Advanced Signal Processing Techniques for Underwater Acoustic Communication Networks

In this thesis, we develop and investigate novel signal processing techniques for underwater acoustic communication networks. Underwater acoustic channels differ from radio communication channels in the lower speed of signal propagation, richer and often sparse multipath arrivals, and more severe Doppler effect. Therefore, many signal processing techniques developed for radio communications may not work equivalently well for underwater acoustic channels. To investigate signal processing techniques in underwater acoustics, efficient simulation of signal transmission is required. Specifically, there is requirement for accurate simulation of doubly-selective underwater channels for different acoustic environments. In this thesis, a low-complexity channel simulator has been developed for scenarios with moving transmitter/receiver. The simulator is based on efficient generation of time-varying channel impulse response obtained using interpolation over a set of waymark impulse responses for a relatively small number of sampling points on the transmitter/receiver trajectory. The waymark impulse responses are generated using an acoustic field computation method, which is the most computationally expensive part of the simulator. To reduce the trajectory sampling rate, and thus, to reduce the complexity of the field computation, an approach for adjusting the time-varying multipath delays has been developed. For setting the trajectory sampling interval, a simple rule has been proposed, based on the waveguide invariant theory. To further reduce the simulator complexity, local spline interpolation is exploited. The developed simulator has been verified by comparing the simulated data with data from real ocean experiments. In particular, applying simulated data to an OFDM modem shows similar performance with that obtained from the data of a deep water experiment. C. Liu, Ph.D. Thesis, Department of Electronics, University of York i 2011 In communication networks, knowledge of positions of communication nodes is important for improving the system performance. A multi-source localization technique has been proposed based on the matched field (MF) processing. The technique locates the nodes by solving a set of basis pursuit de-noising (BPDN) problems corresponding to a set of source frequencies. An efficient technique combining the homotopy approach and coordinate descent search has been developed to solve the BPDN problem. Further reduction in the complexity has been achieved by applying a position grid refinement method. Verified using simulated data generated by the proposed simulator and data from real experiment, the proposed technique outperforms other MF techniques in resolving sources positioned closely to each other, tolerance to noise and capability of locating multiple sources. To provide reliable localization based on MF techniques, accurate knowledge of the underwater acoustic environment is essential. However, such a knowledge is not always available. Estimating uncertain environmental parameters can be achieved using MF inversion techniques. This requires solving a global optimization problem. Several global optimization algorithms have been investigated and an algorithm combining the simulated annealing and downhill simplex method has been applied for estimating the sound speed profile in a deep water scenario. Accurate MF localization results have been demonstrated when using the estimated sound speed profile. A very important task of communication receivers is accurate channel estimation. The knowledge of node positions and the environment can be exploited for enhancing the channel estimation accuracy and reducing the estimation complexity. This knowledge can be used to define the structure of the channel impulse response, such as the multipath spread and the sparsity. A channel estimator exploiting the channel sparsity estimated from the node positions has been proposed and investigated. The sparse taps of the channel impulse response are identified by solving a BPDN problem. The estimator employs an iterative tap-by-tap processing and uses local splines to interpolate the time-varying tap coefficients. This allows reduction in the complexity and memory requirement, whereas providing a high estimation accuracy.