Direction-finding of coherent signals based on cylindrical vector-hydrophones array

Abstract

In this paper, a new "particle-velocity-field and spatial smoothing" (PVFSS) algorithm is proposed for direction-finding of coherent signals, using the cylindrical vector-hydrophones array in the underwater acoustic medium. In contrast to the customary "spatial smoothing" technique, it provides a smaller reduction in the overall array's spatial aperture. While in contrast to the "particlevelocity-field smoothing" technique, it may increase the number of decorrelate-able coherent signals. Moreover, an ESPRIT-based, closed-form direction-finding algorithm is proposed and a method of removing cyclic ambiguity is provided. Finally, the theoretical performance of the proposed algorithm is analyzed. Simulation results are shown to verify the efficacy of the proposed algorithm.

An Hybrid Cramér-Rao Bound in Closed Form for Direction-of-Arrival Estimation by an “Acoustic Vector Sensor” With Gain-Phase Uncertainties

“…The same denominator appears in (67) as in (29). Following a procedure similar to that in the paragraph in Section V containing (31), rewrite (67) as (68) where above is same as that in (31).…”

“…The above (29) can be expressed explicitly in terms of the model parameters, by substituting (14)-(23) for the various entries of the two matrices, followed by brute-force solutions to the two determinants. However, the result would be so complicated that no qualitative insight could be obtainable thereof.…”

“…The corresponding azimuth/elevation direction-of-arrival estimates' Cramér-Rao lower bounds have been derived in [3]- [6], [8], [11]- [14], [17]- [19], [28], [31], [32], [34], [37] for an ideal acoustic vector sensor with nominal gain/phase responses exactly as in (1); and Cramér-Rao bound curves (but unaccompanied by any formula) have been plotted in [15], [20], [21], [23], [26], [29], [38]. However, all above Cramér-Rao bound results unrealistically assume no non-ideality in the acoustic vector sensor's gain/phase response.…”

Robustness of the coherently distributed MUSIC algorithm to the imperfect knowledge of the spatial distribution of the sources

“…According to (4), for the i − th source, the DOA estimationθ i satisfies that the first order derivative of the denominator of (4) equals zero so that :…”

“…DOA estimation issues such as the effects of model errors, the resolution of closely spaced sources, and the geometry of antennas have been widely studied in the past, with the sources assumed to be far-field point transmitters or reflectors [1][2][3][4][5]. Indeed, in most scenarios the point-source assumption is correct, but in some cases, as for instance the localization of acoustics [6] [7] or bio-medical [8] sources, ocean waves [9], or for mobile channel communication [10], a spatially distributed model of the sources is more appropriate.…”