In this paper, a novel DOA estimation method without angles pre-estimating for Wideband Signals is proposed. The main idea is to design a novel Direction-Free focusing matrix using the decomposed representation of the array manifold matrix, where the angle parameter is separated from the array geometry and the frequency parameter. The focusing matrix is derived using a method similar to RSS. The novel method also uses the Toeplitz approximation algorithm to improve the estimation performance. The proposed method combines the RSS method and Toeplitz approximation algorithm, and does not require initial angles, so it can be called Toep-DFRSS method. The performance of Toep-DFRSS and other major methods is compared through computer simulations. The simulations show that Toep-DFRSS method has a better resolution performance and estimation accuracy than conventional RSS method, especially in the case of low SNR.

The geoacoustic inversion based on a horizontal towed array sonar receiving tow-ship noise has demonstrated a promising technique for the parameter inversion in shallow water. In order to characterize the evolution of parameters in the time-varying environment, the adaptive particle filter for the sequential inversion is presented in this paper. The inversion problem is formulated as a dynamic and nonlinear process in the Bayesian framework, due to the fact that the self-noise is recorded sequentially in space and time. To deal with the interparameter correlations and time-varying noise process, the adaptive sequential importance sampling is carried out based on the estimated covariance matrix of parameters that is updated on-line. And the particles are proposed with an adaptive shift to handle the rapidly varying parameters. The tonal components at low frequencies of the self-noise are used in the inversion. The sequential inversion method is verified through the processing of both synthetic data and the sea-trial data in the shallow water environment. The results show that the adaptive particle filter method can achieve a more stable and accurate estimate than successively running global optimization algorithms and can do better than particle filter inversion in a rapidly varying environment.

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