Target acquisition in communication electronic warfare systems

This cutting-edge resource arms you with the full array of traditional methods and modern high-resolution techniques for acquiring communications targets. The book enables you to develop optimum methods for acquiring communications targets for exploitation or countermeasures. Additionally, you learn how to establish the optimum techniques for detection of deterministic signals with random parameters as well as stochastic signals. Whether you design or operate EW systems, you can turn to this guide to measure how effectively your EW systems cope in crowded RF environments.

Author Notes

Richard A. Poisel is a senior engineering fellow at Raytheon Missile Systems, and previously was chief scientist at the U.S. Army Research, Development, and Engineering Command, Intelligence and Information Warfare Laboratory, Ft. Monmouth, New Jersey.

Preface	p. xi
Chapter 1 Introduction	p. 1
1.1 Electronic Warfare	p. 1
1.2 Communications and EW	p. 2
1.3 Signal Detection	p. 4
1.4 Signal Searching	p. 9
1.5 Notation	p. 10
1.6 Concluding Remarks	p. 10
References	p. 11
Chapter 2 Deterministic and Stochastic Processes	p. 13
2.1 The Fourier Transform	p. 13
2.1.1 Important Fourier Transforms	p. 15
2.2 Deterministic Signals	p. 22
2.2.1 Energy and Power in Deterministic Signals	p. 24
2.3 Stochastic Processes	p. 25
2.3.1 Ensembles	p. 25
2.3.2 Power Spectral Densities	p. 27
2.3.3 Mean, Autocorrelation, and Autocovariance Functions	p. 28
2.3.4 Stationary and Wide-Sense Stationary Processes	p. 29
2.3.5 Ergodic Processes	p. 30
2.3.6 Cyclostationary Processes	p. 30
2.4 Stochastic Signals	p. 31
2.5 White Noise	p. 39
2.5.1 Signals in Noise	p. 40
2.6 Concluding Remarks	p. 41
References	p. 41
Chapter 3 Target Search Methods	p. 43
3.1 General Search	p. 45
3.2 Directed Search	p. 49
3.3 Concluding Remarks	p. 49
References	p. 50
Chapter 4 Hypothesis Testing for Signal Detection	p. 51
4.1 Hypothesis Testing	p. 51
4.2 Receiver Operating Characteristics	p. 54
4.3 Likelihood Ratio	p. 56
4.4 Hypothesis Tests	p. 57
4.4.1 Bayes Criterion	p. 59
4.4.2 Minimax Criterion	p. 64
4.4.3 Neyman-Pearson Criterion	p. 67
4.5 Multiple Measurements	p. 68
4.6 Multiple Hypotheses	p. 69
4.7 Concluding Remarks	p. 69
References	p. 70
Chapter 5 Target Parameter Estimation	p. 71
5.1 Signal Parameter Estimation	p. 71
5.2 The Cramer-Rao Bound	p. 73
5.2.1 CRLB for Signals in AWGN	p. 77
5.3 Maximum Likelihood Estimation	p. 88
5.4 Concluding Remarks	p. 99
References	p. 99
Chapter 6 Spectrum Estimation	p. 101
6.1 Spectrum Estimation with the Periodogram	p. 101
6.1.1 Averaged Periodogram	p. 105
6.2 Blackman-Tukey Spectrum Estimation	p. 108
6.3 Windows	p. 111
6.3.1 Other Windows	p. 112
6.3.2 Windows Summary	p. 115
6.4 Frequency Domain Detector Performance	p. 115
6.5 Concluding Remarks	p. 122
References	p. 124
Chapter 7 Detection of Deterministic Signals	p. 125
7.1 Detection of Deterministic Signals with Known Parameters	p. 126
7.1.1 Matched Filter Detection	p. 127
7.1.2 Matched Filter Performance	p. 131
7.2 Detection of Deterministic Signals with Unknown Parameters	p. 135
7.2.1 Quadrature Detector	p. 135
7.2.2 GLRT Detection	p. 141
7.2.3 Detection of Sinusoidal Carriers with Unknown Parameters	p. 151
7.2.4 Locally Optimum Test for Weak Signal Detection	p. 157
7.2.5 Bayes Linear Model	p. 174
7.2.6 MLE of the Unknown Parameters of Sinusoids in AWGN	p. 179
7.2.7 Optimum Detection of Deterministic Signals with Unknown Parameters in Impulsive Noise	p. 184
7.3 Concluding Remarks	p. 187
References	p. 189
Chapter 8 Detection of Stochastic Signals	p. 191
8.1 Detection of Random Signals with Unknown Parameters	p. 191
8.1.1 GLRT Detection of Stochastic Signals	p. 191
8.1.2 Locally Optimum Detection of Stochastic Signals	p. 197
8.2 Radiometer	p. 200
8.2.1 Radiometer	p. 201
8.2.2 Radiometer Performance	p. 202
8.2.3 Radiometer Models	p. 203
8.2.4 Uncertain Noise Power	p. 210
8.2.5 Local Oscillator Offset	p. 212
8.3 Concluding Remarks	p. 214
References	p. 214
Chapter 9 High-Resolution Spectrum Estimation	p. 217
9.1 Autoregressive Moving Average Modeling	p. 217
9.1.1 Moving Average Modeling	p. 221
9.1.2 Autoregressive Modeling	p. 222
9.1.3 ARMA Modeling	p. 223
9.1.4 Maximum Entropy Spectral Estimation	p. 231
9.1.5 Model Order Determination	p. 238
9.1.6 Resolution of AR Spectral Analysis	p. 242
9.2 Line Spectra	p. 247
9.2.1 Least Squares	p. 249
9.2.2 Prony's Method	p. 250
9.2.3 Modified Prony Methods	p. 251
9.3 Signal Subspace Techniques	p. 251
9.3.1 Pisarenko Method	p. 258
9.3.2 Root Pisarenko	p. 263
9.3.3 MUSIC	p. 264
9.3.4 Minimum Norm	p. 266
9.3.5 Principal Components Spectrum Estimation	p. 268
9.4 Maximum Likelihood	p. 269
9.5 Resolution Comparison	p. 270
9.6 Peak Determination	p. 277
9.7 Concluding Remarks	p. 277
References	p. 283
Chapter 10 Artifical Reasoning for Target Identification	p. 285
10.1 Evidential Reasoning	p. 285
10.1.1 Rules of Combination	p. 289
10.1.2 Limitations of the Dempster-Shafer Method	p. 295
10.2 Fuzzy Logic	p. 296
10.2.1 Fuzzy and Crisp Sets	p. 296
10.2.2 Relationships	p. 299
10.2.3 Common Membership Functions	p. 301
10.2.4 Fuzzy If-Then Rules	p. 306
10.2.5 Fuzzy Reasoning	p. 307
10.3 Concluding Remarks	p. 316
References	p. 319
Chapter 11 Resource Allocation	p. 321
11.1 Queues	p. 321
11.1.1 Statistics for Queuing Theory	p. 323
11.1.2 Kendall-Lee Notation	p. 325
11.1.3 Queue Relationships	p. 326
11.1.4 M/M/1 Model	p. 327
11.1.5 Other Queue Types	p. 331
11.2 Concluding Remarks	p. 331
References	p. 332
Appendix A Lagrange Multipliers	p. 335
Appendix B Convex Functions	p. 339
Reference	p. 343
List of Acronyms	p. 345
About the Author	p. 349
Index	p. 351

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