Parallel implementation of the extended square-root covariancefilter for tracking applications

Parallel implementations of the extended square-root covariance filter (ESRCF) for tracking applications are developed. The decoupling technique and special properties used in the tracking Kalman filter (KF) are employed to reduce computational requirements and to increase parallelism. The application of the decoupling technique to the ESRCF results in the time and measurement updates of m decoupled (n/m)-dimensional matrices instead of one coupled n-dimensional matrix, where m denotes the tracking dimension and n denotes the number of state elements. The updates of m decoupled matrices are found to require approximately m fewer processing elements and clock cycles than the updates of one coupled matrix. The transformation of the Kalman gain which accounts for the decoupling is found to be straightforward to implement. The sparse nature of the measurement matrix and the sparse, band nature of the transition matrix are explored to simplify matrix multiplications     

Neural networks expand SP's horizons

Advanced algorithms for signal processing simultaneously account for nonlinearity, nonstationarity, and non-Gaussianity. The article examines the use of neural networks as an engineering tool for signal processing applications. The aim is three fold: to articulate a new philosophy in the approach to statistical signal processing using neural networks, which (either by themselves or in combination with other suitable techniques) account for the practical realities of nonlinearity, nonstationarity, and non-Gaussianity; to describe three case studies using real-life data, which clearly demonstrate the superiority of this new approach over the classical approaches to statistical signal processing; and to discuss mutual information as a criterion for designing unsupervised neural networks, thus moving away from the mean-square error criterion     

Turbo-BLAST for wireless communications: theory and experiments

Turbo-BLAST is a novel multitransmit multireceive (MTMR) antenna scheme for high-throughput wireless communications. It exploits the following ideas: the Bell Labs layered space time (BLAST) architecture; random layered space-time (RLST) coding scheme by using independent block codes and random space-time interleaving; sub-optimal turbo-like receiver that performs iterative decoding of the RLST codes and estimation of the channel matrix in an iterative and, most important, simple fashion. The net result is a new transceiver that is not only computationally efficient compared with the optimal maximum likelihood decoder, but it also yields a probability of error performance that is orders of magnitude smaller than traditional BLAST schemes for the same operating conditions. This paper also presents experimental results using real-life indoor channel measurements demonstrating the high-spectral efficiency of turbo-BLAST.     

Classification of radar clutter using neural networks

A classifier that incorporates both preprocessing and postprocessing procedures as well as a multilayer feedforward network (based on the back-propagation algorithm) in its design to distinguish between several major classes of radar returns including weather, birds, and aircraft is described. The classifier achieves an average classification accuracy of 89% on generalization for data collected during a single scan of the radar antenna. The procedures of feature selection for neural network training, the classifier design considerations, the learning algorithm development, the implementation, and the experimental results of the neural clutter classifier, which is simulated on a Warp systolic computer, are discussed. A comparative evaluation of the multilayer neural network with a traditional Bayes classifier is presented.     

Bayesian sequential state estimation for MIMO wireless communications

This paper explores the use of particle filters, rooted in Bayesian estimation, as a device for tracking statistical variations in the channel matrix of a narrowband multiple-input, multiple-output (MIMO) wireless channel. The motivation is to permit the receiver to acquire channel state information through a semiblind strategy and thereby improve the receiver performance of the wireless communication system. To that end, the paper compares the particle filter as well as an improved version of the particle filter using gradient information, to the conventional Kalman filter and mixture Kalman filter with two metrics in mind: receiver performance curves and computational complexity. The comparisons, also including differential phase modulation, are carried out using real-life recorded MIMO wireless data.     

Nonlinear adaptive prediction of nonstationary signals

We describe a computationally efficient scheme for the nonlinear adaptive prediction of nonstationary signals whose generation is governed by a nonlinear dynamical mechanism. The complete predictor consists of two subsections. One performs a nonlinear mapping from the input space to an intermediate space with the aim of linearizing the input signal, and the other performs a linear mapping from the new space to the output space. The nonlinear subsection consists of a pipelined recurrent neural network (PRNN), and the linear section consists of a conventional tapped-delay-line (TDL) filter. The nonlinear adaptive predictor described is of general application. The dynamic behavior of the predictor is demonstrated for the case of a speech signal; for this application, it is shown that the nonlinear adaptive predictor outperforms the traditional linear adaptive scheme in a significant way     

Making sense of a complex world [chaotic events modeling]

Addresses the identification of nonlinear systems from output time series, which we have called dynamic modeling. We start by providing the mathematical basis for dynamic modeling and show that it is equivalent to a multivariate nonlinear prediction problem in the reconstructed space. We address the importance of dynamic reconstruction for dynamic modeling. Recognizing that dynamic reconstruction is an ill-defined inverse problem, we describe a regularized radial basis function network for solving the dynamic reconstruction problem. Prior knowledge in the form of smoothness of the mapping is imposed on the solution via regularization. We also show that, in time-series analysis, some form of regularization can be accomplished by using the structure of the time series instead of imposing a smoothness constraint on the cost function. We develop a methodology based on iterated prediction to train the network weights with an error derived through trajectory learning. This method provides a robust performance because during learning the weights are constrained to follow a trajectory. The dynamic invariants estimated from the generated time series are similar to the ones estimated from the original time series, which means that the properties of the attractor have been captured by the neural network. We finally raise the question that generalized delay operators may have advantages in dynamic reconstruction, primarily in cases where the time series is corrupted by noise. We show how to set the recursive parameter of the gamma operator to attenuate noise and preserve the dynamics     

Efficient implementation of multiple-beam sampler for continuously scanned array antennas

A multiple-beam sampler is described for continuously scanned array antennas using time modulation or frequency modulation. It is shown that, for the special case of an array antenna with a number of elements equal to an integer power of two, the multiple-beam sampler can be implemented using a minimum number of phase shifters and adders.     

Response of a recursive m.t.i. filter to inputs with staggered p.r.f.

The authors present graphical results of computer simulations showing that proper choice of feedback coefficients in conjunction with a staggered p.r.f.input can achieve significant improvements in low-frequency cutoff and in smoothness of response within the passband, at the expense of increased circuit complexity.     

The cocktail party problem

This review presents an overview of a challenging problem in auditory perception, the cocktail party phenomenon, the delineation of which goes back to a classic paper by Cherry in 1953. In this review, we address the following issues: (1) human auditory scene analysis, which is a general process carried out by the auditory system of a human listener; (2) insight into auditory perception, which is derived from Marr's vision theory; (3) computational auditory scene analysis, which focuses on specific approaches aimed at solving the machine cocktail party problem; (4) active audition, the proposal for which is motivated by analogy with active vision, and (5) discussion of brain theory and independent component analysis, on the one hand, and correlative neural firing, on the other.