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