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—A Probabilistic Data Association (PDA) method is proposed in this letter for multiuser detection over synchronous code-division multiple-access (CDMA) communication channels. PDA models the undecided user signals as binary random variables. By approximating the inter-user interference (IUI) as Gaussian noise with an appropriately elevated covariance(More)
—In this paper, we present various channel estimators that exploit the channel sparsity in a multicarrier underwater acoustic system, including subspace algorithms from the array precessing literature, namely root-MUSIC and ESPRIT, and recent compressed sensing algorithms in form of Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP). Numerical(More)
—In decentralized detection, local sensor observations have to be communicated to a fusion center through the wireless medium, inherently a multiple-access channel (MAC). As communication is bandwidth-and energy-constrained, it has been suggested to use the properties of the MAC to combine the sensor observations directly on the channel. Although this leads(More)
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—Two new algorithms are presented for the segmenta-tion of a white Gaussian-distributed time series having unknown but piecewise-constant variances. The first " sequential/minimum description length (MDL) " idea includes a rough parsing via the GLR, a penalization of segmentations having too many parts via MDL, and an optional refinement stage. The second "(More)
— In this paper, we propose to use nonbinary low density parity check (LDPC) codes to address two main issues in underwater acoustic OFDM communication: i) plain OFDM has poor performance in the presence of channel fading, and ii) OFDM transmission has a high peak-to-average-power ratio (PAPR). We propose new methods to construct nonbinary irregular LDPC(More)
—An algorithm that is capable not only of tracking multiple targets but also of " track management " – meaning that it does not need to know the number of targets as a user input – is of considerable interest. In this paper we devise a recursive track-managed filter via a quantized state-space (" bin ") model. In the limit, as the discretization implied by(More)
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three(More)