This paper deals with the problem of estimating the parameters of the Rice distribution. The distribution has applications in sonar and radar signal processing and a proper estimation procedure with associated confidence intervals is important. Using the sample second moment as an estimate of the second moment of the distribution, two techniques, viz., methods of moments and maximum likelihood are applied to synthetic envelope data of known signal-to-noise ratios, in order to estimate the parameter from different sample sizes. It is concluded that the sample second moment is an unbiased estimate of the theoretical second moment and for the signal-to-noise ratio parameter both methods work without any significant bias and satisfy the criterion of maximum efficiency. However, the method of moments is simpler, easier to apply and therefore recommended as the method of choice.

The purpose is to use acoustic scattering theory and Sea Beam measurements to estimate seafloor roughness parameters. The Sea Beam backscatter data are from a test area in the Laurentian fan, a relatively flat region. Sidescan sonarlike images were reconstructed from the multibeam data. These images in the test area show two distinctly different types of areas (A) and (B). The backscatter model uses the Helmholtz-Kirchhoff formulation of scattering theory and correlation function C(r)=exp[-lr/lln], where r is the displacement, l is the correlation length, and n is the exponent. A single rough interface model fits the backscatter data in (A). The root-mean-square roughness was 6-8 cm and the correlation lengths were 140-270 cm. The exponent n ranged from 0.95 to 1.5. The type (B) areas required a two-layer model: the interfaces in the first type of areas (A) is covered by a sediment having a smoother surface. The rms roughness of the covering sediments were about 3 cm, the exponent n was nearly 2 and correlation lengths I were 90-120 cm. These acoustic models are consistent with the geological setting and processes. A insonified area P a • =l/2X•q_k2X• cos 40•/2R 2 P(s) • 1 a e --= 1/2Y• q-k2Y•/2R• 2 b power lawinP(s)=P•s -b orA(s)=A•s -•/2 Ps B • scattering function constant r B • --=k2R•22/8,r cos 2 B sf 1 and B sf2 scattering function constants for interfaces 1 and 2 C(r) C C a and Cs d ds DR DrDR i(o) G h Iref R • and R 2 =exp[--Ir/ll n] spatial correlation function, radial symmetry R 12 spatial correlation function sound speed in water average and surface sound velocity depth to the seafloor from ship's transducers depth of sub-bottom interface beneath the sea floor •'•T receiver directivity function W transmitter directivity function Ax •exp[-(x2/X•+y2/y•)] and Xo•dAqb, x• 2 Yo • dA •b for 0• =0 a = 1/cos 0•, slope corrected adB ----exp(-a•2-av•72+i2k• sin 0•) fi rms (root-mean-square) relief of the surface y mean backscattered intensity at the receiver • source intensity at the reference distance R re f (usually 1 m) wave number "correlation length" 0• and • Oe(i) exponent of the correlation function vector of parameters "power" spectrum is the spectral density at s = s • scattered sound pressure radial distance from origin to a scattering element on the scattering surface in the (r,½) coordinates are the distances of the source and receiver from the origin is the pressure reflection coefficient at the 1-2 interface spectral frequency -•(k, 0• ,R • ,R 2 ,A) scattering function backscattering strengths from the surface and subbottom total backscattering strength -=exp[-y2h2(1-C)] • rc / (2 sin 0) =(k/R•)cos 2 0•, yf 2=k/R• =k sin 0• attenuation in sediment in dB/m =0 =-k cos 0• elevation of an element of area above the mean level = arcsin{(c a/C s) sin[ Oa(i) +/x]}, effective angle of arrival incident angle 1545 d. Acoust. Soc. Am. 97 (3),

Due to budget constraints, many multibeam sonar customers are being required to operate smaller ocean survey vessels, equipped with less-expensive oceanographic tools. At the same time, ocean survey mission requirements are becoming more and more demanding.In response to this dilemma, L-3 SeaBeam Instruments, Inc. of East Walpole, MA has developed an innovative method for mathematically extending the performance of smaller, low-cost multibeam systems. This enhanced signal processing technique uses Array Extrapolation (AE) to provide:-Narrower receive beam dimensions -Better separation of closely spaced targets -improved signal-to-noise performance This paper studies the effectiveness of this technique as employed in an actual SEA BEAM 2112 multibeam sonar system by comparing several bathymetric data sets, acquired during survey, with and without AE being implemented. After processing the data, an examination is made of the beampatterns, the standard deviations obtained after detrending the data, and the maps produced from the bathymetric data. In conclusion, it is found that the AE technique does improve the results in most situations encountered in practice, and does not degrade sonar performance in the remaining situations.

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