Automatic Modulation Recognition of Communication Signals

Bìa trước
Springer Science & Business Media, 30 thg 11, 1996 - 218 trang
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
39
52 Suggested Structure for ANN Modulation Recognisers
129
521 Preprocessing
130
522 Training and learning phase of ANNs
131
523 Test phase of ANNs
136
53 Analogue Modulation Recognition Algorithms AMRAs
137
531 Choice of ANN architectures
138
532 Performance evaluations
139
533 Speedup of the training phase
140

23 Developed Analogue Modulated Signal Recognition Algorithms AMRAs
41
231 Classification of each segment
42
232 Classification of a signal frame
45
24 Computer Simulations
46
241 Analogue modulated signal simulations
47
242 Bandlimiting of simulated modulated signals
49
25 Thresholds Determinations and Performance Evaluations
50
252 Performance evaluations
56
253 Processing Time and Computational Complexity
57
Recognition of Digital Modulations
73
32 Relevant Previous Work
74
33 Developed Digitally Modulated Signal Recognition Algorithms DMRAs
79
332 Classification of a signal frame
83
341 Digitally modulated signal simulations
84
35 Threshold Determinations and Performance Evaluations
86
352 Performance Evaluations
89
36 Conclusions
90
Recognition of Analogue Digital Modulations
104
42 Relevant Previous Work
105
43 Developed Analogue Digitally Modulation Recognition Algorithms ADMRAs
108
432 Classification of a signal frame
111
44 Threshold Determinations and Performance Evaluations
112
442 Performance evaluations
114
443 Processing Time and Computational Complexity
115
Modulation Recognition Using Artificial Neural Networks
128
54 Digital Modulation Recognition Algorithms DMRAs
142
542 Performance evaluations
143
543 Speedup of the training phase
144
55 Analogue and Digital Modulations Recognition Algorithms ADMRAs
145
552 Performance evaluations
147
553 Speedup of the training time
148
57 Conclusions
149
Summary and Suggestions for Future Directions
170
61 Summary by Chapters
171
613 Analogue and digital modulation recognition algorithms Chapter 4
172
62 Suggestions for Future Directions
173
Bibliography
175
Numerical problems associated with the evaluation of the instantaneous amplitude phase and frequency
180
A2 Speed of computation
181
A3 Weak intervals of a signal segment
182
A4 Phase wrapping
183
A5 Linearphase component
184
Carrier frequency estimation
186
B2 Timedomain estimation
187
B3 Simulation results
188
Alternative Algorithms for Modulation Recognition
190
C2 Digital modulation recognition algorithms
191
Index
207
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