Channel Protection: Random Coding Meets Sparse Channels

Multipath interference is an ubiquitous phenomenon in modern communication systems. The conventional way to compensate for this effect is to equalize the channel by estimating its impulse response by transmitting a set of training symbols. The primary drawback to this type of approach is that it can be unreliable if the channel is changing rapidly. In this paper, we show that randomly encoding the signal can protect it against channel uncertainty when the channel is sparse. Before transmission, the signal is mapped into a slightly longer codeword using a random matrix. From the received signal, we are able to simultaneously estimate the channel and recover the transmitted signal. We discuss two schemes for the recovery. Both of them exploit the sparsity of the underlying channel. We show that if the channel impulse response is sufficiently sparse, the transmitted signal can be recovered reliably.Comment: To appear in the proceedings of the 2009 IEEE Information Theory Workshop (Taormina

Compressive Matched-Field Processing

Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then, the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented which demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP, but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area

Parametric estimation of randomly compressed functions

Within the last decade, a new type of signal acquisition has emerged called Compressive Sensing that has proven especially useful in providing a recoverable representation of sparse signals. This thesis presents similar results for Compressive Parametric Estimation. Here, signals known to lie on some unknown parameterized subspace may be recovered via randomized compressive measurements, provided the number of compressive measurements is a small factor above the product of the parametric dimension with the subspace dimension with an additional logarithmic term. In addition to potential applications that simplify the acquisition hardware, there is also the potential to reduce the computational burden in other applications, and we explore one such application in depth in this thesis. Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. We introduce a technique that mitigates this computational workload by ``compressing'' these computations. Drawing on key concepts from the recently developed field of compressed sensing, we show how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then, the source can be located by performing a number of ``short'' correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented which demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP, but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area. This thesis also introduces a round-robin approach for multi-source localization based on Matched-Field Processing. Each new source location is estimated from the ambiguity function after nulling from the data vector the current source location estimates using a robust projection matrix. This projection matrix effectively minimizes mean-square energy near current source location estimates subject to a rank constraint that prevents excessive interference with sources outside of these neighborhoods. Numerical simulations are presented for multiple sources transmitting through a generic Pekeris ocean waveguide that illustrate the performance of the proposed approach which compares favorably against other previously published approaches. Furthermore, the efficacy with which randomized back-propagations may also be incorporated for computational advantage (as in the case of compressive parametric estimation) is also presented.Ph.D

Distributed alternating localization-triangulation of camera networks

Localization, estimating the positions and orientations of a set of camera nodes, is a critical first step in camera-based sensor network applications such as geometric estimation and scene reconstruction. We propose a distributed algorithm for camera network localization based on feature point correspondences between multiple cameras with sparse overlapping view structure. We prove convergence of the iterative piecewise-linearized algorithm using the projection onto convex sets (POCS) principle, since this algorithm corresponds to projecting onto subspaces that approximately overlap. We provide bounds on convergence rates and worst-case errors and show experimental results from actual images. Finally, we introduce a new technique to obtain initial localization estimates based on trajectory observations

Distributed Camera Network Localization

Conference PaperLocalization, estimating the positions and orientations of a set of cameras, is a critical first step in camera-based sensor network applications such as geometric estimation, scene reconstruction, and motion tracking. We propose a new distributed localization algorithm for networks of cameras with sparse overlapping view structure that is energy efficient and copes well with networking dynamics. The distributed nature of the localization computations can result in order-of magnitude savings in communication energy over centralized approaches

Random channel coding and blind deconvolution

Abstract — Blind deconvolution arises naturally when dealing with finite multipath interference on a signal. In this paper we present a new method to protect the signals from the effects of sparse multipath channels—we modulate/encode the signal using random waveforms before transmission and estimate the channel and signal from the observations, without any prior knowledge of the channel other than that it is sparse. The problem can be articulated as follows. The original message x is encoded with an overdetermined m × n (m> n) matrix A whose entries are randomly chosen; the encoded message is given by Ax. The received signal is the convolution of the encoded message with h, the s-sparse impulse response of the channel. We explore three different schemes to recover the message x and the channel h simultaneously. The first scheme recasts the problem as a block ℓ1 optimization program. The second scheme imposes a rank-1 structure on the estimated signal. The third scheme uses nuclear norm as a proxy for rank, to recover the x and h. The simulation results are presented to demonstrate the efficiency of the random coding and proposed recovery schemes. I