct for a randomly sent signal under randomly high or low SNR levels (low: 0dB to 8dB; high: 50dB to 100dB) over 83.9% of the time.
Showing items related by title, author, creator and subject.
Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R. (SPIE, 2013);Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were ...
Costica, Yinon (Monterey, California. Naval Postgraduate School, 2010-12);The intelligence making process, often described as the intelligence cycle, consists of phases. Congestion may be experienced in phases that require time consuming tasks such as translation, processing and analysis. To ...
Autonomous time-frequency cropping and feature-extraction algorithms for classification of LPI radar modulations Zilberman, Eric R. (Monterey, California. Naval Postgraduate School, 2006-06);Three autonomous cropping and feature extraction algorithms are examined that can be used for classification of low probability of intercept radar modulations using time-frequency (T-F) images. The first approach, Erosion ...