It is known that the conventional time reversal (or passivephase conjugation) is not applicable to time-varying channels with large Doppler spreads. Recently a new time reversal communication technique that can simultaneously counteract delay and Doppler spreads has been proposed and tested via simulations [22]. In this paper, we present testing results from a field experiments of underwater acoustic communication, which was conducted with a fast mobile source in the Thousand-Island Lake located at Chunan, China, in December 2011. Experimental results demonstrate that the new time reversal receiver that uses a rake-like structure to compensate for multiple Doppler shifts can eliminate both Doppler spread introduced by temporal channel variations and delay spread caused by waveguide effects. As such, it provides a quite promising data transmission technique for underwater communication network.
This paper analyzes the problem of detecting a monochromatic plane wave with unknown amplitude, phase, temporal frequency and direction of arrival (DOA) in complex white Gaussian noise with unknown variance. The uniformly-most-powerful-invariant (UMPI) test is derived by using statistical invariance principle but doesn't exist unless the signal-to-noise ratio (SNR) is known. However, it provides performance bound to evaluate any invariant test's performance when SNR is unknown. Typically, the generalized-likelihood-ratio-test (GLRT) and locally-mostpowerful-invariant (LMPI) test are derived in this paper as realizable suboptimal invariant tests with their performance comparison in different SNR through theoretical analysis. Simulation examples corroborate our analysis and indicate that the GLRT is close to the UMPI bound especially in the low-probability-of-false-alarm region of the receiver operating characteristic (ROC) curve while the performance of LMPI test is close to that of UMPI test in low SNR region.
A problem of localizing and tracking an acoustic source is researched when ocean environment including water column sound speed profile, ocean depth and seabed property is uncertain. In a Bayesian framework, the source and environmental parameters are regard as random variables with known prior knowledge, then the prior knowledge of parameters and acoustic model are combined with a likelihood function of data to provide posterior probability density functions (PDF) of both south and environmental parameters, target location parameters are estimated by marginalization integrates over the environmental parameters finally. In other hand, the environmental parameters and target location parameters evolve in time or space, which can be described by state-space model. Information on these parameters evolution and uncertainty at preceding steps can be incorporated to determine future probability of parameters with acoustic data being available at current step. A framework of a sequential Bayesian filter is derived naturally based on the model. A Kalman filter or particle filter could be used to implement the sequential Bayesian filter depend on the linear or nonlinear of the measurement equation or/and the state equation. The sequential Bayesian filter is demonstrated to be able to localize and track a source broadcasting a broadband signal in shallow water using both simulated and real data acquired by a towed array.
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