Effective Acoustic Energy Sensing Exploitation for Target Sources Localization in Urban Acoustic Scenes

This letter proposes a new approach to improve the accuracy of the Energy-based source localization methods in urban acoustic scenes. The proposed acoustic energy sensing flow estimation (ESFE) uses the sensors signal nonstationarity degree to determine the area with highest energy concentration in the scenes. The ESFE is applied to different acoustic scenes and yields to source localization accuracy improvement with computational complexity reduction. The experiments results show that the proposed scheme leads to significant improvement in source localization accuracy

Hybrid Sparse Array Beamforming Design for General Rank Signal Models

The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR) for both single point source and multiple point sources, operating in an interference active environment. Unlike existing sparse design methods which either deal with structured environment-independent or non-structured environment-dependent arrays, our method is a hybrid approach and seeks a full augumentable array that optimizes beamformer performance. This approach proves important for limited aperture that constrains the number of possible uniform grid points for sensor placements. The problem is formulated as quadratically constraint quadratic program (QCQP), with the cost function penalized with weighted l_1-norm squared of the beamformer weight vector. Simulation results are presented to show the effectiveness of the proposed algorithms for array configurability in the case of both single and general rank signal correlation matrices. Performance comparisons among the proposed sparse array, the commonly used uniform arrays, arrays obtained by other design methods, and arrays designed without the augmentability constraint are provided

Sparse Array Beamformer Design for Active and Passive Sensing

Sparse sensor placement, with various design objectives, has successfully been employed in diverse application areas, particularly for enhanced parameter estimation and receiver performance. The sparse array design criteria are generally categorized into environment-independent and environment-dependent performance metrics. The former are largely benign to the underlying environment and, in principle, seek to maximize the spatial degrees of freedom by extending the coarray aperture. Environment-dependent objectives, on the other hand, consider the operating conditions characterized by emitters and targets in the array field of view, in addition to receiver noise. In this regard, applying such objectives renders the array configuration as well as the array weights time-varying in response to dynamic and changing environment. This work is geared towards designing environment-dependent sparse array beamformer to improve the output signal-to-interference and noise ratio using both narrowband and wideband signal platforms. One key challenge in implementing the data-dependent approaches is the lack of knowledge of exact or estimated values of the data autocorrelation function across the full sparse array aperture. At the core of this work is to address the aforementioned issues by devising innovative solutions using convex optimization and machine learning tools, structured sparsity concepts, low rank matrix completion schemes and fusing the environment-dependent and environment-independent deigns by developing a hybrid approach.Comment: PhD thesi