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Superresolution Underwater Acoustics Superresolution underwater acoustics Annie Cuyt, Wen-shin Lee, Engelbert Tijskens Department of Mathematics and Computer Science, Universiteit Antwerpen, Belgium Wen-Ling Hong, Jr-Ping Wang Department of Naval Architecture and Ocean Engineering, National Kaohsiung University of Science and Technology, Taiwan Tim Cools, Tim Geerts Antwerp Maritime Academy, Belgium Summary We explore the application of some recent results in exponential analysis and sparse interpolation to underwater acoustics, in a joint effort from marine engineers and computational mathematicians. The fact that, in practice, the sampling rate used for the recording of an echoed signal is often a multiple of the Nyquist rate, is regarded as a constraint on the cost and performance of echo sounding devices. Here we illustrate how the latter can be overcome, using a new regular sampling scheme, that can even go well below the Nyquist rate. In the numerical examples an average of 20-25 % of the Nyquist rate is amply sufficient to reliably recognize and reconstruct the echo. The sparse sub-Nyquist method under consideration allows to recover from possible aliasing introduced by the subsampling. The technique does not need newly designed hardware, has low computational complexity and uses a small number of samples. PACS no. 43.25.Jh, 43.30.Gv, 43.60.Jn 1. Introduction method samples at a rate below the Shannon-Nyquist one while maintaining a regular sampling scheme. The applications of underwater sound analysis span a The key idea is quite generic so that it can be com- wide range, including positioning, navigation, wireless bined [2, 1] with several popular signal processing pro- communication, echo-sounding, seabed mapping, geo- cedures, both parametric and non-parametric, such as physical surveying, water quality measurement, active ESPRIT [14], MUSIC [15], the matrix pencil [10] algo- and passive sonar, military intelligence, and oceanic rithm, variable projection methods [13], and the dis- measurements such as wave heights, ocean currents crete Fourier transform. We illustrate some of these and temperature. Underwater acoustics is also a key possibilities on real-life hydrophone recordings. underpinning technology in offshore oil and gas activ- ities, is increasingly used in oceanographic and envi- 2. Classical exponential analysis ronmental studies, and continues to play a crucial role in defence. Its fundamental role cannot be overstated. Consider the interpolation problem In signal processing data are traditionally sampled n uniformly at a rate dictated by the Shannon-Nyquist X f = α exp(φ t ); α ; φ 2 (1) theorem, which states that the sampling rate needs j i i j i i C i=1 to be at least twice the maximum bandwitdh of the signal. A coarser time grid than dictated by the the- from the values fj at the uniformly spaced interpola- ory of Nyquist and Shannon causes aliasing, mapping tion points tj. We assume that the frequency content higher frequencies to lower ones in the analysis. Here in (1) is limited by [12, 16] we introduce a procedure for underwater acoustics that works with sub-sampled data: our parametric j=(φi)j=(2π) = j!ij < Ω=2; i = 1; : : : ; n; (2) and that the 2n sample points tj = j∆ are spaced with ∆ ≤ 1=Ω. In the sequel we also assume that n (c) European Acoustics Association is known, as its computation is less relevant in the current application. Techniques for the extraction of Copyright © 2018 | EAA – HELINA | ISSN: 2226-5147 - 2845 - All rights reserved Euronoise 2018 - Conference Proceedings the value of n from the samples fj can be found in For chosen ρ and r we denote the sample at tρ+jr; j = [4, 11, 2]. The interpolation problem (1) consists in 0;:::; 2n−1 by fρ+jr. The interpolation problem now estimating the parameters φ1; : : : ; φn and α1; : : : ; αn becomes from the samples fj at the points tj. n X Let us define the Hankel matrix fρ+jr = αi exp (φi(ρ + jr)∆) ; 2 3 i=1 fρ : : : fρ+n−1 ρH := 6 : :: : 7 ; ρ 2 : where j runs for ρ = 0 (at least) from 0 to 2n − 1 1 n 4 : : : 5 Z and for some ρ 6= 0 (at least) from 0 to n − 1. With fρ+n−1 : : : fρ+2n−2 r 2 N; ρ 2 Z we now define It is well-known that the Hankel matrix ρH can be 1 n 2 f f : : : f 3 decomposed as ρ ρ+r ρ+(n−1)r fρ+r ρ 6 7 ρ T Hn := 6 : : 7 : Hn = VnΛnAnV ; r 6 : :: : 7 1 n 4 : : : 5 fρ+(n−1)r : : : fρ+(2n−2)r 2 3 1 ··· 1 ρ This Hankel matrix r Hn is decomposable as 6 exp(φ1∆) ··· exp(φn∆) 7 Vn = 6 : : 7 ; ρ T 6 : : 7 Hn = VnΛnAnV ; 4 : : 5 r n exp(φ1(n − 1)∆) ··· exp(φn(n − 1)∆) 2 1 ··· 1 3 6 exp(φ1r∆) ··· exp(φnr∆) 7 An = diag(α1; : : : ; αn); V = 6 7 ; n 6 : : 7 Λn = diag(exp(φ1ρ∆);:::; exp(φnρ∆)): 4 : : 5 exp(φ1(n − 1)r∆) ··· exp(φn(n − 1)r∆) For chosen ρ, the values exp(φi∆) can be retrieved from the generalized eigenvalue problem [10] A = diag(α ; : : : ; α ); n 1 n ρ+1H v = exp(φ ∆) (ρH ) v ; 1 n i i 1 n i Λn = diag(exp(φ1ρ∆);:::; exp(φnρ∆)): i = 1; : : : ; n; (3) Again the values exp(φir∆), now with r > 1, can be obtained from the generalized eigenvalue problem where vi are the generalized right eigenvectors. The choice ρ = 0 coincides with the original solution of ρ+r ρ Hn vi = exp(φir∆) ( Hn) vi; the interpolation problem already proposed in 1795 by r r i = 1; : : : ; n; (5) de Prony [8]. From the exp(φi∆), the complex num- bers φi are uniquely defined because of the restriction where vi are the generalized right eigenvectors. With j=(φi∆)j < π. Subsequently the remaining linear co- r > 1 the φi cannot be retrieved uniquely from the efficients αi; i = 1; : : : ; n are obtained from the linear generalized eigenvalues exp(φir∆) anymore because system of interpolation conditions j=(φ r∆)j < rπ: (6) n i X αi exp(φij∆) = fj; j = 0;:::; 2n − 1 (4) Choosing r > 1 offers several numerical advantages i=1 though [9, 7, 3] besides the fact that one can work with which can be solved in the least squares sense in the sub-Nyquist sampled data [5, 6]. Especially when Ω is case of noisy data or exactly in the case of exact data quite large, the latter may be interesting as ∆ ≤ 1=Ω (in the latter case n of the interpolation conditions may become so small that collecting the samples fj are linearly dependent because the φi satisfy the gen- becomes costly. eralized eigenvalue problem). We remark that the co- We now indicate how to resolve (6). First of all, efficient matrix of (4) is a Vandermonde matrix. with ρ = 0, the linear coefficients αi; i = 1; : : : ; n are In the noisy case the structured matrices in both computed from the linear system (3) and (4) can also be extended to rectangular N ×n n X matrices with N > n and (3) and (4) can be consid- αi exp(φijr∆) = fjr; j = 0;:::; 2n − 1: ered in the least squares sense [13]. Then the index j i=1 in (4) runs from 0 to N + n − 1 and the total sample Next, for chosen ρ, a shifted set of at least n samples usage equals N + n. fρ+jr is interpreted as n 3. Sub-Nyquist exponential analysis X fρ+jr = (αi exp(φiρ∆)) exp(φijr∆); Now let us consider more general sample locations i=1 ρ 2 : (7) tρ+jr = (ρ+jr)∆ with r 2 N; r > 1 and gcd(r; ρ) = 1. Z - 2846 - Euronoise 2018 - Conference Proceedings Note that (7) is merely a shifted version of the 4.1. Test pool recording original problem where the effect of the shift is The test pool is 1 m deep with a surface of 1.8 m by pushed into the coefficient. From (7) the coefficients 1.8 m and for the experiment a Lowrance fishfinder α exp(φ ρ∆) can be computed since the generalized i i was used. The recording was of an 83 kHz outgo- eigenvalues exp(φ r∆) are known. From the α and i i ing signal, about 30-fold oversampled at 5 MHz (the the α exp(φ ρ∆) we obtain i i Nyquist rate being 166 kHz), and of an incoming echo with the same frequency, about 75-fold oversampled αi exp(φiρ∆) = exp(φiρ∆); at 12.5 MHz. Extracts of both are displayed in Figure α i 1. from which again the φi cannot be extracted unam- biguously if ρ > 1. But if gcd(r; ρ) = 1 the sets 2πi exp φ ∆ + ` ; ` = 0; : : : ; r − 1 i r and 2πi exp φ ∆ + ` ; ` = 0; : : : ; ρ − 1 ; i ρ which contain all the possible values for exp(φi∆) obtained respectively from exp(φir∆) in (3) and exp(φiρ∆) in (7), have a unique intersection [7]. How to obtain this and identify the φi is detailed in [7, 3]. So at this point the nonlinear parameters φi; i = 1; : : : ; n and the linear αi; i = 1; : : : ; n in (1) are com- puted through the solution of either (3) or (5) and (4), and if r > 1 also (7). Remedying the aliasing intro- duced by taking r > 1 is consequently at the expense of another n samples at shifted locations tρ+jr. As in the previous section, the structured matri- ces in the generalized eigenvalue problem (5) can be extended to N × n matrices with N > n.
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