Jitendra K. Tugnait

Bio: Jitendra K. Tugnait is an academic researcher from Auburn University. The author has contributed to research in topics: Estimation theory & Communication channel. The author has an hindex of 45, co-authored 377 publications receiving 7070 citations. Previous affiliations of Jitendra K. Tugnait include ExxonMobil & University of Alabama.

Identification and deconvolution of multichannel linear non-Gaussian processes using higher order statistics and inverse filter criteria

Abstract: This paper is concerned with the problem of estimation and deconvolution of the matrix impulse response function of a multiple-input multiple-output (MIMO) system given only the measurements of the vector output of the system. The system is assumed to be driven by a temporally i.i.d. and spatially independent non-Gaussian vector sequence (which is not observed). An iterative, inverse filter criteria-based approach is developed using the third-order or the fourth-order normalized cumulants of the inverse filtered data at zero lag. Stationary points of the proposed cost functions are investigated. The approach is input iterative, i.e., the input sequences are extracted and removed one by one. The matrix impulse response is then obtained by cross correlating the extracted inputs with the observed outputs. Identifiability conditions are analyzed. The strong consistency of the proposed approach is also briefly discussed. Computer simulation examples are presented to illustrate the proposed approaches.

Identification of linear stochastic systems via second- and fourth-order cumulant matching

Abstract: The identification problem for time-invariant single-input single-output linear stochastic systems driven by non-Gaussian white noise is considered The system is not restricted to be minimum phase, and it is allowed to contain all-pass components A least-squares criterion that involves matching the second- and the fourth-order cumulant functions of the noisy observations is proposed Knowledge of the probability distribution of the driving noise is not required An order determination criterion that is a modification of the Akaike information criterion is also proposed Strong consistency of the proposed estimator is proved under certain sufficient conditions Simulation results are presented to illustrate the method

On channel estimation using superimposed training and first-order statistics

Abstract: Channel estimation for single-input multiple-output (SIMO) time-invariant channels is considered using only the first-order statistics of the data. A periodic (nonrandom) training sequence is added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. Recently superimposed training has been used for channel estimation assuming no mean-value uncertainty at the receiver and using periodically inserted pilot symbols. We propose a different method that allows more general training sequences and explicitly exploits the underlying cyclostationary nature of the periodic training sequences. We also allow mean-value uncertainty at the receiver. Illustrative computer simulation examples are presented.

Single-user channel estimation and equalization

Abstract: There has been much interest in blind (self-recovering) channel estimation and blind equalization where no training sequences are available or used. In multipoint networks, whenever a link from the server to one of the tributary stations is interrupted, it is clearly not feasible (or desirable) for the server to start sending a training sequence to re-establish a particular link. In digital communications over fading/multipath channels, a restart is required following a temporary path interruption due to severe fading. During on-line transmission impairment monitoring, the training sequences are obviously not supplied by the transmitter. Consequently, the importance of blind channel compensation research is also strongly supported by practical needs. We present a comprehensive summary of research development on single-user channel estimation and equalization, focusing on both training-based and blind approaches. Our emphasis is on linear time-invariant channels.

Unscented filtering and nonlinear estimation

Abstract: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.

On sequential Monte Carlo sampling methods for Bayesian filtering

Abstract: In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literatures these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.

Detection of abrupt changes: theory and application

Abstract: This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.