A log-likelihood gridless sparse direction-of-arrival (DOA) estimation is presented. The likelihood fit is optimized using the sample covariance matrix and a reconstructed covariance matrix constrained to a few atoms. This approach enables using Wirtinger gradients for DOA. The sensitivity to local minima is mitigated by initializing with the best DOAs from a gridded DOA method. In simulations, the method achieves the Cramer-Rao bound and offers superior resolution compared to conventional gridless DOA methods.

Predicting ocean acoustic fields in shallow water is challenging due to high spatial variability, with depth scales of 100 m and range scales of 1 km. Limited acoustic data further complicates this task. We propose a physics-informed neural network (PINN) with the Helmholtz equation as a physics constraint, enhancing prediction accuracy with scarce data. A preprocessing step using an envelope smoothing technique is introduced. This reduces the spatial field variability, enabling more accurate training of the PINN than purely data-driven approaches. Our method is validated through ocean data, demonstrating substantial improvements in PINN performance for complex ocean acoustic predictions.

Gridless direction-of-arrival (DOA) estimation with multiple frequencies can be applied to acoustic source localization. We formulate this as an atomic norm minimization (ANM) problem and derive a regularization-free semi-definite program (SDP) avoiding regularization bias. We also propose a fast SDP program to deal with non-uniform frequency spacing. The DOA is retrieved via irregular Vandermonde decomposition (IVD), and we theoretically guarantee the existence of the IVD. We extend ANM to the multiple measurement vector setting and derive its equivalent regularization-free SDP. For a uniform linear array using multiple frequencies, we can resolve more sources than the sensors. The effectiveness of the proposed framework is demonstrated via numerical experiments.

A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate t-distribution (MVT) with ν degrees of freedom, as well as Huber and Tyler loss functions. For Gauss loss, the method reduces to Sparse Bayesian Learning (SBL). The root mean square DOA error of the derived estimators is discussed for Gaussian, MVT, and ϵ-contaminated data. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian array data.

We present a sparsity-promoting method for the detection and estimation of the directions of arrival (DOAs) of source signals. The proposed method is based on the recently introduced variational Bayesian line spectral estimation (VALSE) approach, which is gridless. However, the performance of VALSE is sensitive to an initial guess of the measurement noise variance and potential DOAs. Thus, we propose a sparse Bayesian learning-aided initialization. Simulation results show that this learning-aided VALSE outperforms state-of-the-art DOA estimation methods as well as the conventional VALSE. We also evaluate the proposed method using acoustic data from an ocean acoustics experiment.

The direction-of-arrivals (DOAs) of plane waves in a high-frequency region are estimated without spatial aliasing using multi-frequency processing. The method exploits the difference frequency (DF), the difference between two high frequencies. This enables processing data in a feasible region without spatial aliasing. We analyze DOA characteristics upon DF processing and propose a MUSIC-based method dealing with multi-DF and multi-snapshot. Multiple DFs having the same frequency difference allow processing multi-DF equivalently to multi-snapshot. We propose a method that considers all DFs and snapshots jointly and a joint DF method providing a single snapshot DF-MUSIC that does not require stationary DOAs. Numerical examples validate the effectiveness of the proposed method and its DOA performance is discussed.

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.

We present a Bayesian method for sequential direction finding based on variational line spectral estimation (VALSE). The proposed method promotes sparse solutions by means of a Bernoulli-Gaussian amplitude model, is grid-less, and provides marginal posterior distributions from which DOA estimates and their uncertainties can be extracted. Simulation results demonstrate performance improvements in the considered scenario. We also evaluate the proposed method using acoustic data from an underwater source localization experiment.

This paper introduces compressive sensing (CS) based two-dimensional (2-D) off-grid direction-of-arrival (DOA) estimation approach which can output the azimuths and elevations of radiating sources for propeller tip vortex cavitation localization. With a discretized angular search-grid of the conventional CS based approach, grid mismatch deteriorates the DOA estimation performance. To obtain the off-grid estimation performance, we formulate the 2-D off-grid DOA estimation problem into a block-sparse CS framework. In addition, the presented method can be applied to arrays of arbitrary geometry with no array configuration constraint. The approach is illustrated by numerical simulations and experimental data (cavitation tunnel experiment).

An alternative method for solving the gridless direction-of-arrival (DOA) estimation problem is presented. Gridless DOA estimation involves solving the semi-definite characterization of a rank minimization problem. We show that the original non-convex formulation of the gridless DOA estimation problem can be solved efficiently using the method of alternating projections (AP). We deem our solution `alternating projections based gridless DOA estimation,' or APG. Using insight from the derivation of APG we present a reduced dimension variation of APG, (RD-APG). The presented algorithms are compared in speed and accuracy to gridless DOA estimation solved using the current state of the art SDP solver.

In this research, we have developed a Multi-Agent based Negotiation Support System to enhance the competitive power of a company in dynamic environments and correspond to various orders from customers by capitalizing on electronic commerce. The system uses the agent technology that comes to light as a new paradigm in dynamic environment and flexible system framework. The multi-agent technology is used to solve these problems through cooperation between agents. The system consists of six sub agents: mediator, manufacturability analysis agent, process planning agent, scheduling agent, selection agent and negotiation strategy building agent. In this paper, the proposed Multi-Agent based Negotiation Support System aims at the automation of transaction process from order to manufacturing plan through negotiation automation that is the most important in a series of business transactions.