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For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori knowledge of matrix structure. In particular, we consider linearly structured matrices, such as Hankel and Toeplitz, as(More)
In this paper, we propose a robust transmit beampattern design for multiple-input multiple-output (MIMO) radar systems. The objective considered here is minimization of the beampattern sidelobes, subject to constraints on the transmit power where the waveform co-variance matrix is the optimization variable. Motivated by the fact that the steering vectors(More)
—The implementation challenges of cooperative local-ization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning(More)
For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space. For such signals, the developed algorithm is able to incorporate sequential predictions, thereby providing better compressive sensing recovery performance, but not at the(More)
We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean. Since finding the conditional mean requires multidimensional(More)
—Pedestrian Dead-Reckoning (PDR) and Radio Frequency (RF) ranging/positioning are complementary techniques for position estimation but they usually locate different points in the body (RF in the head/hand and PDR in the foot). We propose to fuse the information from both navigation points using a constraint filter with an upper bound in the distance between(More)
In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an(More)
—A method is proposed to fuse the information from two navigation systems whose relative position is unknown, but where there exists an upper limit on how far apart the two systems can be. The proposed information fusion method is applied to a scenario in which a pedestrian is equipped with two foot-mounted zero-velocity-aided inertial navigation systems;(More)