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Moving Target Depth Estimation for Passive Sonar, Using Sequential Resampling Techniques

机译：基于序贯重采样技术的被动声纳移动目标深度估计

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In this paper, the authors investigate an approach of joint estimation of range-rate and depth in a littoral environment, rather than range and depth. Range-rate provides another dimension with which to discriminate targets against interfering sources (such as moving ships). In addition, discrimination based on range-rate is more robust with respect to environmental uncertainties as verified by simulations, and with respect to associated uncertainties in the horizontal wave numbers of the channel modes used for the matched-field target response. Using this approach, the complex amplitudes of the modes are treated as nuisance parameters, which comprise a hidden, first- order Markov state process. In lieu of an analytic expression of the updated likelihood, they have investigated a technique of sequential resampling or particle filtering. They compare its performance with the conventional matched- field processor (MFP), which localizes in depth and range. The limitations of this particular technique seem to be its ability to compensate for low signal- to-noise ratio by integrating over many snapshots. It should be emphasized, however, that this is a limitation of the particle-filter implementation investigated here and not a limitation of the basic state-model approach of localizing with respect to range-rate and depth, rather than range and depth. Future work will be focused on implementations that more effectively exploit the entire data history. (8 figures, 10 refs.).

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