Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets

Cramer-Rao bounds for source localization in shallow ocean with generalized Gaussian noise

Localization of underwater acoustic sources is a problem of great interest in the area of ocean acoustics. There exist several algorithms for source localization based on array signal processing.It is of interest to know the theoretical performance limits of these estimators. In this paper we develop expressions for the Cramer-Rao-Bound (CRB) on the variance of direction-of-arrival(DOA) and range-depth estimators of underwater acoustic sources in a shallow range-independent ocean for the case of generalized Gaussian noise. We then study the performance of some of the popular source localization techniques,through simulations, for DOA/range-depth estimation of underwater acoustic sources in shallow ocean by comparing the variance of the estimators with the corresponding CRBs

Acoustic pressure and particle velocity for spatial filtering of bottom arrivals

This paper discusses the advantages of using a combination of acoustic pressure and particle velocitymotion for filtering bottom arrivals. A possible area of application is reflection seismology where, traditionally, the seismic image is extracted from the bottom-reflected broadband acoustic signals received on hydrophones. Since hydrophones are omnidirectional in nature, the received bottom returns are often contaminated by waterborne signals, sea surface reflections, and noise. A substantial part of the processing of the data is dedicated to filtering out these unwanted signals. Today, vector sensors allow us to measure both acoustic pressure and particle velocity motion in a single and compact sensor. The combination of pressure and particle velocity measured at a single location or particle velocity and particle velocity gradient at closely spaced locations allows for spatial beam steering to predetermined directions and filter out unwanted replicas from other directions. Moreover, this can be done at the sensor level, dramatically decreasing the offline processing. The spatial filtering capabilities of various pressure-pressure, particle velocity-particle velocity, and pressure-particle velocity combinations are analyzed in view of filtering the bottom arrivals. It is shown that the combination of pressure and vertical particle velocity and, particularly, the combination of vertical particle velocity and particle velocity gradient enhance bottom arrivals. Moreover, a simple steering procedure combining pressure and particle velocity components of a triaxial sensor allows us to determine the tridimensional structure of the acoustic field and the separation of the bottom reflections. The spatial selectivity of the various sensor combinations is shown with simulations and verified with experimental data acquired with 10 cm separated vector sensors in the 800-1250-Hz band, during the Makai 2005 sea trial, off Kauai Island, HI, USA.This work was supported by the European Union H2020 Research Program under WiMUST Project (Contract 645141).info:eu-repo/semantics/publishedVersio

Broadband MFP: coherent vs. incoherent

Matched-Field Processing (MFP) is now a mature technique for source localization and tracking. There are at least two aspects that emerge, by their relevance, to the success of MFP: one is the ability of a given MFP processor to accurately pinpoint the source location while rejecting sidelobes, and the other is the impact of erroneous or missing environmental information (known as model mismatch) in the final source location estimate. This study addresses the first aspect regarding sidelobe rejection while considering that the processor is working on a mismatch free situation. One well known procedure to reduce sidelobes is to use a broadband MFP processor (whenever a band of frequencies is available). There are a number of different ways to combine MFP information across frequency that ran be classified in two broad groups: the conventional incoherent methods, that are based on the direct averaging of the auto-frequency inner products and the, say, less conventional methods, that perform a weighted average of the cross-frequency inner products where the weights are the frequency compensation phase-shifts. The later are generally termed as coherent broadband methods since they combine complex inner products. The coherent broadband methods proposed in the literature are either suboptimal or very computationally Intensive, even for a small number of frequencies. An alternative method is presented that combines cross-frequency information with the same localization performance than the standard coherent methods and a computation load similar to that of the incoherent processor. The performance of the various broadband processors is compared in simulated data

Maximizing the Number of Spatial Nulls with Minimum Sensors

In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level

Broadband matched-field processing: coherent and incoherent approaches

Matched-field based methods always involve the comparison of the output of a physical model and the actual data. The method of comparison and the nature of the data varies according to the problem at hand, but the result becomes always largely conditioned by the accurateness of the physical model and the amount of data available. The usage of broadband methods has become a widely used approach to increase the amount of data and to stabilize the estimation process. Due to the difficulties to accurately predict the phase of the acoustic field the problem whether the information should be coherently or incoherently combined across frequency has been an open debate in the last years. This paper provides a data consistent model for the observed signal, formed by a deterministic channel structure multiplied by a perturbation random factor plus noise. The cross-frequency channel structure and the decorrelation of the perturbation random factor are shown to be the main causes of processor performance degradation. Different Bartlett processors, such as the incoherent processor [Baggeroer et al., J. Acoust. Soc. Am. 80, 571-587 (1988)], the coherent normalized processor [Z.-H. Michalopoulou, IEEE J. Ocean Eng. 21, 384-392 (1996)] and the matched-phase processor [Orris et al., J. Acoust. Soc. Am. 107, 2563-2375 (2000)], are reviewed and compared to the proposed cross-frequency incoherent processor. It is analytically shown that the proposed processor has the same performance as the matched-phase processor at the maximum of the ambiguity surface, without the need for estimating the phase terms and thus having an extremely low computational cost. (C) 2003 Acoustical Society of America

Applying Spatial Diversity to Mitigate Partial Band Interference in Undersea Networks

Many acoustic channels suffer from interference which is neither narrowband nor impulsive. This relatively long duration partial band interference can be particularly detrimental to system performance. We survey recent work in interference mitigation and orthogonal frequency division multiplexing (OFDM) as background motivation to develop a spatial diversity receiver for use in underwater networks. The network consists of multiple distributed cabled hydrophones that receive data transmitted over a time-varying multipath channel in the presence of partial band interference produced by interfering active sonar signals as well as marine mammal vocalizations. In operational networks, many “dropped” messages are lost due to partial band interference which corrupts different portions of the received signal depending on the relative position of the interferers, information source and receivers due to the slow speed of propagation

Three-Dimensional Passive Source Localisation using the Flank Array of an Autonomous Underwater Vehicle in Shallow Water

Researchers have become interested in autonomous underwater vehicles equipped with various kinds of sonar systems that can perform many of underwater tasks, which is encouraged by the potential benefits of cost reduction and flexible deployment. This paper proposes an approach to three-dimensional passive source localisation with the flank array of an autonomous underwater vehicle in shallow water. The approach is developed based on matched-field processing for the likelihood of passive source localisation in the shallow water environment. Inter-position processing is also used for the improved localisation performance and the enhanced stability of the estimation process against the lack of spatial gain due to the small physical size of the flank array. The proposed approach is presented and validated through simulation and experimental data. The results illustrate the localisation performance at different signal-to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the autonomous underwater vehicle cruises at a low speed along a straight line at a constant depth.Defence Science Journal, 2013, 63(3), pp.323-330, DOI:http://dx.doi.org/10.14429/dsj.63.301

Physically constrained maximum likelihood (PCML) mode filtering and its application as a pre-processing method for underwater acoustic communication

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2009Mode filtering is most commonly implemented using the sampled mode shape or pseudoinverse algorithms. Buck et al placed these techniques in the context of a broader maximum a posteriori (MAP) framework. However, the MAP algorithm requires that the signal and noise statistics be known a priori. Adaptive array processing algorithms are candidates for improving performance without the need for a priori signal and noise statistics. A variant of the physically constrained, maximum likelihood (PCML) algorithm is developed for mode filtering that achieves the same performance as the MAP mode filter yet does not need a priori knowledge of the signal and noise statistics. The central innovation of this adaptive mode filter is that the received signal's sample covariance matrix, as estimated by the algorithm, is constrained to be that which can be physically realized given a modal propagation model and an appropriate noise model. The first simulation presented in this thesis models the acoustic pressure field as a complex Gaussian random vector and compares the performance of the pseudoinverse, reduced rank pseudoinverse, sampled mode shape, PCML minimum power distortionless response (MPDR), PCML-MAP, and MAP mode filters. The PCML-MAP filter performs as well as the MAP filter without the need for a priori data statistics. The PCML-MPDR filter performs nearly as well as the MAP filter as well, and avoids a sawtooth pattern that occurs with the reduced rank pseudoinverse filter. The second simulation presented models the underwater environment and broadband communication setup of the Shallow Water 2006 (SW06) experiment. Data processing results are presented from the Shallow Water 2006 experiment, showing the reduced sensitivity of the PCML-MPDR filter to white noise compared with the reduced rank pseudoinverse filter. Lastly, a linear, decision-directed, RLS equalizer is used to combine the response of several modes and its performance is compared with an equalizer applied directly to the data received on each hydrophone