SC

S.P. Chepuri

57 records found

In this paper, the focus is on the gain and phase calibration of sparse sensor arrays to localize more sources than the number of physical sensors. The proposed technique is a blind calibration method as it does not require any calibrator sources. Joint estimation of the gain err ...
We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-s ...
Multiple-input multiple-output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to ...
Orthogonal signal-division multiplexing (OSDM) is a promising modulation scheme that provides a generalized framework to unify orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. By partitioning each data block into vectors, it allo ...
In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of intere ...
Distributed multiple-input multiple-output (MIMO) radars can use multiple transmitters and receivers simultaneously to detect targets.In order to maximize the probability of target detection,it is necessary to allocate the available transmit power resources suitably. The optimal ...
In this work, we address the problem of identifying the underlying network structure of data. Different from other approaches, which are mainly based on convex relaxations of an integer problem, here we take a distinct route relying on algebraic properties of a matrix representat ...
In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selec ...
Detection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available sensors, referred to as sparse sensing, that ...
In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the ...
In this paper, we present a calibration algorithm for acoustic vector sensors arranged in a uniform linear array configuration. To do so, we do not use a calibrator source, instead we leverage the Toeplitz blocks present in the data covariance matrix. We develop linear estimators ...

Statistical Graph Signal Processing

Stationarity and Spectral Estimation

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many contemporary applications the information of interest resides in more irregular domains that can ...
An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present ...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary graphs or arbitrary signals that may be represented using graphs, where we extend concepts from generalized sampling theory to the graph setting. To recover such signals from a gi ...
In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostl ...
Factor analysis decomposition, i.e., decomposition of a covariance matrix as a sum of a low-rank positive semidefinite matrix and a diagonal matrix is an important problem in a variety of areas, such as signal processing, machine learning, system identification, and statistical i ...
In this paper, we show the advantages of spatially under-sampled acoustic vector sensor (AVS) arrays over conventional acoustic pressure sensor (APS) arrays for performing direction-of-arrival (DOA) estimation and interference cancellation. We provide insights into the theoretica ...
We study the sensor selection problem for field estimation, where a best subset of sensors is activated to monitor a spatially correlated random field. Different from most commonly used centralized selection algorithms, we propose a decentralized architecture where sensor selecti ...
In this paper, the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defin ...
In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the grap ...