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E-raamat: Sea Clutter: Scattering, the K distribution and radar performance

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  • Formaat: PDF+DRM
  • Sari: Radar, Sonar and Navigation
  • Ilmumisaeg: 22-Apr-2013
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781849195904
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Sea Clutter: Scattering, the K distribution and radar performanceSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Radar, Sonar and Navigation
  • Ilmumisaeg: 22-Apr-2013
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781849195904
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The second edition of Sea Clutter: Scattering, the K Distribution and Radar Performance gives an authoritative account of our current understanding of radar sea clutter.

Topics covered include the characteristics of radar sea clutter, modelling radar scattering by the ocean surface, statistical models of sea clutter, the simulation of clutter and other random processes, detection of small targets in sea clutter, imaging ocean surface features, radar detection performance calculations, CFAR detection, and the specification and measurement of radar performance. The calculation of the performance of practical radar systems is presented in sufficient detail for the reader to be able to tackle related problems with confidence.

For this fully revised and updated second edition new material has been added on the Doppler characteristics of sea clutter and associated detection processing methods, bistatic sea clutter measurements; electromagnetic scattering theory of littoral sea clutter and bistatic sea clutter; the use of models for predicting radar performance, including discussion of Lognormal and Weibull models; further results and extended discussion on the modelling of the K distribution shape parameter for different conditions; the simulation of Doppler spectra of sea clutter; high grazing angle scattering; and the use of the K distribution in other fields. The material has been reorganized into four parts: Sea Clutter Properties, Mathematics of the K distribution, Radar Detection and Physical Modelling. This reorganization allows readers to access specific areas quickly, without the need for an extensive knowledge of the other parts.
List of symbols xv
1 Introduction
1(14)
1.1 Prologue
1(1)
1.2 Maritime radar
1(5)
1.3 The modelling of radar returns from the sea
6(1)
1.4 The use of clutter models in radar development
7(2)
1.4.1 Requirement definition
7(1)
1.4.2 Modelling of potential performance
7(1)
1.4.3 System and algorithm development
8(1)
1.4.4 Performance assessment and acceptance trials
8(1)
1.4.5 In-service tactics and training
9(1)
1.4.6 In-service upgrades
9(1)
1.5 Outline of the book
9(4)
References
13(2)
Part I Sea clutter properties 15(120)
2 The characteristics of radar sea clutter
17(42)
2.1 Overview
17(2)
2.2 The sea surface
19(2)
2.3 Sea clutter reflectivity
21(2)
2.4 Amplitude statistics
23(5)
2.4.1 The compound nature of sea clutter amplitude statistics
26(2)
2.5 Frequency agility and sea clutter
28(1)
2.6 Observations of amplitude distributions
28(3)
2.7 Polarisation characteristics
31(3)
2.8 Clutter spikes and modulations
34(7)
2.9 Coherent properties of radar sea clutter
41(5)
2.10 Spatial characteristics
46(6)
2.10.1 Range Autocorrelation Function (ACF)
47(2)
2.10.2 Power spectrum analysis of range-time intensity plots
49(3)
2.11 Bistatic clutter
52(3)
2.11.1 Bistatic scattering geometry
53(1)
2.11.2 Bistatic reflectivity NBRCS
53(1)
2.11.3 Bistatic amplitude statistics
54(1)
2.11.4 Bistatic Doppler spectra
55(1)
References
55(4)
3 Empirical models for sea clutter
59(46)
3.1 Overview
59(1)
3.2 Low grazing angle normalised sea clutter RCS models
59(8)
3.2.1 RRE model
60(1)
3.2.2 GIT model
60(4)
3.2.3 Sittrop's model
64(1)
3.2.4 The TSC model
64(1)
3.2.5 The hybrid model
65(1)
3.2.6 Other results
66(1)
3.3 Medium and high grazing angle normalised RCS models
67(3)
3.4 Bistatic normalised RCS models
70(5)
3.4.1 In-plane NBRCS models
71(3)
3.4.2 Out-of-plane NBRCS
74(1)
3.5 Low grazing angle statistics
75(12)
3.5.1 Lognormal distribution
75(1)
3.5.2 Weibull distribution
75(1)
3.5.3 Compound K distribution
76(1)
3.5.4 Compound K distribution plus noise
77(1)
3.5.5 Shape parameter at low grazing angle
78(5)
3.5.6 Discrete spike modelling
83(4)
3.6 Medium grazing angle statistics
87(3)
3.7 Bistatic amplitude statistics
90(2)
3.8 Doppler spectra
92(8)
3.8.1 Average Doppler spectra
92(1)
3.8.2 Evolution of Doppler spectra with time
93(5)
3.8.3 Bistatic Doppler spectra
98(2)
References
100(5)
4 The simulation of clutter and other random processes
105(30)
4.1 Introduction
105(1)
4.2 Generating uncorrelated random numbers with a prescribed PDF
106(1)
4.3 Generating correlated Gaussian random processes
107(4)
4.4 Fourier synthesis of random processes
111(1)
4.5 Approximate methods for the generation of correlated gamma distributed random numbers
112(2)
4.6 The correlation properties of non-Gaussian processes generated by MNLT
114(2)
4.7 Correlated exponential and Weibull processes
116(3)
4.8 The generation of correlated gamma processes by MNLT
119(5)
4.9 Simulating coherent clutter
124(9)
4.9.1 Simulation of clutter spectra
125(4)
4.9.2 Simulation of time series data
129(3)
4.9.3 Discussion
132(1)
References
133(2)
Part II Mathematics of the K distribution 135(120)
5 Elements of probability theory
137(42)
5.1 Introduction
137(1)
5.2 Finite numbers of discrete events
138(2)
5.3 An infinite number of discrete events
140(2)
5.4 Continuous random variables
142(4)
5.5 Functions of random variables
146(3)
5.6 The normal process
149(8)
5.7 The time evolution of random processes
157(1)
5.8 Power spectra and correlation functions
158(1)
5.9 The complex Gaussian process
159(3)
5.10 Spatially correlated processes
162(1)
5.11 Stochastic differential equations and noise processes
163(7)
5.12 Miscellaneous results
170(7)
5.12.1 Correcting moments for the effect of noise
170(1)
5.12.2 Correcting the moments for a limited number of samples
171(2)
5.12.3 Order statistics
173(1)
5.12.4 Sequential testing
174(3)
References
177(2)
6 Gaussian and non-Gaussian clutter models
179(18)
6.1 Introduction
179(1)
6.2 Gaussian clutter models
179(5)
6.3 Non-Gaussian clutter
184(6)
6.3.1 Compound models of non-Gaussian clutter
185(1)
6.3.2 The gamma distribution of local power and the K distribution
186(1)
6.3.3 A coherent signal in K distributed clutter
187(1)
6.3.4 K distributed clutter with added thermal noise
188(1)
6.3.5 Phases of homodyned and generalised K processes
189(1)
6.4 Modelling coherent clutter
190(6)
References
196(1)
7 Random walk models
197(20)
7.1 Introduction
197(1)
7.2 A random walk model of non-Gaussian scattering
197(4)
7.3 The Class A and breaking area models
201(6)
7.4 A Fokker-Planck description of K distributed noise
207(7)
7.5 Conclusions
214(1)
References
214(3)
8 Some extensions of the K distribution
217(18)
8.1 Introduction
217(1)
8.2 The homodyned and generalised K models
218(8)
8.3 Populations on coupled sites and their continuous limit
226(5)
8.4 Some applications
231(2)
8.5 Conclusions
233(1)
References
233(2)
9 Special functions associated with the K distribution
235(20)
9.1 Introduction
235(1)
9.2 The gamma function and related topics
235(5)
9.3 Some properties of the K distribution PDF
240(5)
9.4 The Bessel functions In, Jn
245(5)
9.5 Expansions in Hermite and Laguerre polynomials
250(3)
References
253(2)
Part III Radar detection 255(184)
10 Detection of small targets in sea clutter
257(32)
10.1 Introduction
257(1)
10.2 Statistical models for probabilities of detection and false alarm
258(1)
10.3 Likelihood ratios and optimal detection
259(2)
10.4 Some simple performance calculations
261(4)
10.5 The generalised likelihood ratio method
265(2)
10.6 A simple Gaussian example
267(5)
10.6.1 A simple likelihood ratio-based approach
267(1)
10.6.2 Generalised likelihood ratio-based approach
268(4)
10.7 The detection of a steady signal in Rayleigh clutter
272(6)
10.7.1 Generalised likelihood ratio-based approach
272(4)
10.7.2 Peak within interval detection
276(2)
10.8 Applications to coherent detection
278(2)
10.9 The estimation of clutter parameters
280(3)
10.9.1 Maximum likelihood estimators for gamma and Weibull parameters
280(2)
10.9.2 Tractable, but sub-optimal, estimators for K and Weibull parameters
282(1)
10.10 Implications of the compound form of non-Gaussian clutter
283(3)
10.10.1 Modified generalised likelihood ratio-based detection
283(2)
10.10.2 Modified peak within interval detection
285(1)
10.11 Concluding remarks
286(1)
References
286(3)
11 Imaging ocean surface features
289(32)
11.1 Introduction
289(1)
11.2 The analysis of correlated Gaussian data
289(5)
11.2.1 χ processing
290(1)
11.2.2 χa processing and the whitening filter
290(3)
11.2.3 Optimal χo processing
293(1)
11.3 The Wishart distribution
294(4)
11.3.1 The real Wishart distribution
295(1)
11.3.2 The complex Wishart distribution
296(2)
11.4 Polarimetric and interferometric processing
298(10)
11.4.1 χ processing of interferometric and polarimetric data
300(2)
11.4.2 Phase increment processing of interferometric data
302(3)
11.4.3 Coherent summation and discrimination enhancement
305(3)
11.5 Feature detection by matched filtering
308(2)
11.6 False alarm rates for matched filter processing
310(7)
11.6.1 A simple model for the global maximum single point statistics
311(2)
11.6.2 The global maximum of a one-dimensional Gaussian process and the matched filter false alarm curve for a time series
313(2)
11.6.3 Extension to two-dimensional matched filters
315(2)
11.7 A compound model for correlated signals
317(2)
References
319(2)
12 Radar detection performance calculations
321(48)
12.1 Introduction
321(1)
12.2 Radar equation and geometry
322(3)
12.3 Sea clutter fluctuations and false alarms
325(7)
12.4 Target RCS models and detection probability
332(13)
12.5 Detection performance with a logarithmic detector
345(3)
12.6 Comparison of K distribution, Weibull and lognormal models
348(7)
12.7 Performance prediction of pulsed Doppler processing
355(2)
12.8 End-to-end radar detection performance
357(10)
12.8.1 Radar polarisation
360(2)
12.8.2 Target models
362(1)
12.8.3 Target exposure time
363(1)
12.8.4 Radar resolution
364(1)
12.8.5 Scan rate
365(2)
12.9 Modelling other types of radar
367(1)
References
367(2)
13 CFAR detection
369(42)
13.1 Introduction
369(1)
13.2 Adaptation to changing clutter amplitude
370(25)
13.2.1 Control of received signal dynamic range
371(1)
13.2.2 Log FTC receiver for Rayleigh clutter
372(1)
13.2.3 Cell-averaging CFAR detector
373(21)
13.2.3.1 CFAR variants
375(1)
13.2.3.2 CFAR loss in noise
376(2)
13.2.3.3 GO CFAR in noise
378(2)
13.2.3.4 OS CFAR in noise
380(1)
13.2.3.5 CFAR loss in K distributed clutter
381(4)
13.2.3.6 CFAR loss in K distributed clutter plus noise
385(1)
13.2.3.7 Ideal CFAR detection and CFAR gain in K distributed clutter
386(3)
13.2.3.8 CFAR gain with a cell-averaging CFAR
389(5)
13.2.4 Linear prediction techniques
394(1)
13.2.5 Non-linear predictors
395(1)
13.3 Adaptation to changing clutter PDF
395(11)
13.3.1 Fitting to a family of distributions
396(2)
13.3.2 Distribution-free detection
398(2)
13.3.3 Estimation of the K distribution shape parameter
400(5)
13.3.3.1 Matching moments
400(2)
13.3.3.2 Matching to the tail of the distribution
402(3)
13.3.4 Estimation of a Weibull shape parameter
405(1)
13.4 Other CFAR detection techniques
406(2)
13.4.1 Site-specific CFAR
406(1)
13.4.2 Closed-loop systems
406(1)
13.4.3 Exploitation of transient coherence
407(1)
13.4.4 Scan-to-scan integration
408(1)
13.5 Practical CFAR detectors
408(1)
References
409(2)
14 The specification and measurement of radar performance
411(28)
14.1 Introduction
411(1)
14.2 Performance specification issues
412(9)
14.2.1 Discussion
412(2)
14.2.2 Adaptive radars
414(1)
14.2.3 Specification of adaptive systems
415(1)
14.2.4 Practical performance specification
416(5)
14.2.4.1 Probability of false alarm, PFA
416(1)
14.2.4.2 Spatial variation of PFA
416(4)
14.2.4.3 Probability of detection, PD
420(1)
14.2.4.4 Spatial variation of PD
420(1)
14.3 Performance prediction
421(5)
14.3.1 Clutter amplitude statistics
424(1)
14.3.2 Clutter speckle component
424(1)
14.3.3 False alarms
425(1)
14.4 Measuring performance
426(3)
14.4.1 Trials
427(1)
14.4.2 Factory measurements
428(1)
14.4.3 Modelling and simulation
428(1)
14.5 Measurement methods and accuracies
429(9)
14.5.1 Probability of detection
430(5)
14.5.1.1 Blip-to-scan ratio
430(1)
14.5.1.2 Estimation of SNR
431(2)
14.5.1.3 Detection in sea clutter
433(2)
14.5.2 Probability of false alarm PFA
435(1)
14.5.3 Statistical analysis of trials
435(77)
14.5.3.1 Sequential testing
436(2)
References
438(1)
Part IV Physical modelling 439(108)
15 High grazing angle radar scattering
441(28)
15.1 Introduction
441(1)
15.2 The sea surface
442(8)
15.3 EM scattering from the sea at high grazing angles
450(5)
15.4 Imaging ocean currents at high grazing angles
455(11)
References
466(3)
16 Low grazing angle scattering by the ocean surface
469(32)
16.1 Introduction
469(1)
16.2 The composite model for scattering at medium grazing angles
469(4)
16.3 Scattering at low grazing angles: beyond the composite model
473(13)
16.4 Scattering from breaking waves
486(5)
16.5 Average backscatter from the ocean at low grazing angles
491(3)
16.6 Imaging ocean currents at low grazing angles
494(3)
16.7 Sea clutter in littoral environments
497(1)
References
498(3)
17 Scattering from a corrugated surface
501(46)
17.1 The integral formulation of the scalar scattering problem
501(3)
17.2 Helmholtz equation Green's functions in two and three dimensions
504(3)
17.3 Derivation of the Fresnel formulae
507(3)
17.4 Approximate decoupling of the integral equations: the impedance boundary condition
510(2)
17.5 Scattering by a perfectly conducting surface
512(10)
17.5.1 The physical optics or Kirchoff approximation
512(2)
17.5.2 Small height perturbation theory: PC case
514(3)
17.5.3 The half-space and reciprocal field formalisms
517(5)
17.6 Scattering by an imperfectly conducting surface: small height perturbation theory
522(4)
17.7 Numerical solutions of the scattering problem
526(12)
17.7.1 Scattering from a perfect conductor
526(9)
17.7.2 Scattering from an imperfect conductor; modification of the F/B method
535(3)
17.8 Incorporation of the impedance boundary condition in F/B calculations
538(1)
17.9 Evaluation of adjunct plane contributions
539(3)
17.10 Summary
542(1)
References
543(4)
Index 547
Professor Keith Ward has worked on radar and military systems throughout his career with his ideas exploited for in-service radars and remote sensing. He is a Visiting Professor at UCL, in electronics and electrical engineering.



Dr Robert Tough has carried out extensive research in ocean imaging, small target detection, rough surface scattering, detection theory and range profile classification for a number of clients based in the UK and USA.



Professor Simon Watts works for Thales UK and is a Visiting Professor at UCL, in electronics and electrical engineering. His research interests include sea clutter and detection signal processing methods.
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