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Rayleigh Distribution and Its Generalizations RADIO SCIENCE Journal of Research NBS/USNC-URSI Vo!' 68D, No.9, September 1964 Rayleigh Distribution and Its Generalizations Petr Beckmann 1 Department of Ele ctrical Engineering, University of Color ado, Boulder, C olo. (R eceived D eccmber 7, ] 963) Th e Rayleigh d istribution is the distribution of thc sum of a large number of coplanar (or t imc) vectors \\'ith random. amplitudes alld Ull ifO rlllly distributed phases. As such, it is the lim iting case of distributions a ~soci at('d II ith 111 0re gl'IlPral vpctor surn s t hat arise in practical problems. S uch cases arC' til(' folloll' ing: (a) 'I'll(' p hasC' distributions of the vector tC'rms are not unifo rm, e.g., in the case of scatt l' ring fro m rough surface'S; (b) One or more vector tcrms predominaLP, their nwan sq uare value' not I>l'illg neglig;ble compared to th e m ean square valup of Uw SU Ill , c.g., in tJle ea~e of sigl als propagated in eil i (,'. ll1<'teor-scatter, and atmosplwrie noise; (0) The numiJer of \'('ctO I' t('rn ' j~ small, ('.g., in radar ret urns from several clo",(' targets; (d) '1'110 Jlumi)('r of vector lel'm o is ilsl'H randolll, e.g., in atmosp heric t urbuh'nce, mekor-scatter and atm ~ pl'(' .. ic noise'. The ('('suI ting distributions for t hese cases and their deviations from the R ayleigh distribution will be co nsidered. 1. Introduction In m any pl'Oblems arisin g in radio waye p ropaga­ Tho present paper considers (1) under more Lion LllC roslil Lant field is rormed by Lhe stlper­ gell eral co ncli Lions. J n principle the problem can posi Lion or interference of a number o[ clelllcntclry a l ways be sol ved by resol "ing (1) into its roctangular waves: cO lllponen Ls (i.o ., r eal and imaginary parts) x and y, J/ Ee iO = L:: Ejei~j, (1) j=J X= E.I cos 8= 1.:'Ej cos <PJ='±XJj j=li j=l (4) where t he TEj and Lhe <P j fw d el'en n rnay be rfl,ndom , y= E sin 8= ... and t he distributions of tllCindi I'idu <li Lerms of the slim need n ot be id entical. One is then faced wi t h findill g Lh o joint probability donsiLy vV(x, V), and the problem or determi !ling the distribuLion of IE l'eLmnsformin g to polar coord inates t hroug h (and so metimes of 8) if the distribu tions of t he E j, the <P j and are known. It will b e assumed that n 2 the terms of the sum (1) are mutually independent. p_(E) = E 1". W eE cos 8, E sin 8)d8 The slim (1) may also be regarded as the sum of coplanar vectors. po(8) = EW(E cos 8, E sin 8) dE. (5) In its most elemenlttry form , when n is a large 1 00 constant, tbe E ; are all ('qllal to Lhe Same constant, and the <P j are all uniforlll ly cii stribuLed fro m 0 to The various cases that may arise are co n veniently 271' , Lbe problem was sol wd by Rayleig-h [1896] flnd classified according to whether or not Lhe sums (4) leads to Lbe well-known Rayleigh distribution satisfy t he Central Limit Theorem , i. e. , whether or not x and yare normally distributed as n-?oo . ?E Each of these two cases again includes several p ( E) = ~sJ e-E 2 1 (2) s, further possibilities. L et D(x) denote the variance of x; t hen the di sLri­ where s=< 11;2)= nEJ and the brad ets < ) denote t he bution of x will tend to a normal distri bution as luean value; or n-?oo provided t hat the Xj a re mutuall y independent and that lim D (x;) = 0 for all j 1 On leave of absence from the Institute of Badio Engin eerin g and Electron ics. (6) Czechoslovak Academy of ScicJlccs , Praf,!; lIc. 11--) 00 D (x) , 927 with a similar statement for y. (This statement (14) usually suffices for engineering purposes; for a more rigorous enunciation of the Central Limit Theorem and Lindebol'g conditions d. Gnedenko and Kol­ Thus a Rayleigh vector is a UDP \'ec tol' whose x mogoro\' [1954].) Condition (6) essentially means and y components are di stributed normally with that nono of the terms Xj must predominate in the (x)=(y)=O and D (x) = D (y) = s/2. From this it fol­ resulting sum x. Howeyer, if the X j are themselves lows that the sum of any number of Rayleigh vectors normally distributed, t hen x will, of course, be also is itself a Rayleigh vectol'. normally distributed even if (6) does not hold. A R ayleigh distribution will thus be found when­ In most (but not all) applications the <P j are ever the resultant field is composed of a large number distributed uniformly between 0 and 2'7T' or in an of UDP yectors and (13) is satisfied. equivalent manner; i.e. , the phase distribution W ", (<p ) is such that 3. Nonuniform Phase Distributions In a number of applications the phases <P j in (1 ) are not distributed uniformly as in (7), but fiuctuate A vector with suoh a Uniformly Distributed Phase about some priyileged yalue. This will occur in will be called a UDP vector. The sum of UDP vec­ scattering from To ugh sW'faces (e.g., rough layers tors is obviously itself a UDP vector. If the terms in the atmosphere) 1'01' small roughness or small in (1) are UDP Yectors, then grazing angles. Since the terms in (1 ) are now not UDP Yectors, (11) and (12) will not hold. How­ e\-er, if (6) holds, x and y will still be normally dis­ tributed. If the phase distributions are symmetrical abou t zero, tben (y)= O. By the usual rules of probability theory one then :finds the quantities But for j~k , and the integration (5) yields (9) Substitu ti ng (9) in (8) we find the importan t relation (10) where 1m is the modi:rred Bessel function of order valid for UDP vectors regardless of the value of n m and Eo = l , E",=2 for m~O. Details of the pro­ or the distributions of the E j (possibly all different). cedure and Clll'Yes of (15) will be found in [Beckmann, 1962a] . The general distribution (15) simpli fies in certain 2. Rayleigh Distribution special cases. Ifa= O, but sl ~ s, theD (15) l'educes to If the terms in (1) are UD P Yectol's, then from (4) (X) = (y)= O (11) a distribution derived directly by Hoyt [1947]. On the other hand, if SI= sl = 4 s, but a~ O, then (15) reduces to the N akagami-Rice di stribution Then using (10), condi tion (6) becomes 2 peE)= 2~ exp [ _ a : E] 10 (2 ~E), (17) (13) a distribution derived by Rice [1944 and 1945] 2 and further analyzed, e.g., by Norton et al. [1955], and Zuhrt [1957]. The distribution (17) is obtained If (13) is satisfied and n is large, x and y can be when a constaDt yector (E 1= a, <Pl = O) is added to a approximated by a normal distribution with mean value zero and the same variance. The integration Z '-rhe distribution w as origina11 y derived h y N akagami in 1940. A summary and bibliography or t he ",-ark on this and related top ics by :-Jakagam; and other (5) then leads to the R ayleigh distribution (2) with Japanese scientists will be found in [~a k aga,m i , 1960]. 928 1_ R ayleigh nctor , for the x a nd y co mponents of th e for one (or m ore) j. If more than one of such waves sum will then obviously be di stributed normally are presen t, we m ay sum them by standard methods wit h (x)=a, (y)=O, D (x) = D (Y) = 8/2 (where 8 is (conITolutions, characteristic functions) and regard the mean square value of the R ayleigh vector) thi s p ar ti,)'l sum as on.e wave. Since assumption (21 ) just as assumed in deriving (17). lL should be noted will hold for all other j , the rema ining terms will add that (J 7) will equ ally well h old for E [ exp (ict>l) a up to a R ayleigh ITector, so that the problem reduces UDP yector with constant a mplitude E[ = a; t his to :finding the distri bution of t he sum of a UDP may be shown by measuring the p hases from ct>l as Yectol' witb random ampliLude El and a Rayleigh a reference phase: the distribu tions of ct>; = ct> j- ct>l yector. will for j ~ 11'em ain uniform ,),s before, whereas ct>; = 0, This problem ma,y be sollred directly from first thus reducing to t he same co nditions under whicb principles by (4) to (5) or more quickly by random­ (1 7) was d er i,~ e d . izing a= E[ in (17) a nd using Lh e theorem of total The Rayleig h distribution, as may easily be probability: if the density of a is w(a), then (17) verified, is the limiting di stribution of (15), and of gilres t he density p(E la), so t ltat Li tO required total its special cases (16) and (1 7), for a= O; 8[ = S2 = S/2.
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