Adaptive Equalization Using In Underwater Acoustic Channel

With the quality of underwater acoustic communication and data transfer rate demand increasing, a variety of adaptive technology is gaining a wide spread ,adaptive technology can improve transmission quality, low bit error rate. in the case of without increasing the transmit power, This paper analyzes the two traditional adaptive channel equalization algorithms: LMS (least mean square algorithm) and RLS (recursive least squares algorithm)'s algorithm theory and the implementation process, gives the current commonly used LMS Algorithm: Normalized of the LMS algorithm. Meanwhile, for the feature of the RLS algorithm's complexity, large storage space and unstable. We analyze the AFA (the average adaptive filter) channel equalization algorithm.For the existence issue of underwater acoustic communication experiments and noise, we build a water acoustic communication system simulation platform. This platform includes three parts of the channel.the sending module included a M sequence generation module,a modulation module, a preprocessing, module and a adaptive channel equalization balance module and so on. In order to make the balancing algorithm more targeted and more effective, the channel part was designed by the experimental acoustic transmission characteristics of the channel.This design use the acoustic model of the credit channel as a reference.,which is easy to implement and has a small computational complexity,Then, in the structures of the system simulation platform, we simulate and comparise of the traditional LMS and RLS algorithms including their respective algorithms to improve system performance improvement Based on the actual system parameters, we test the simulation platform and find the improve performance of algorithm is generally superior to the traditional method.Referred to in the literature,the AFA algorithm is less effective than the NLMS algorithm,this Conclusion indicate that the sound of the water transmission system, NLMS algorithm is the optimal algorithm. Finally,by processing the collected acoustic signal communication experiment ,we verify the correctness of conclusions.