34 accepted papers
Earth Observation (EO) data is captured with different instruments and available in multiple formats. The complementation of two passive remote sensing approaches in local small areas is performed here to produce a single, large-area coverage, volumetric soil moisture (VSM) solution. The sensing app…
Leveraging one-bit analog-to-digital converter (ADC) instead of high resolution ADC has been introduced as a promising solution for reducing the power consumption and hardware cost of massive millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. However, performance loss caused by…
We propose a robust activity detection for grant free random access using greedy covariance-learning-based matching pursuit (RCL-MP) algorithm. The method incorporates a robust loss function into the Gaussian negative log-likelihood function, and uses matching pursuit framework for greedily selectin…
Robust hybrid beamforming for integrated sensing and communications (ISAC) system under bounded uncertainties in sensing reception is developed using algorithm unrolling technique. First, the robust hybrid beamforming design problem is formulated as an optimization problem that jointly maximizes the…
In the integrated sensing and communication system, sensing functionalities are expected to benefit the communication instead of compromising its performance. In this paper, a sensing-aided communication channel estimation method is proposed, where the non-cooperative moving targets are localized an…
Most available convolutional dictionary learning (CDL) methods use a batch-learning strategy, which consists of alternating optimization of the dictionary and the sparse representations using a training dataset. The computational efficiency of CDL can be improved using an online-learning approach, w…
Existing matrix-based neural network for direction-of-arrival (DOA) estimation has to train a large amount of parameters proportional to the length of vectorized signal statistics, resulting in a heavy system overload. To address the problem, a tensorized neural layer decomposition-based neural netw…
Recently, Parallel Factor-Direct (PARAFAC-Direct) method has been proposed for parameter estimation including velocity disambiguation for Doppler Division Multiple Access (DDMA) Multiple-Input Multiple-Output (MIMO) radar. However, DDMA MIMO radar spreads the overall transmit energy into the entire…
A novel algorithm for direction-of-arrival (DOA) estimation in nonuniform sensor noise is developed. The diagonal nonuniform sensor noise covariance matrix is estimated by an iterative procedure, which only requires a few iterations. Using the generalized eigendecomposition of two matrices and the l…
A novel method for learning correlated features in multimodal images based on convolutional sparse coding with applications to image fusion is presented. In particular, the correlated features are captured as coupled filters in convolutional dictionaries. At the same time, the shared and independent…
The robust adaptive beamforming (RAB) problem is considered via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty sets for the random interference-plus-noise covariance (INC) matrix and desired signal steering vector. The distributional uncert…
In this paper, a constrained tensor decomposition method that enables two dimensional (2D) direction of arrival (DOA) estimation for transmit beamspace (TB) Multiple-Input Multiple-Output (MIMO) radar with subarrays is proposed. Specifically, a higher-order tensor model is designed to collect the re…
Direction-of-arrival (DOA) estimation problem is a challenging one in the presence of coherent sources, when the sample size is small, and the signal-to-noise ratio is low. We address this problem by developing a new method called enhanced standard ESPRIT (ES ESPRIT), and also its unitary extension…
In this paper, a general sparse array (SA) design principle, called uniform linear array (ULA) fitting, is proposed. It uses concatenation of sub-ULAs to design SAs with feasible difference coarrays (DCAs). Motivation for the ULA fitting is that the nested array and coprime array are in fact the exa…
We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm which visits merely a single branch of the search tree of the sphere decoding (SD) algorithm, while maintaining the DMT optimality at high signal to noise ratio (SNR) regime. The search tree of the sphe…
With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information. For graph signals, such prior information…
The image fusion problem consists in combining complementary parts of multiple images captured, for example, with different focal settings into one image of higher quality. This requires the identification of the sharpest areas in sets of input images. Recently, it was shown that coupled dictionary…
The worst-case robust adaptive beamforming problem for generalrank signal model is considered. This is a nonconvex problem, and an approximate version of it (by introducing a matrix decomposition on the presumed covariance matrix of the desired signal) has been studied in the literature. Herein the…
The MVDR robust adaptive beamforming design problem based on estimation of the signal-of-interest (SOI) steering vector is considered. In this case, the optimal beamformer is obtained by computing the sample matrix inverse and an optimal estimate of the SOI steering vector. In order to find the opti…
An analytical lower bound on uplink channel capacity of a user in a massive multiple-input multiple-output system where the channel vector and the covariance matrices of the users in that cell are unknown is derived in this paper. This analytical bound enables us to choose appropriate sample size fo…
Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph…
The first step in any graph signal processing (GSP) task is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors…
A novel computationally efficient method for jointly designing the space-(slow) time (SST) transmission with unimodular waveforms and receive adaptive filter is developed for different radar configurations. The range sidelobe effect and Doppler characteristics are considered. In particular, we devel…
Millimeter wave (mmWave) multiple-input multiple-output (MIMO) transceivers employ narrow beams to obtain a large array-gain, rendering them sensitive to changes in the angles of arrival and departure of the paths. Since the singular vectors that span the channel subspace are used to design the prec…
Most of the existing ultrasound image restoration methods consider a spatially-invariant point-spread function (PSF) model and circulant boundary conditions. While computationally efficient, this model is not realistic and severely limits the quality of reconstructed images. In this work, we address…
The millimeter wave WLAN standard can be used for joint communication-radar by exploiting the waveform preamble as a radar pulse. The velocity estimation accuracy with this approach, however, is limited due to the short integration time. A physical increase in the radar pulse integration duration, h…
The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is regarded as the state-of-the-art among a number of proximal gradient-based methods used for addressing large-scale optimization problems with simple but non-differentiable objective functions. However, the efficiency of FISTA in a wide r…
A new method for designing single/multiple unimodular waveforms with good weighted correlation properties, which is based on minimizing the weighted integrated sidelobe levels of waveforms, is developed. The main contributions of the paper lie in formulating the objective as a quartic form where Had…
In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed (TM) pilots exhibit a complementary behavior, with the former and latter schemes offering a higher throughput in high and low inter-cell interference scenarios, respectively. Based on this observation, i…
A novel multi-focus image fusion approach using coupled dictionary training is proposed. It exploits the facts that (i) the patches in example data can be sparsely represented by a couple of over-complete dictionaries related to the focused and blurred categories of images and (ii) merging such repr…
Superimposed pilots are proposed as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output systems. Provided that the uplink duration is larger than the total number of users in the system, superimposed pilots enable eac…
We address the problem of terrain-scattered jammer suppression in multiple-input multiple-output (MIMO) radar using space-(fast) time adaptive processing (SFTAP). The correlation function of jamming components after matched filtering at the receiving end of MIMO radar is derived, and its relationshi…
In this paper, the problem of joint hot and cold clutter mitigation in the context of transmit beamspace (TB)-based multipleinput multiple-output (MIMO) radar is studied. The TB-based MIMO radar enables special spatio-temporal structure and low rank of clutter covariance matrices. To efficiently mit…
Subspace based methods provide a good compromise between performance and complexity. However, these methods are exposed to performance breakdown at the low SNR and/or small sample size region. It has been known for a long time that a major reason for such performance breakdown is the subspace swap p…