Exploiting platform motion for passive source localization with a co-prime sampled large aperture array.

Co-prime array geometries have received a great deal of attention due to their ability to discriminate O(MN) sources with only O(M + N) sensors. This has been demonstrated both theoretically and in simulation. However, there are many practical limitations that make it difficult to realize the enhanced degrees of freedom when applying co-prime geometries to real acoustic data taken on a horizontal line array. For instance, co-prime sampling leads to grating lobes that can obscure lower signal-to-noise-ratio acoustic signals making them difficult to detect. In this work, a synthetic aperture (SA) method is presented for filling in holes and increasing redundancy in the difference co-array by exploiting array motion. The SA method is applied to acoustic data collected off the Southeastern shore of Florida on a fixed large aperture horizontal array. Array motion is simulated by taking a co-prime sampled subarray and virtually moving it along the horizontal aperture of the fixed array. It is demonstrated that SA processing on real acoustic data results in reduced side-lobe and grating lobe levels compared to that of the physical co-prime aperture.

A performance metric for screen selection with the acoustic single pixel imager.

In the recent literature, an Acoustic Single-Pixel Imager has been successfully developed for source localization in a two-dimensional waveguide. Source bearing angle estimation was carried out by applying sparse recovery techniques on sensor measurements taken over different imaging screens. This paper shows that the Mutual Coherence of the sensing matrix can be used as a metric to predict the source localization capability of the single-pixel imaging system. In particular, this paper's analysis focuses on the sparsity of open cells within the imaging screen and the number of imaging screens used to maximize the probability of correct detection over varying levels of source sparsity. In this work, a simulation environment to demonstrate how the mutual coherence of the sensing matrix correlates with source localization performance over source sparsity, sparsity of open screen cells, and number of measurements used for sparse recovery is developed. The analysis shows that the leading factor in source localization performance gains is primarily from the number of imaging screens used to measure the acoustic wave-field.

Beamforming using chip-scale atomic clocks in a controlled environment.

Recently developed low-power Chip-Scale Atomic Clocks (CSACs) hold promise for underwater acoustics applications because they enable time-coherent processing, critical for estimating the directionality of the sound field, when acoustic array elements cannot share a timing reference. Controlled, tank-based experiments with a small acoustic array (N = 4) featuring CSAC-equipped elements show that optimal disciplining is important for continued array coherence. Clock drift equivalent to a 10% wavelength error at 0.3, 1, and 10 kHz was reached at approximately 25, 10, and 3 days, respectively. Within application-specific limits, this technology brings enhanced capabilities to acoustic thermometry, geoacoustic, biological, and under-ice acoustic oceanography.

Ambient noise forecasting with a large acoustic array in a complex shallow water environment.

Forecasting ambient noise levels in the ocean can be a useful way of characterizing the detection performance of sonar systems and projecting bounds on performance into the near future. The assertion is that noise forecasting can be improved with a priori knowledge of source positions coupled with the ability to resolve closely separated sources in bearing. One example of such a system is the large aperture research array located at the South Florida Test Facility. Given radar and Automatic Identification System defined source positions and environmental information, transmission loss (TL) is computed from known source positions to the array. Source levels (SLs) of individual ships are then estimated from computed TL and the pre-determined beam response of the array using a non-negative least squares algorithm. Ambient noise forecasts are formed by projecting the estimated SLs along known ship tracks. Ambient noise forecast estimates are compared to measured beam level data and mean-squared error is computed. A mean squared error as low as 3.5 dB is demonstrated in 30 min forecast estimates when compared to ground truth.

Maximum-likelihood spatial spectrum estimation in dynamic environments with a short maneuverable array.

This work concerns the development of field directionality mapping algorithms for short acoustic arrays on mobile maneuverable platforms that avoid the left/right ambiguities and endfire resolution degradation common to longer non-maneuverable line arrays. In this paper, it is shown that short maneuverable arrays can achieve a high fraction of usable bearing space for target detection in interference-dominated scenarios, despite their lower array gain against diffuse background noise. Two narrowband techniques are presented which use the expectation-maximization maximum likelihood algorithm under different models of the time-varying field directionality. The first, derivative based maximum likelihood, uses a deterministic model while the second, recursive Bayes maximum likelihood, uses a stochastic model for the time-varying spatial spectrum. In addition, a broadband extension is introduced that incorporates temporal spectral knowledge to suppress ambiguities when the average sensor array spacing is greater than a half-wavelength. Dynamic multi-source simulations demonstrate the ability of a short, maneuvering array to reduce array ambiguities and spatial grating lobes in an interference dominated environment. Monte Carlo evaluation of receiver operating characteristics is used to evaluate the improvement in source detection achieved by the proposed methods versus conventional broadband beamforming.

Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new 'D-site' class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates.

Time-varying spatial spectrum estimation with a maneuverable towed array.

This paper addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow-ship maneuvers. In this paper, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution toward endfire. The Cramér-Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: (1) Maximum likelihood (ML) estimation solved using the expectation maximization algorithm and (2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics are presented to evaluate the algorithms' detection performance versus signal-to-noise ratio. The results indicate that both FDM algorithms offer the potential to provide superior detection performance when compared to conventional beamforming with a maneuverable array.