Shallow Water Doubly Spread Acoustic Channel Estimation And Tracking

Underwater acoustic (UWA) communication technology is the key for solving the transmission and processing of underwater information. In order to improve the system spectrum efficiency and deal with application requirement for high speed underwater acoustic communication, phase modulation technology has replaced the frequency modulation technology. Considering the rapid changes of phase response due to ocean dynamic, phase modulation technology is particularly vulnerable to the impact of the sound propagation environment. The channel impact can be summarized as limited bandwidth, long multipath delay spread, and rapid time variation. The excessive delay spread results in significant inter symbol interference (ISI) while time variation can lead to Doppler effects. The channels which often exhibit both time and frequency dispersion are referred to as doubly spread channels. When there exists large Doppler spread caused by large difference of arrival angles from different propagation paths, Doppler compensation using a single average value is not enough and the joint estima-tion for both delay and Doppler spread is necessary to recover the broadened signal. Moreover, the channel estimation results should be updated in real time to ensure the stability of the receiver when the time variation is severe. Facing the doubly spread and rapidly time-varying channels, this thesis conducts a research incorporating chan-nel estimation and dynamic channel tracking, aiming at realizing reliable acoustic communications.The study on the channel characters is the basis for the analysis and design of communication systems. First of all, the basic KRAKEN model and ray model is ana-lyzed for the way to introduce the Doppler spread. Then, the parameterization channel model is derived from a mathematical model that describe the discrete time channel input-output relationship. Finally, the image method is used to generate discrete mul-tipath according to the locations of transmitted/received sensors and the waveguide environment information. The amplitude of each paths is determined according to propagation attenuation and the autoregressive model which indicates the dynamic character. This dynamic doubly spread channel model is developed for the simulation and analysis of channel estimation and tracking algorithms stated in the thesis.To study the channel character of both delay and Doppler, one can obtain spatial information of environment and location of the transmitter/receiver, and achieve the dynamic evolution of the environment and platform motion information. Therefore, it is effective and complete to study the channel and the channel response to be estimated is the delay-Doppler spread function. Since the estimation of delay-Doppler spread function problem is an underdetermined inverse problem, the traditional estimation approaches do not work. This thesis adopts two approaches to solve the estimation problem. From the perspective of constraint optimization problem, applications of compressive sensing have been extended to delay-Doppler spreading function estima-tion. This thesis chooses the orthogonal matching pursuit (OMP) algorithms to conduct the simulation research. Moreover, an alternative solution is considered from the per-spective of matched filtering. Unlike the sparse channel estimation, the approach is not based on sparseness assumption, and the number of paths is determined simply by thresholding cross-ambiguity function (CAF) based on the noise power. The basic idea is to set rules of selecting the distinct paths on the plane instead of improving the resolution of the CAF. Simulation results validate that the OMP may achieve good estimation precision; however, this greedy algorithm with good estimation precision requires sufficient training symbols and much more computational effort to precisely select the parameters. While the direct CAF-based approach could support determining the number of paths by the noise power adaptively with lower computational complex-ity to achieve more reliable performance.However, the assumption that the channel remains stable over the training time could be easily violated in practice. It means that the channel estimates obtained from training symbols is not suitable for the subsequent information symbols. Hence, chan-nel tracking is the key technology to realize the reliable communication system. Facing the dynamic of channels, this thesis adopts existing sparse adaptive algorithms, which integrates the basic adaptive filter theory and the sparse property of the channel, to track the two dimensional spread function. Moreover, an alternative way is considered that the dynamic model is established to indicate the time-varying of channel and Kal-man filter is used to track the spreading function.Finally, using experimental data collected in shallow water with a mobile trans-mitter, the limitation of the traditional time reversal receiver has proved. The time reversal receiver with the algorithms analyzed in this thesis has summarized. Pro-cessing results show that the CAF-based channel estimation could achieve more robust performance with lower computational complexity and fewer training symbols than OMP method; the sparse sequential tracking algorithm failed in practice, while the dynamic Kalman tracking could demodulate the signal successfully.