Automatic Modulation Recognition of Communication Signals

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Springer Science & Business Media, 30 thg 11, 1996 - 218 trang
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Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process.
Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types.
Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.
 

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Introduction
1
11 Background and Motivations
2
12 Mathematical Preliminaries
9
13 General Concepts about Modulation Techniques
12
132 Digitally modulated signals
20
14 Summary
23
Recognition of Analogue Modulations
38
22 Relevant Previous Work
40
52 Suggested Structure for ANN Modulation Recognisers
130
521 Preprocessing
131
522 Training and learning phase of ANNs
132
523 Test phase of ANNs
137
53 Analogue Modulation Recognition Algorithms AMRAs
138
531 Choice of ANN architectures
139
532 Performance evaluations
140
533 Speedup of the training phase
141

23 Developed Analogue Modulated Signal Recognition Algorithms AMRAs
42
231 Classification of each segment
43
232 Classification of a signal frame
46
24 Computer Simulations
47
241 Analogue modulated signal simulations
48
242 Bandlimiting of simulated modulated signals
50
25 Thresholds Determinations and Performance Evaluations
51
252 Performance evaluations
57
253 Processing Time and Computational Complexity
58
Recognition of Digital Modulations
74
32 Relevant Previous Work
75
33 Developed Digitally Modulated Signal Recognition Algorithms DMRAs
80
332 Classification of a signal frame
84
341 Digitally modulated signal simulations
85
35 Threshold Determinations and Performance Evaluations
87
352 Performance Evaluations
90
36 Conclusions
91
Recognition of Analogue Digital Modulations
105
42 Relevant Previous Work
106
43 Developed Analogue Digitally Modulation Recognition Algorithms ADMRAs
109
432 Classification of a signal frame
112
44 Threshold Determinations and Performance Evaluations
113
442 Performance evaluations
115
443 Processing Time and Computational Complexity
116
Modulation Recognition Using Artificial Neural Networks
129
54 Digital Modulation Recognition Algorithms DMRAs
143
542 Performance evaluations
144
543 Speedup of the training phase
145
55 Analogue and Digital Modulations Recognition Algorithms ADMRAs
146
552 Performance evaluations
148
553 Speedup of the training time
149
57 Conclusions
150
Summary and Suggestions for Future Directions
171
61 Summary by Chapters
172
613 Analogue and digital modulation recognition algorithms Chapter 4
173
62 Suggestions for Future Directions
174
Bibliography
176
Numerical problems associated with the evaluation of the instantaneous amplitude phase and frequency
181
A2 Speed of computation
182
A3 Weak intervals of a signal segment
183
A4 Phase wrapping
184
A5 Linearphase component
185
Carrier frequency estimation
187
B2 Timedomain estimation
188
B3 Simulation results
189
Alternative Algorithms for Modulation Recognition
191
C2 Digital modulation recognition algorithms
192
Index
208
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