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—A mean-square error lower bound for the discrete-time nonlinear filtering problem is derived based on the Van Trees (posterior) version of the Cramér–Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly non-Gaussian, dynamical systems and is more general than the previous bounds in the literature. The case of singular(More)
FastICA is one of the most popular algorithms for independent component analysis (ICA), demixing a set of statistically independent sources that have been mixed linearly. A key question is how accurate the method is for finite data samples. We propose an improved version of the FastICA algorithm which is asymptotically efficient, i.e., its accuracy given by(More)
The problem of estimating the angles of arrival of M plane waves incident simultaneously on a line array with L > M sensors utilizing the special eigenstructure of the covariance matrix R of the signal plus noise at the output of the array is considered. The asymptotical analysis of the two most popular— MUSIC and Minimum-Norm— methods following the paper(More)
material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. Abstract— We propose a new low complexity Approximate Joint Diagonalization (AJD) algorithm, which incorporates non-trivial block-diagonal weight matrices into a Weighted Least-Squares (WLS) AJD criterion. Often in Blind Source Separation (BSS),(More)
2863 [2] D. W. Tufts and R. Kumaresan, " Estimation of multiple sinusoids: making linear prediction perform like maximum likelihood, " Proc. A nonlinear filter for estimating a sinusoidal signal and its parameters in white noise: On the case of a single sinusoid, " IEEE Abstract—We present an adaptive cross-product algorithm for tracking the direction to a(More)
—Time-domain algorithms for blind separation of audio sources can be classified as being based either on a partial or complete decomposition of an observation space. The decomposition, especially the complete one, is mostly done under a constraint to reduce the computational burden. However, this constraint potentially restricts the performance. The authors(More)
Alternating optimization algorithms for canonical polyadic decomposition (with/without nonnegative constraints) often accompany update rules with low computational cost, but could face problems of swamps, bottlenecks, and slow convergence. All-at-once algorithms can deal with such problems , but always demand significant temporary extra-storage, and high(More)
—CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemomet-rics, telecommunication, data mining, neuroscience, separated representations. For an order-N tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao(More)