Piya Pal

Bio: Piya Pal is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Matrix (mathematics) & Covariance matrix. The author has an hindex of 23, co-authored 109 publications receiving 4495 citations. Previous affiliations of Piya Pal include California Institute of Technology & University of Maryland, College Park.

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

Abstract: A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide O(N2) degrees of freedom using only N physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input-minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations.

Sparse Sensing With Co-Prime Samplers and Arrays

Abstract: This paper considers the sampling of temporal or spatial wide sense stationary (WSS) signals using a co-prime pair of sparse samplers. Several properties and applications of co-prime samplers are developed. First, for uniform spatial sampling with M and N sensors where M and N are co-prime with appropriate interelement spacings, the difference co-array has O(MN) freedoms which can be exploited in beamforming and in direction of arrival estimation. An M -point DFT filter bank and an N-point DFT filter bank can be used at the outputs of the two sensor arrays and their outputs combined in such a way that there are effectively MN bands (i.e., MN narrow beams with beamwidths proportional to 1/MN), a result following from co-primality. The ideas are applicable to both active and passive sensing, though the details and tradeoffs are different. Time domain sparse co-prime samplers also generate a time domain co-array with O(MN) freedoms, which can be used to estimate the autocorrelation at much finer lags than the sample spacings. This allows estimation of power spectrum of an arbitrary signal with a frequency resolution proportional to 2π/(MNT) even though the pairs of sampled sequences xc(NTn) and xc(MTn) in the time domain can be arbitrarily sparse - in fact from the sparse set of samples xc(NTn) and xc(MTn) one can estimate O(MN) frequencies in the range |ω| <; π/T. It will be shown that the co-array based method for estimating sinusoids in noise offers many advantages over methods based on the use of Chinese remainder theorem and its extensions. Examples are presented throughout to illustrate the various concepts.

Coprime sampling and the music algorithm

Abstract: A new approach to super resolution line spectrum estimation in both temporal and spatial domain using a coprime pair of samplers is proposed. Two uniform samplers with sample spacings MT and NT are used where M and N are coprime and T has the dimension of space or time. By considering the difference set of this pair of sample spacings (which arise naturally in computation of second order moments), sample locations which are O(MN) consecutive multiples of T can be generated using only O(M + N) physical samples. In order to efficiently use these O(MN) virtual samples for super resolution spectral estimation, a novel algorithm based on the idea of spatial smoothing is proposed, which can be used for estimating frequencies of sinusoids buried in noise as well as for estimating Directions-of-Arrival (DOA) of impinging signals on a sensor array. This technique allows us to construct a suitable positive semidefinite matrix on which subspace based algorithms like MUSIC can be applied to detect O(MN) spectral lines using only O(M + N) physical samples.

Theory of Sparse Coprime Sensing in Multiple Dimensions

Abstract: Coprime sampling and coprime sensor arrays have been introduced recently for the one-dimensional (1-D) case, and applications in beamforming and direction finding discussed. A pair of coprime arrays can be used to sample a wide-sense stationary signal sparsely, and then reconstruct the autocorrelation at a significantly denser set of points. All applications based on autocorrelation (e.g., spectrum and DOA estimation) benefit from this property. It was also shown in the past that coprimality can be exploited in the frequency domain by using a pair of coprime DFT filter banks, to produce the effect of a much denser tiling in the frequency domain, compared to what the two filter banks can individually achieve. This paper extends these ideas to multiple dimensions. In the 1-D case the samples or sensors lie on a pair of uniform grids, whereas in the multidimensional case, they lie on a pair of multidimensional lattices, not necessarily rectangular. This makes the developments mathematically more intricate. First several properties of coarrays of lattices are derived. It is shown how one can get dense coarrays from sparse arrays on non rectangular lattices. This requires that the lattice generating matrices M and N be commuting and coprime (to be defined). Multidimensional DFT filter banks for applications such as beamforming, with commuting coprime lattice arrays, are then described, and it is shown that a very dense tiling of the frequency plane can be obtained from the two sparse lattice arrays. A particular family of commuting coprime matrices called adjugate pairs are considered in some detail, and shown to have attractive properties. A brief review of the 1-D case is included at the beginning for convenience.

Nested Arrays in Two Dimensions, Part I: Geometrical Considerations

Abstract: A new class of two dimensional arrays with sensors on lattice(s) is proposed, whose difference co-array can give rise to a virtual two dimensional array with much larger number of elements on a “dense” lattice. This structure is obtained by systematically nesting two arrays, one with sensors on a sparse lattice and the other on a dense lattice where the lattices bear a certain relation with each other. The difference co-array of such an array with M and N elements respectively on the two lattices, is a two dimensional array with O(MN) elements present contiguously (without holes) on the dense lattice. The difference co-array can be realized on any arbitrary lattice by choosing the dense and sparse lattices appropriately. The generator matrices of the sparse and the dense lattices are related by an integer matrix. The Smith form of this integer matrix is shown to provide a very insightful perspective which is exploited heavily in the construction of nested arrays. The design of the two dimensional nested array gives rise to several interesting geometrical orientations of the co-array which are addressed in detail, and it is shown how the orientations can be manipulated to yield more virtual sensors in a continuum on the dense lattice. The increased number of elements in the virtual difference co-array can be exploited to perform two dimensional direction of arrival (DOA) estimation of many more sources than what traditional methods can achieve. A novel algorithm for application of the nested array in two dimensional direction of arrival estimation is reported in the accompanying part II of the paper.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

Abstract: A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide O(N2) degrees of freedom using only N physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input-minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations.