IEEE Transactions on Signal Processing

Two-dimensional (2-D) spectrum sensing is addressed in the context of a cognitive radar to gather real-time space-frequency electromagnetic awareness. Assuming a sensor equipped with multiple receive antennas, a discrete-time sensing signal model formally accounting for multiple snapshots of observations is introduced.

Beamforming is an attractive technique to improve the system performance for multi-input multi-output (MIMO) communications. Previous works mainly focus on improving the data transmission quality. However, the potential of beamforming for improving the localization quality is not yet fully studied.

We propose two-channel critically-sampled filter banks for signals on undirected graphs that utilize spectral domain sampling. Unlike conventional approaches based on vertex domain sampling, our transforms have the following desirable properties.

We present our results of applying wavelet theory to the classic problem of estimating the unknown parameters of a model function subject to noise. The model function studied in this context is a generalization of the second-order Gaussian derivative of which the Gaussian function is a special case.

Coprime sensor arrays (CSAs) can estimate the directions of arrival of O(MN) narrowband planewave sources using only O(M + N) sensors with the CSA product processor. All previous investigations on the product processed CSA's performance for detecting Gaussian signals assumed spatially white Gaussian noise.

This paper considers a set of multiple independent control systems that are each connected over a nonstationary wireless channel. The goal is to maximize control performance over all the systems through the allocation of transmitting power within a fixed budget.

Polar codes have received recent attention due to their potential to be applied in advanced wireless communication protocols such as the fifth generation mobile communication system (5G). Among the existing decoding algorithms, Belief Propagation (BP) exhibits high-throughput, low-latency, and soft output with a high hardware cost.

In this paper, we address the estimation of a time-varying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sensors. We employ a radio propagation model where the path loss exponent and the transmitted power are unknown with Gaussian priors whose hyper-parameters are estimated by applying the empirical Bayes method.

We consider the problem of detecting the presence of a complex-valued, possibly improper, but unknown signal, common among two or more sensors (channels) in the presence of spatially independent, unknown, possibly improper and colored, noise. Past work on this problem is limited to signals observed in proper noise.

Motivated by the many applications associated with estimation of sparse multivariate models, the estimation of sparse directional connectivity between the imperfectly measured nodes of a network is studied. Node dynamics and interactions are assumed to follow a multivariate autoregressive model driven by noise, and the observations are a noisy linear combination of the underlying node activities.