Research Of The Bathymetry Inversion Method And Experiment With X-band Radar

It has a significant impact on the near-shore activities to monitor the changes of the topography of shallow water in the coastal areas. There are many ways to get water depth with the traditional methods include shipboard sonar detection, airborne laser detection and submersible vehicle probe. However, all of the above methods are high-cost and low-efficient. What’s more, the traditional methods is affected by the transparence of the water and extreme weather. In order to overcome the shortcomings of these methods, how to gain the depth for shallow water based on the X-band radar has been developed.In the shallow water, the propagation of the surface waves are influenced by the local variations of bottom topography and surface current, we can obtain the local bathymetry and current velocity based on the changes of the propagation. At first, this paper introduce the inversion algorithm of depth. With the assumption that the local region is homogeneous, the three-dimensional frequency of the local wave field was derived through the frequency decomposition and directional decomposition. Then we can get the local depth and velocity by analyzing the local spectrum. At last, we can gain the spatial distribution of depth of the whole wave field. At the same time, we also introduce the inversion algorithm for the homogenous wave field. Then, we use the inversion algorithm for the inhomogeneous wave field to verify the experimental data. The result of this algorithm and the real value are in good agreement, which verifies the feasibility of this inversion algorithm.The last part of this thesis introduces the way to gain ocean wave information using the wavelet transform for the inhomogenous real wave field. It can improve the accuracy of the depth inversion instead of the Fourier transform by wavelet transform on the assumption of the homogeneous of the time domain. The basic theory of this inversion algorithm is applying the two-dimension continuous wavelet transform in spatial domain and Fourier transform in the time domain. We use the simulate radar data and monitor radar data to verify the algorithm and gain the wave spectrum and mean wave directional spectrum. The comparison of the derived data and the input value are in good agreement, and thus, the algorithm is effective.