Marine Sediment Classification Spectral Ratio Technique from a Signal Decomposition View Based on Chirp Sonar Data

Sediment classification based on Chirp sonar data is very important in support of marine science and engineering. The traditional adopted spectral-ratio (SR) method is widely applied for its theory of simplicity and easy employment. However, the performance of SR method is heavily degraded by spectrum vibrations introduced by overlapping reflections. To solve this problem, a reflection signal decomposition method is proposed in this paper, which decomposes the overlapping reflection into separate reflection sub-signals to avoid the spectrum vibration. Firstly, we re-derive the overlapping reflection expression from the convolution model and give the initial model for the decomposition of the Chirp sonar signal. Then, introducing the spectrum smooth prior into consideration and incorporating it with the fidelity and the bandwidth terms, the decomposition model with constraints is proposed. After that, an iterative algorithm is introduced to solve the model optimization problem. The reflection sub-signal can be well obtained. Finally, through a least square linear fitting for the logarithm of the spectral ratio (the log-SR) of reflection sub-signals, quality factor ( Q ) which is applied for sediment classification can finally be achieved. Both real and simulated experiments have been conducted to verify the proposed method and some meaningful discussions are also drawn.