Automatic Detection of the Number of Raypaths in a Shallow-Water Waveguide

International audienceCorrect identification and tracking of stable raypaths are critical for shallow-water acoustic tomography. Separating raypaths using high-resolution methods has been presented to improve resolution ability based on the prior knowledge of the number of raypaths. It is clear that the precise knowledge of the number of raypaths largely determines the separation performance. Therefore, a noise-whitening exponential fitting test (NWEFT) using short-length samples is proposed in this paper to automatically detect the number of raypaths in a shallow-water waveguide. Two information-theoretic criteria are considered as comparative methods in terms of the capability of correct detection. Their performances are tested with simulation data and real data obtained from a small-scale experiment. The experimental results show that the NWEFT can provide satisfactory detection compared to the two classic information-theoretic criteria--the Akaike information criterion (AIC) and the minimum description length (MDL). MDL is asymptotically consistent while AIC overestimates even if analyzed asymptotically. Compared to these criteria, the proposed method is more suitable for short-length data

Automatic detection of the number of Raypaths

International audienceIn this paper, an Exponential Fitting Test (EFT) is presented in the context of ocean acoustic tomography for detecting the number of the raypaths. It is based on the fact that the profile of the ordered eigenvalues fits an exponential law for white Gaussian noise and small samples. The number of raypaths could be detected when a mismatch occurs between the observed profile and the exponential model. Its performance is studied with simulated experiment datas. EFT works for the case of small number of samples when information theoretic criteria fail

Automatic detection of the number of raypaths in colored noise using short-length samples

International audienceIn ocean acoustic tomography (OAT) (especially in shallow water where raypaths are mixed), knowledge of the number of raypath is crucial for inversion algorithm. In this paper, a noise-whitening exponential fitting test (NWEFT) is presented in this context for detecting the number of raypaths. Classically, two suggested approaches are the Akaike information criterion (AIC) and the minimum description length (MDL). Based on ideal assumption of ergodic Gaussian random processes and white Gaussian noise, MDL is shown to be asymptotically consistent, whereas the AIC tends to overestimate the order of model. However, these assumptions could not be fulfilled in practical case of OAT. In order to be adapted for real case of OAT, noise whitening processing is applied as first step. Then, NWEFT bases on the fact that the profile of the ordered eigenvalues fits an exponential law for short-length samples of white Gaussian noise. The number of raypaths could be detected when a mismatch occurs between observed profile and exponential model. The fact that NWFET works on short-length samples is very important as a long duration of the received signal in OAT is unavailable. Its performance is studied with synthetic and real data set and compared with classical algorithms