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.

We propose computationally efficient carrier frequency offset estimators for multicarrier underwater acoustic communication using identical pilot tones equi-spaced in the frequency domain. The first estimator uses the phase of the maximal eigenvector of a channel-dependent correlation matrix. Next, the phase of the minimal eigenvector of a channel-independent correlation matrix is combined with the first estimation using a weighted linear least squares principle. The third estimator solves a generalized eigenvalue decomposition problem by jointly considering the two correlation matrices, and then performs a similar second step as the previous estimator. Simulations and pool trials show that the proposed estimators achieve similar performance as common estimation techniques while surpassing them in severe environments.

This paper addresses the direct geolocation of sources in one step via a multi-base (or multi-array) context. The 1-step methods such as DPD and LOST are working on a global array composed of all the sensors of each base. However, even if these algorithms introduce a narrowband decomposition (unfortunately imperfect), these recent powerful algorithms can be disturbed by the residual broadband effect due to the partial coherence of signals (even incoherent signals) between stations. The main contribution of this work is to study the DPD performance in presence of a residual array-broadband effect.

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.

Two novel cooperative localization algorithms for mobile wireless networks are proposed. To continuously localize the mobile network, given the pairwise distance measurements between different wireless sensor nodes, we propose to use subspace tracking to track the variations in signal eigenvectors and corresponding eigenvalues of the double-centered distance matrix. We compare the computational complexity of the new algorithms with a recently developed algorithm exploiting the extended Kalman filter (EKF) and show that our proposed algorithms are computationally efficient, and hence, appropriate for practical implementations compared to the EKF. Simulation results further illustrate that the proposed algorithms are more accurate when the distance errors are small (low noise scenarios) in comparison with the EKF, while being more robust to the sampling period in high noise scenarios.

In the distributed linear source coding problem a set of distributed sensors observe subsets of a data vector, and provide the fusion center with linearly encoded data. The goal is to determine the encoding matrix of each sensor such that the fusion center reconstructs the entire data vector with minimum mean square error (MSE). The recently proposed local Karhunen Loève transform (KLT) approach performs this task by optimally determining the encoding matrix of each sensor assuming the other matrices are fixed. This approach is implemented iteratively until convergence is reached. Herein, we propose a greedy-based non-iterative algorithm. In each step, one of the encoding matrices is updated by appending an additional row. The algorithm selects in a greedy fashion one sensor that provides the largest improvement in MSE, and terminates when all the encoding matrices reach their predefined encoded data size. The algorithm can be implemented recursively, and it reduces the complexity from cubic dependency on the data size, using the iterative method, to quadratic dependency. This makes it a prime candidate for on-line and real-time implementations of the distributed KLT. Simulation results show that for many covariance matrix types, the MSE performance of the suggested algorithm is equivalent to the iterative approach.

We consider the problem of node localization given partial pairwise distance measurements. Current solutions first complete the missing distances and then apply the classical multidimensional scaling (MDS) algorithm. Instead, we extend the classical MDS to a setup where the sensor network is composed of a fully connected group of nodes that communicate with each other (e.g., beacons), and a group of nodes that cannot communicate with each other, but each one of them communicates with each node in the first group. The positions of all nodes are unknown. We localize the fully connected nodes by exploiting their distance measurements to the disconnected nodes. At the same time, the positions of the disconnected nodes are obtained up to a translation relative to the positions of the connected nodes. Recovering this translation, can be obtained with an additional step. Simulation results show that the proposed algorithm outperforms current MDS-like solutions to the problem.

Collaborative beamforming is an approach where sensor nodes in a wireless sensor network, deployed randomly in an area of interest, transmit a common message by forming a beampattern towards a destination. Previous statistical analysis of the averaged power beampattern considered multipath-free conditions. Herein, we express the averaged power beampattern when the signal is observed at the destination in the presence of local scattering. Assuming the spreading angles are uniformly distributed around the destination direction, we derive closed-form expressions for the maximum gain and numerically examine the beamwidth as a function of the number of nodes, the cluster size, and the scattering parameters, for node positions with a uniform distribution or a Gaussian distribution.

Fundamental limitations on estimation accuracy are well known and include a variety of lower bounds including the celebrated Cramer Rao Lower Bound. However, similar theoretical limitations on resolution have not yet been presented. We exploit results from detection theory for deriving fundamental limitations on resolution. In this paper we discuss the resolution of two zero mean complex random Gaussian signals with a general and predefined covariance matrix observed with additive white Gaussian noise. The results are not based on any specific resolution technique and thus hold for any method and any resolution success rate. The theoretical limit is a simple expression of the observation interval, the user's pre-specified resolution success rate and the second derivative of the covariance matrix. We apply the results to the bearing resolution of two emitters with closely spaced direction of arrival impinging on an array of sensors. The derived limits are verified experimentally by model order selection methods such as the Akaike Information Criterion and the Minimum Description Length.

Fundamental limitations on estimation accuracy are well known and include a variety of lower bounds including the celebrated Cramer Rao Lower Bound. However, similar theoretical limitations on resolution have not yet been presented. We exploit results from detection theory for deriving fundamental limitations on resolution. The results are general and are not based on any specific resolution technique and therefore hold for any method and for any resolution success rate. We show that for signals with additive white Gaussian noise the resolution is given by a simple expression related to signal to noise ratio, the signals waveforms and the resolution success rate. As an example, we discuss the resolution of two sinusoids with closely spaced frequencies. The result is compared with the empirical performance of the Akaike information criterion, and the Minimum Description Length criterion for model order selection.

The most common methods for localization of radio signal emitters are based on measuring a specified parameter such as signal angle-of-arrival (AOA) or time-of-arrival (TOA). The measured parameters are then used to estimate the transmitter location. Since the measurements are done at each base station independently, without using the constraint that all AOA/TOA estimates must correspond to the same transmitter, they are sub-optimal. Moreover, if the number of array elements at each base station is M, the number of cochannel simultaneous transmitters that can be localized is M-1. We propose a technique that uses exactly the same data as the common AOA/TOA methods, but the position determination is direct. The proposed method can handle LM-1 cochannel simultaneous signals, using L base stations. Although there are many stray parameters, only a two-dimensional search is required for a planar geometry and a three-dimensional search for the general case. The technique exploits the principles of the MUSIC algorithm, and provides a natural solution to the measurements-sources association problem that is encountered in AOA/TOA based systems.