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Abstract - SSAP9

SSAP9.1	Instantaneous Parameter Estimation Based on Continuous Wavelet Transform and Some Improvements H. Zhang, J. Zhao, J. Huang (Northwestern Polytechnical University, P R China) In this paper,. an novel method based on the phase information of continuous wavelet transform to estimate the instantaneous parameter of AM-FM signal is introduced, and some strategies, including the determination of initial value in iteration and post-processing to the estimated results, to improve the performance of the algorithm are proposed. Compared to several other instantaneous parameter estimators, such as CDF, Teager-Kaiser energy operator, and some TFR-based estimators, the proposed method has the advantages of noise resistance and accuracy by exploiting the time-scale localization of the wavelet transform. Simulation results testify that the proposed strategies improve the performance of the CWT based iteration algorithm greatly, and the method has excellent performance including robustness and accuracy in noisy condition.
SSAP9.2	Stabilization of Stationary and Time-Varying Autoregressive Models M. Juntunen, J. Tervo, J. Kaipio (University of Kuopio, Finland) A method for the stabilization of stationary and time-varying autoregressive model is presented. The method is based on the hyperstability constrained LS-problem with nonlinear constraints. The problems are solved with iteratively with Gauss-Newton type algorithm that sequentially linearizes the constraints. The proposed method is applied to simulated data in stationary case and to real EEG data in the time-varying case.
SSAP9.3	An Algorithm for Tracking a Random Walk with Unknown Drift J. Le Calvez (IRISA/Univ. de Rennes, France); B. Delyon (IRISA/INRIA, France); A. Juditsky (INRIA Rhone-Alpes, France) In this paper we study the problem of tracking a random walk observed with noise when the variance of the walk increment is unknown. We describe a sequence of estimators of the random walk and we design an algorithm to choose the best estimator among all the sequence. We give also a bound for the mean square error of this estimator. Finally, some simulations are presented and we compare our algorithm with the Kalman filter when the variance of the walk increment is estimated.
SSAP9.4	Identification of Arbitrarily Time-Variant Systems Y. Steinsaltz (EMC Corporation, USA); H. Lev-Ari (Northeastern University, USA) We introduce a technique for identification of systems with arbitrarily time-variant responses from samples of their input and output signals, and without using any prior information about the dynamics of the unknown system response. Our technique is based on the use of optimized averaging filters for the estimation of time-variant second order moments. We demonstrate the utility of our approach and the quality of the resulting estimates via a numerical example.
SSAP9.5	Adaptive RLS Filtering under the Cyclostationary Regime H. Lev-Ari (Northeastern University, USA) We present a methodology for adaptive filtering and system identification under the cyclostationary regime. Our technique is based on a deterministic periodic least-squares criterion, and gives rise to adaptive periodic recursive-least-squares (P-RLS) algorithms. Furthermore, we show that every adaptive RLS algorithm has a P-RLS counterpart, which has exactly the same performance attributes, and differs only in the length of the delay used in its time-update recursion.
SSAP9.6	Extending the Transfer Function Calculus of Time-Varying Linear Systems: A Generalized Underspread Theory G. Matz, F. Hlawatsch (Vienna University of Technology, Austria) We extend the approximate transfer function calculus of ``underspread'' linear time-varying (LTV) systems introduced by W. Kozek. Our extension is based on a new, generalized definition of underspread LTV systems that does not assume finite support of the systems' spreading function. We establish explicit bounds on various error quantities associated with the transfer function approximation. Our results yield a simple and convenient transfer function calculus for a significantly wider and practically more relevant class of LTV systems than that previously considered.
SSAP9.7	Dynamic Estimation with Selectable Linear Measurements D. Sinno, D. Cochran (Arizona State University, USA) This paper deals with a class of dynamic estimation problems in which the estimator may dynamically select, from among a temporally evolving set of possibilities, the source of the data on which the estimate will be based. After motivating and formulating this class of "attentive estimation" problems, the paper focuses on the case in which the state of a linear discrete-time dynamical system driven by gaussian noise is to be estimated using linear measurements corrupted by additive gaussian noise. This differs from the standard Kalman filtering problem in that the measurement map at each stage is selectable from a pre-determined set of such maps. When the system dynamics and noise statistics are known, a criterion for measurement selection yielding an optimal sequence of output functions can be defined prior to the onset of estimation. When the noise statistics or other parameters are unknown, closed-loop adaptive strategies for measurement selection are necessary.
SSAP9.8	Interpolation of Nonstationary Fields with Stationary Increments B. Pesquet-Popescu, P. Larzabal (LESIR, ENS Cachan, France) The problem of the linear interpolation of nonstationary multidimensional processes with stationary increments is studied. The expressions of the interpolation filters and of the estimation error are derived, which generalize the results of the interpolation theory for stationary processes. Both finite and infinite extent interpolation are considered. An application to the interpolation of an underwater depth map is presented.
SSAP9.9	Multiweight Optimization in OBE Algorithms for Improved Tracking and Adaptive Identification D. Joachim, J. Deller, M. Nayeri (Michigan State University, USA) Optimal Bounding Ellipsoid (OBE) algorithms offer an attractive alternative to traditional least squares methods for identifying linear-in-parameters signal and system models due to their low computational efficiency, superior tracking ability, and selective updating that permits processor sharing among tasks. These benefits are further enhanced by multiweight optimization (MWO) which yields improved per-point parameter convergence. This paper introduces the MWO process and describes advances in its implementation including the incorporation of a forgetting factor for improved tracking, a new method for efficient weight computation, and extensions to volume-minimizing OBE algorithms. Simulation studies illustrate the results.
SSAP9.10	An H-Infinity Approach to Multi-Source Tracking T. Ratnarajah (McMaster University, Canada) In this paper, a novel $H^\infty$ approach is proposed for tracking of polarized co-channel sources using an array of tripole antennas. The proposed approach partitions the observation data matrix into two sub-matrices that are used, in conjunction with a new state-space model, to provide an $H^\infty$-type recursive estimation of a {\em linear combiner}. The linear combiner then provide estimates of the noise and signal subspaces, from which the directions of the incident signals can be estimated and tracked. The proposed technique is also capable of handling the tracking of {\em appearing / disappearing} sources during the observation interval and, furthermore, can accommodate array modeling uncertainties. The difficult problem of tracking the crossing sources can be successfully handled by using diversely polarized array.

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