Research On Imaging Algorithms Of TKIS Series Imaging Sonar

The advancement of technology and the development of the times have led mankind to focus more and more attention on the vast and mysterious underwater world.The TKIS-Ⅰ imaging sonar system can be used to detect the underwater environment to help divers complete their underwater tasks.At present,TKIS-Ⅰ imaging sonar system can meet the needs of underwater imaging,but the poor anti-noise performance of the system affects the quality of imaging.The large amount of computing complexity and data leads to a long scanning cycle.These problems can increase the amount of time a diver spends underwater,increasing the risk.To address these shortcomings,the imaging algorithm of TKIS imaging sonar is studied.The single-frequency pulse imaging algorithm is studied from the TKIS-Ⅰ imaging sonar system.The single-frequency pulse imaging algorithm adopts the theory of the FFT to obtain the echo signal information from the frequency domain to achieve imaging.Through the simulation of the process and performance analysis,we provide a performance comparison standard for the subsequent study of TKIS series imaging sonar imaging algorithm.For further cut down the complexity of computation and the data volume of TKIS series imaging sonar system,the orthogonal demodulation imaging algorithm is studied.The orthogonal demodulation imaging algorithm uses band-pass sampling instead of the original system algorithm,which significantly reduces the sampling rate of the system.A combination of orthogonal demodulation and band-pass sampling is used to find the signal envelope,instead of the original system algorithm to find the spectrum,to extract the information in the echo signal.By simulating and testing the orthogonal demodulation imaging algorithm and the imaging algorithm of the original system,it is demonstrated that the orthogonal demodulation imaging algorithm meets the underwater imaging requirements while reducing the sampling frequency,data volume and algorithmic complexity of the system.To increase levels of the noise immunity of TKIS series imaging sonar,the linear frequency modulation imaging algorithm is studied.To implementation the linear frequency modulation imaging algorithm can uses the linear frequency modulation signal instead of the single-frequency rectangular pulse signal.In this way,the original system can obtain a large time-bandwidth product,which has a better anti-noise capability.The pulse compression method of matched filtering is used to process the echo signal instead of the FFT method of the original system.Through the simulation analysis of the linear frequency modulation signal and the comparison with the performance of the original system,the availability of the linear frequency modulation imaging algorithm is proved,and it has good anti-noise performance.Finally,the anti-noise performance,computational complexity and data volume of the three imaging algorithms are compared and analyzed from the theoretical point of view.he Linear frequency modulation imaging algorithm has the strongest theoretical anti-noise performance at a large range,but its theoretical operation complexity is the largest;the orthogonal demodulation imaging algorithm has the weakest theoretical antinoise performance,but its operation theoretical complexity and data volume are the smallest.In addition,the single-frequency pulse imaging algorithm is implemented on TKIS-Ⅰ system and the orthogonal demodulation imaging algorithm is implemented on TKIS-Ⅰ and TKIS-Ⅱ systems.The imaging effect,operation time and scanning period of single frequency pulse imaging algorithm and orthogonal demodulation imaging algorithm are compared on TKIS-Ⅰ system.The following conclusions are drawn: the anti-noise ability of the single-frequency pulse imaging algorithm is better than that of the orthogonal demodulation imaging algorithm on the TKIS-Ⅰ system,but the sweep period and operation time of the orthogonal demodulation imaging algorithm are smaller than that of the single-frequency pulse imaging algorithm.