Seafloor Bathymetry Tracking With Multi-beam Echo Sounder

With the development of marine economy and technology,measurement of seafloor bathymetry has become the foundation of the numerous human ocean activities such as environmental investi-gation,ocean engineering design and resource exploitation.Compared to the single point measure-ment with traditional echo sounder,the multi-beam echo sounder(MBES)generates high density strip depth measurements by exploiting wide-swath directional transmitting,multi-channel receiv-ing and array processing technique.which improves the efficiency of the seafloor bathymetry and is of great application value in fields such as underwater investigation of resources and maritime delimitation.To further improve MBES measurement accuracy,this thesis introduces a new concept of bathymetry tracking and develops relevant signal processing techniques.We start from reviewing the main features and the fundamental theory of the MBES including ray tracing.The tradition-al way of ray tracing for measuring of seafloor bathymetry is always affected by measurement noises and sound speed changes.Considering the environmental variation with time and space,a sequential filtering method is proposed and demonstrated,which constructs a state-space model u-tilizing the relationship among the time-of-arrivals(TOAs),direction-of-arrivals(DOAs)and sound speed profile(SSP)over different pings.The theory and operation procedure of the Kalman filter-ing especially the Unscented Kalman Filter(UKF)is then presented.The tracking performance of the sequential filtering algorithm and the traditional algorithm under the same Signal Noise Ra-tio(SNR)is then compared through the simulation data,and the relationship between the tracking performance and the SNR is then evaluated.The effectiveness of the sequential filtering algorith-m is shown by the comparison of conditional mean square error and posterior Cramer-Rao lower bound.To overcome the problem of large computation and low efficiency with the sequential filtering,a method of unknown parameter dimensionality reduction is proposed,which introduces empiri-cal orthogonal function(EOF)representation for seafloor bathymetry,thus remarkably reduces the complexity.Based on the orthogonality of the EOF,the state-space model is further extended to characterize more complicated seafloor evolutions.The developed approach is validated by the simulation data.Study the common error sources and editing rules of MBES,and a binary classification model based on support vector machine(SVM)is then applied to the referred algorithms to enhance the reliability of the measurement and the precision of the tracking.In this development,the obtained binary classification model is utilized to evaluate the performance of the algorithms and eliminate the exceptional points.At last,a deep sea MBES developed by the institute of acoustics of the Chinese academy of Sciences jointly with some other organizations including Zhejiang University is introduced.The hardware design of the field programmable gate array(FPGA)signal preprocessing module and the relevant digital signal processing algorithm are described,and its implementation is verified via comparison with the MATLAB based simulation.