In this paper, we propose an incoherent design method of sparse broadband arrays that optimizes the number of sensors and their positions simultaneously. We introduce an iterative clustering procedure that merges different groups of sensors with a small distance, in terms of Bhattacharyya distance, between their angle distributions. The iterative clustering procedure is initialized with a large number of groups of sensors, and computes in each iteration a clustering score and a threshold. Then, near groups are merged into joint groups, yielding a new set of groups of sensors. We show that the optimal set of sensors is obtained when the clustering score is larger than the threshold, indicating that the remaining groups are distant. The proposed approach is demonstrated by a design of a superdirective beamformer, and its performance is compared with an existing incoherent approach. Experimental results show improved performance in terms of a more favorable tradeoff between directivity factor and white noise gain.

A common approach for acoustic source localization is based on finding the maximum of a spatial cost function, such as the steered response power (SRP) function. The shape of the SRP highly depends on the constellation of sensors within the array layout, and have a direct impact on the performance. Thus, an array may be designed to produce high localization performance and small error regions, especially when a spatially prioritized source location distribution is taken into account. We introduce a new measure called power spread, which quantifies the localization error region. Then, we propose a greedy algorithm for a sparse array design, aiming to minimize the power spread for optimal error region in a given area of interest. Simulations demonstrate that the proposed design, compared to standard linear array design and random array design, obtains superior performance in terms of power spread and localization error, with a reasonable computational effort.

Frequency-invariant concentric arrays are fundamental components in some real-world applications, like teleconferencing, voice service devices, underwater acoustics, and others, where the azimuthal arrival direction of the desired signal is varying. The fact that the demand for limited hardware and computational resources in such applications is essential, motivates the use of a sparse design which can optimize both the number of the required sensors and the complex weights of the beamformer. Herein, we propose a new greedy based joint-sparse design of frequency and rotationally invariant concentric arrays which preserves the properties of the designed directivity pattern for different azimuthal directions of steering. Simulation results show that the greedy sparse design, compared to uniform and random designs, gives superior performance in terms of array gain, and frequency and rotationally invariant beampattern, with a reasonable computational and hardware resources.

Frequency-invariant beamformers are used to prevent signal waveform distortions in real world applications like audio, underwater acoustics, and radar. Most of existing methods assume uniform arrays, and only few consider sparse designs, which may lead to higher performance in terms of robustness and directivity factor. We propose an incoherent approach that first determines for each frequency bin a sparse set of sensors positions. Subsequently, by using tools of dimensionality reduction and clustering, these selections are merged together yielding the optimal sensors on a sparse array layout. We present design examples of sparse linear and planar superdirective array designs. We show that the proposed incoherent sparse design obtains superior performance in terms of white noise gain, directivity factor, and computational load compared to a uniform array design and compared to a coherent sparse approach, where the sensors' locations and the beamformer coefficients are optimized simultaneously for all frequencies.

Circular differential microphone arrays (CDMAs) facilitate compact superdirective beamformers whose beampatterns are nearly frequency invariant. In contrast to linear differential microphone arrays where the optimal steering direction is at the endfire, CDMAs provide perfect steering for all azimuthal directions. Herein, we extend the traditional symmetric model of DMAs and establish an analytical asymmetric model for Nth-order CDMAs. This model exploits the circular geometry to eliminate the inherent limitation of symmetric beampatterns associated with a linear geometry and allows also asymmetric beampatterns. This new model is then used to develop asymmetric versions of two optimal commonly used beampatterns namely the hypercardioid and the supercardioid. Experimental results demonstrate the advantages of the asymmetric model compared to the traditional symmetric one, when additional directional constraints are imposed. The proposed model yields superior performance in terms of white noise gain, directivity factor, and front-to-back ratio, as well as more flexible design of nulls for the interfering signals.

We compare two orthogonal modulation schemes for underwater acoustic sensor network using spread spectrum communication. The first which has been recently published, employs a frequency modulation signals spread by the same code. As an alternative, we propose to modulate the signals by using a different orthogonal code for each signal. Simulations and sea trial results demonstrate that the performance of both schemes is quite similar. In general, both schemes provide more flexibility of the system design. The first scheme is mainly suitable to a memory resources limited platform and a high number of network nodes, whereas, the second scheme has better utilization of the bandwidth and low computational complexity.

We present a closed-form least squares algorithm for estimating the position and velocity of a source, asynchronously transmitting known dual linear chirp signals, given times of arrival measurements obtained by spatially distributed sensors. The estimates involve less computational load compared to the optimal maximum likelihood estimates. Simulations show that the proposed estimates have similar mean square errors as the optimal estimates, and are close to the lower bound for small and moderate measurement noise.

In this paper, we present and study a Bayesian focusing transformation (BFT) for coherent wideband array processing, which takes into account the uncertainty of the direction of arrivals (DOAs). The Bayesian focusing method minimizes the mean-square error of the transformation over the probability density functions (pdfs) of the DOAs, thus achieving improved focusing accuracy over the entire bandwidth. In order to solve the Bayesian focusing problem, we derive and utilize a weighted extension of the wavefield interpolated narrowband generated subspace (WINGS) focusing transformation. We provide a closed-form expression for the optimal BFT and extend it to the case of directional sensors. We then consider a numerical computation scheme of the BFT in the angular domain. We show that if an angular sampling condition is satisfied then the angle domain approximation yields the optimal BFT. We also treat the important issue of robust focused minimum variance distortionless response (MVDR) beamformer. We analyze the sensitivity of the focused MVDR to the focusing errors and show that the array gain (AG) is inversely proportional to the square of the signal-to-noise ratio (SNR) for large values of the SNR, and highly sensitive to the focusing errors. In order to reduce this sensitivity we generalize the popular narrowband diagonal loaded MVDR to the focused wideband case, referred to as the Q-loaded focused MVDR wideband beamformer. We derive a closed-form analytic expression for the AG of the Q-loaded focused MVDR beamformer which depends on the focusing transformations. A numerical performance evaluation and simulations demonstrate the advantage of the BFT over that of other focusing transformations, for multiple source scenarios.