Automatic Modulation Recognition of Communication SignalsSpringer 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. |
Nội dung
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 |
Ấn bản in khác - Xem tất cả
Automatic Modulation Recognition of Communication Signals Elsayed Azzouz,A.K. Nandi Xem trước bị giới hạn - 2013 |
Automatic Modulation Recognition of Communication Signals Elsayed Azzouz,Asoke Nandi Không có bản xem trước - 2013 |
Automatic Modulation Recognition of Communication Signals Elsayed Azzouz,Asoke Nandi Không có bản xem trước - 2010 |
Thuật ngữ và cụm từ thông dụng
1500 Micro Sec 2000 Time Instants 400 realizations absolute phase ADMRA amplitude information Amplitude modulation AMRAS analogue and digital analogue modulated signals analogue modulation recognition ASK2 ASK2 and ASK4 ASK4 band-limited bandwidth carrier frequency Chapter classified with success combined AM-FM Confusion matrix correct decisions curve corresponding dB and 20 dB the curve decision-theoretic Deduced Modulation Type digital modulation recognition digitally modulated signals discriminate DMRA DSB VSB LSB Frequency KHZ Furthermore hidden layer ANN Hilbert transform instantaneous amplitude instantaneous frequency instantaneous phase intercepted signal introduced key features extraction key features threshold Megaflops MFSK modulation index modulation recognition algorithms MPSK normalisation number of nodes output layer overall success rate performance evaluation probability of correct PSK4 signals samples shown in Fig signal frame signal segment single hidden layer SNR of 15 spectral power density subset t(max t(odp Table types of interest VSB LSB USB Ymar Ymax