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

Improved localization of underwater acoustic sources by nonlinear wavelet denoising under non-Gaussian noise conditions

Bearing estimation of underwater acoustic sources is an important aspect of passive localization in the ocean. The performance of all bearing estimation techniques degrades under conditions of low signal-to-noise ratio (SNR) at the sensor array. The degradation may be arrested by denoising the array data before performing the task of bearing estimation. In the last few years, there has been considerable progress in the use of the wavelet transform for denoising signals. It is known that the traditionalwavelet transform, which is a linear transformation, can be used for denoising signals in Gaussian noise; but this method is not suitable if the noise is strongly non-Gaussian. Statistical measurements of ocean acoustic ambient noise data indicate that the noise may have a significantly non-Gaussian heavy-tailed distribution in some environments. In this work, we have explored the possibility of employing nonlinear wavelet denoising [1, 2], a robust technique based on median interpolation, to improve the performance of bearing estimation techniques in ocean in a strongly non-Gaussian noise environment. We propose the application of nonlinear wavelet denoising to the noisy signal at each sensor in the array to boost the SNR before performing bearing estimation by known techniques such as MUSIC and Subspace Intersection Method [3]. Simulation results are presented to show that denoising leads to a significant reduction in the mean square errors (MSE) of the estimators, and enhancement of resolution of closely spaced sources

Improved bearing estimation in ocean by nonlinear wavelet denoising under non-Gaussian noise conditions

Bearing estimation of underwater acoustic sources is an important aspect of passive localization in the ocean. The performance of all bearing estimation techniques degrades under conditions of low signal-to-noise ratio (SNR) at the sensor array. The degradation may be arrested by denoising the array data before performing the task of bearing estimation. In the last few years, there has been considerable progress in the use of the wavelet transform for denoising signals. It is known that the traditional wavelet transform, which is a linear transformation, can be used for denoising signals in Gaussian noise; but this method is not suitable if the noise is strongly non-Gaussian. Statistical measurements of ocean acoustic ambient noise data indicate that the noise may have a significantly non-Gaussian heavy-tailed distribution in some environments. In this work, we have explored the possibility of employing nonlinear wavelet denoising [1, 2], a robust technique based on median interpolation, to improve the performance of bearing estimation techniques in ocean in a strongly non-Gaussian noise environment. We propose the application of nonlinear wavelet denoising to the noisy signal at each sensor in the array to boost the SNR before performing bearing estimation by known techniques such as MUSIC and Subspace Intersection Method [3]. Simulation results are presented to show that denoising leads to a significant reduction in the mean square errors (MSE) of the estimators, and enhancement of resolution of closely spaced sources

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

Passive Source Localization Using Compressively Sensed Towed Array

The objective of this work is to estimate the sparse angular power spectrum using a towed acoustic pressure sensor (APS) array. In a passive towed array sonar, any reduction in the analog sensor signal conditioning receiver hardware housed inside the array tube, significantly improves the signal integrity and hence the localization performance. In this paper, a novel sparse acoustic pressure sensor (SAPS) array architecture is proposed to estimate the direction of arrival (DOA) of multiple acoustic sources. Bearing localization is effectively achieved by customizing the Capons spatial filter algorithm to suit the SAPS array architecture. Apart from the Monte Carlo simulations, the acoustic performance of the SAPS array with compressively sensed minimum variance distortionless response (CS-MVDR) filter is demonstrated using a real passive towed array data. The proposed sparse towed array architecture promises a significant reduction in the analog signal acquisition receiver hardware, transmission data rate, number of snapshots and software complexity.Defence Science Journal, 2013, 63(6), pp.630-635, DOI:http://dx.doi.org/10.14429/dsj.63.576

Robust Conditional Probability Constraint Matched Field Processing

192-200In order to improve the robustness of Adaptive Matched Field Processing (AMFP), a Conditional Probability Constraint Matched Field Processing (MFP-CPC) is proposed. The algorithm derives the posterior probability density of the source locations from Bayesian Criterion, then the main lobe of AMFP is protected and the side lobe is restricted by the posterior probability density, so MFP-CPC not only has the merit of high resolution as AMFP, but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. The results show that in the uncertain ocean environment MFP-CPC is robust not only to the moored source, but also to the moving source. Meanwhile, the localization and tracking is consistent with the trajectory of the moving source

Acoustically driven control of mobile robots for source localization in complex ocean environments

Ocean based robotic systems are an opportunity to combine the power of acoustic sensing in the water with sophisticated control schemes. Together these bodies of knowledge could create autonomous systems for mapping acoustic fields and localizing underwater sources. However, existing control schemes have often been designed for land and air robots. This creates challenges for applying these algorithms to complex ocean environments. Acoustic fields are strongly frequency dependent, can rarely be realistically modeled analytically, have complex contours where the feature of interest is not always located at the peak pressure, and include many sources of background noise. This work addresses these challenges for control schemes from three categories: feedback and observer control, gradient ascent control and optimal control. In each case the challenges of applying the control scheme to an acoustic field are enumerated and addressed to create a suite of acoustically driven control schemes. For many of these algorithms, the largest issue is the processing and collection of acoustic data, particularly in the face of noise. Two new methods are developed to solve this issue. The first is the use of Principal Component Analysis as a noise filter for acoustic signals, which is shown to address particularly high levels of noise, while providing the frequency dependent sound pressure levels necessary for subsequent processing. The second method addresses the challenge that an analytical expression of the pressure field is often lacking, due to uncertainties and complexities in the environmental parameters. Basis functions are used to address this. Several candidates are considered, but Legendre polynomials are selected for their low error and reasonable processing time. Additionally, a method of intermediate points is used to approximate high frequency pressure fields with low numbers of collected data points. Following this work, the individual control schemes are explored. A method of observer feedback control is proposed to localize sources by linearizing the acoustic fields. A gradient ascent method for localizing sources in real time is proposed which uses Matched Field Processing and Bayesian filters. These modifications allow the gradient ascent algorithm to be compatible with complex acoustic fields. Finally, an optimal control method is proposed using Pontryagin's Maximum Principle to derive trajectories in real time that balance information gain with control energy. This method is shown to efficiently map an acoustic field, either for optimal sensor placement or to localize sources. The contribution of this work is a new collection of control schemes that use acoustic data to localize acoustically complex sources in a realistic noisy environment, and an understanding of the tradeoffs inherent in applying each of these to the acoustic domain

Experimental results of underwater cooperative source localization using a single acoustic vector sensor

This paper aims at estimating the azimuth, range and depth of a cooperative broadband acoustic source with a single vector sensor in a multipath underwater environment, where the received signal is assumed to be a linear combination of echoes of the source emitted waveform. A vector sensor is a device that measures the scalar acoustic pressure field and the vectorial acoustic particle velocity field at a single location in space. The amplitudes of the echoes in the vector sensor components allow one to determine their azimuth and elevation. Assuming that the environmental conditions of the channel are known, source range and depth are obtained from the estimates of elevation and relative time delays of the different echoes using a ray-based backpropagation algorithm. The proposed method is tested using simulated data and is further applied to experimental data from the Makai’05 experiment, where 8–14 kHz chirp signals were acquired by a vector sensor array. It is shown that for short ranges, the position of the source is estimated in agreement with the geometry of the experiment. The method is low computational demanding, thus well-suited to be used in mobile and light platforms, where space and power requirements are limited