This paper focuses on statistical characterization and capacity evaluation of shallow water acoustic communications channels. Wideband single-carrier and multi-carrier probe signals are employed to measure the time-varying channel response, and to estimate its statistical properties that play an important role in the design of bandwidth-efficient and power-efficient communication systems. To assess the capacity of an underwater acoustic channel, we first assess the probability density functions that model the signal fading. Rician fading is found to provide a good match for the experimental data collected in the Pacific Ocean during the summer 2008 Kauai acoustic communications experiment (KAM'08). This model is used to evaluate the ergodic channel capacity and the related bandwidth efficiency for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems.

/pdf/statistical-characterization-and-capacity-of-shallow-water-4nj9mx386t.pdf

Statistical characterization and capacity of shallow water acoustic channels

: This publication is a listing of journal articles and conference papers by Naval Research Laboratory authors for 1994. The bibliographic information is taken, primarily, from the Institute of Scientific Information database, Science Citation Index Expanded. Additional citations are generated from reprints of publications which are received in the Laboratory's Ruth H. Hooker Research Library. All references are unclassified. Citations are arranged alphabetically by author. An author index is included at the end of the citation listing. An HTML version of this publication is mounted on the Website of the Ruth H. Hooker Research Library - http://infoweb.nrl.navy.mil.

/pdf/1994-bibliography-of-nrl-journal-articles-and-conference-4cvtf1h8mb.pdf

1994 Bibliography of NRL Journal Articles and Conference Papers

Noise in the ocean is the result of many contributions. Sources emitting sound in open sea as well as in a coastal area can be placed both in the sea surface and underwater. Excluding the self-noise, the noise impinging a sonar system is called ambient noise and usually is split in two groups: anthropic and natural. In this paper we focus in modeling noise produced by ship transit, which falls in the anthropic category, and noise due to sea surface agitation, that is classified as a natural source. In particular we aspire to simulate the acoustic noise radiated by the machinery of a vessel once the rotational speed of the propeller induces the cavitation effect. Further, we take account of the wind speed action in the sea state and its contribution to the actual underwater ambient noise. An algorithm based on a non-Gaussian approach allows to generate sequence of samples representative of a noise realization having specified kurtosis level and to reproduce the desired source spectrum. The results of the simulation suggest that the surface ship transit can be thought as a major factor in limiting the performance of a underwater acoustic communications systems operating in a coastal shallow waters scenario.

Simulation of non-White and non-Gaussian underwater ambient noise

The noise radiated from ships can be used for their identification and classification using passive sonar systems. Several techniques have been proposed for military ship classification based on acoustic signatures, which can be acquired through controlled experiments performed in an acoustic lane. The cost for such data acquisition is a significant issue since the ship and crew have to be dislocated from the fleet. In addition, the experiments have to be repeated for different operational conditions, taking a considerable amount of time. Even with this massive effort, the scarce amount of data produced by these controlled experiments may limit further detailed analyses. In this paper, deep learning models are used for full exploitation of such acquired data, envisaging passive sonar signal classification. A drawback of such models is the large number of parameters, which requires extensive data volumes for parameter tuning along the training phase. Thus, generative adversarial networks (GANs) are used to synthesize data so that a larger data volume can be produced for training convolutional neural networks (CNNs), which are used for the classification task. Different GAN design approaches were evaluated and both maximum probability and class-expert strategies were exploited for signal classification. Special attention was paid to how the expert knowledge might give a handle on analyzing the performance of the various deep learning models through tests that mirrored actual deployment. An accuracy as high as 99.0±0.4% was achieved using experimental data, which improves upon previous machine learning designs in the field.

/pdf/deep-learning-models-for-passive-sonar-signal-classification-3u460m16.pdf

Deep Learning Models for Passive Sonar Signal Classification of Military Data

In this paper a coding unit early termination algorithm resulting in a fast intra prediction is proposed that terminates complete full search prediction for the coding unit. This is followed by a prediction unit mode decision to find the optimal modes HEVC encoder 35 prediction modes. This includes a two-step process: firstly calculating the Sum of Absolute Differences (SAD) of all the modes by down sampling method and secondly applying a three-step search algorithm to remove unnecessary modes. This is followed by early RDOQ (Rate Distortion Optimization Quantization) termination algorithm to further reduce the encoding time. Experimental results based on several video test sequences for 30 frames from each test sequence show for HEVC a decrease of about 35%-48% in encoding, with negligible degradation in peak signal to noise ratio (PSNR). Metrics such as BD-bitrate (Bjontegaard Delta bitrate), BD-PSNR (Bjontegaard Delta Peak Signal to Noise Ratio) and RD plots (Rate Distortion) are also used.

/pdf/fast-intra-mode-decision-in-high-efficiency-video-coding-nhpd355i23.pdf

Fast Intra Mode Decision In High Efficiency Video Coding

Many underwater acoustic signal processing algorithms are designed for use in stationary and/or Gaussian noise. While these assumptions are often valid for applications in deep water ocean areas, they may not be appropriate for shallow water areas, especially in the presence of local shipping activity. Local shipping also produces spatial correlation in the noise and introduces additional complexity for multichannel processing. In this paper, two 30-minute sets of ambient ocean noise, recorded near the San Diego, California coast, are analyzed for stationarity and Gaussianity using the Kolmogorov-Smirnov test. Since processing algorithms based on higher order statistics often assume Gaussianity, time-dependent fluctuations in the third and fourth order cumulants are also analyzed. The analysis reveals significant variability in the time lengths of stationary periods, and episodic periods of nonGaussianity that last for up to five minutes. Statistical fluctuations appear predominantly in the second and fourth order cumulants rather than the third order cumulant. The shipping noise is also shown to be correlated between pairs of hydrophones with the level of correlation varying over time and the correlation ranging from positive to negative with increasing channel separation.

Variability in higher order statistics of measured shallow-water shipping noise

Prefiltering, or limiting the spectral domain of data, improves maximum magnitude correlation peak detectors, both ordinary and higher order, for known and unknown sources. The only exception is the matched filter, which intrinsically contains prefiltering. For the cases studied, prefiltering generally has a higher order advantage, i.e., for higher order and in higher dimensions, it is even more effective than in one dimension for the ordinary correlation. Geometrical considerations can give some insight into this advantage. The tricorrelation detector with prefiltering performs best for all eight tested signals in the unknown source tests, and is the best detector for seven of the eight signals in the known source tests. Prefiltering for higher order correlation detectors involves only one-dimensional filtering and so is computationally efficient. >

Prefiltering for higher order advantage

This paper presents an evaluation of second, third, and fourth-order moments for the passive detection of transient signals in both simulated Gaussian noise and measured noise. The measured noise was recorded by a vertical array located near the San Diego, CA, harbor and is dominated at low frequencies by ship-generated noise. The detectors assume neither noise nor signal stationarity and can use single or multiple channels of data. Simulation results indicate that the fourth-order moment detector often performs better than the energy detector in the correlated measured noise, with increasing channel contributions to the moment function, resulting in increased gain. The results in simulated Gaussian noise likewise favor the fourth-order moment detector, at least for the signals with significant fourth-order moments, but the ability of the higher order detector to discriminate against correlated noise is evident. Analysis over a 30-min segment of the measured noise with selected signals demonstrates that fourth-order detection gains can be reliably expected as the noise statistics change.

http://ieeexplore.ieee.org/iel5/48/28497/01273570.pdf

Multichannel moment detectors for transients in shallow-water noise

A comparison between second and fourth order moment detectors is made for three low frequency transient signals embedded in measured ambient shipping noise from a shallow-water area. Detector evaluations must be made over short periods of time because of the nonstationary nature of the noise. Results indicate that the fourth order moment detects better than the second order moment for the two nonGaussian transient signals, but not as well for the more Gaussian transient signal. This suggests that the fourth order moment detector be used in addition to the second order moment detector, rather than as a replacement. The results of detection simulations with independent Gaussian noise are given for comparison to the results with the measured shipping noise. The comparison indicates that gains for the fourth order moment detector over the second order moment detector are higher in the shipping noise than in the Gaussian noise for the two nonGaussian signals.

Higher order moment detection of transients in measured shallow-water noise

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Variability in higher order statistics of measured shallow-water shipping noise

Prefiltering for higher order advantage

Multichannel moment detectors for transients in shallow-water noise

Higher order moment detection of transients in measured shallow-water noise