Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.

Recurrent Neural Networks for Prediction

This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels. These channels model the effects of inter-symbol interference (ISI), co-channel interference (CCI), and noise. A low-order autoregressive model approximates the MIMO channel variation and facilitates tracking via a Kalman filter. Hard decisions to aid Kalman tracking come from a MIMO finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs the equalization task. Since the optimum DFE for a wide range of channels produces decisions with a delay /spl Delta/ > 0, the Kalman filter tracks the channel with a delay. A channel prediction module bridges the time gap between the channel estimates produced by the Kalman filter and those needed for the DFE adaptation. The proposed algorithm offers good tracking behavior for multiuser fading ISI channels at the expense of higher complexity than conventional adaptive algorithms. Applications include synchronous multiuser detection of independent transmitters, as well as coordinated transmission through many transmitter/receiver antennas, for increased data rate.

Multi-input multi-output fading channel tracking and equalization using Kalman estimation

Systems and methods according to the present invention describe 3D pointing devices which enhance usability by transforming sensed motion data from a first frame of reference (e.g., the body of the 3D pointing device) into a second frame of reference (e.g., a user's frame of reference). One exemplary embodiment of the present invention removes effects associated with a tilt orientation in which the 3D pointing device is held by a user.

3D pointing devices with orientation compensation and improved usability

A home entertainment system for video games and other applications includes a main unit and handheld controllers. The handheld controllers sense their own motion by detecting illumination emitted by emitters positioned at either side of a display. The controllers can be plugged into expansion units that customize the overall control interface for particular applications including but not limited to legacy video games.

Video game system with wireless modular handheld controller

A rectangular-windowed least-squares estimator using a polynomial model of the time-varying channel taps is proposed for estimating the impulse response of a frequency-selective fading channel. This method provides a significant improvement in mean square identification error (MSIE) over the conventional least mean squares (LMS) and the exponentially weighted recursive least squares (EW-RLS) algorithms without a polynomial model. A detailed study of the effects of channel parameters, such as the fading rate and the signal-to-noise ratio, on the proposed method is carried out. The performance of the method depends on the window size of the least squares estimator and the polynomial order being used. Algorithms to obtain the approximately optimal window size and polynomial order are proposed and are shown to perform well.

Frequency-selective fading channel estimation with a polynomial time-varying channel model

We exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applications, experiments are presented that demonstrate the tracking superiority of the extended RLS algorithms compared with the standard RLS and least-mean-squares (LMS) algorithms.

Adaptive tracking of linear time-variant systems by extended RLS algorithms

In this paper we exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms are considered, one pertaining to a system identification problem and the other to the tracking of a chirped sinusoid in additive noise. For both applications, experiments are presented that demonstrate the tracking optimality of the extended RLS algorithms, compared with the standard RLS and least-mean-squares (LMS) algorithms.

P.C. Wei

Papers

Adaptive tracking of linear time-variant systems by extended RLS algorithms

Tracking of linear time-variant systems