Estimation and identification for 2-D block Kalman filtering

Includes bibliographical references.This correspondence is concerned with the development of a recursive identification and estimation procedure for 2-D block Kalman filtering. The recursive identification scheme can be used on-line to update the image model parameters at each iteration based upon the local statistics within a block of the observed noisy image. The covariance matrix of the driving noise can also be estimated at each iteration of this algorithm. A recursive procedure is given for computing the parameters of the higher order models. Simulation results are also provided

Two-dimensional adaptive block Kalman filtering of SAR imagery

Includes bibliographical references.Speckle effects are commonly observed in synthetic aperture radar (SAR) imagery. In airborne SAR systems the effect of this degradation reduces the accuracy of detection substantially. Thus, the elimination of this noise is an important task in SAR imaging systems. In this paper a new method for speckle noise removal is introduced using 2-D adaptive block Kalman filtering (ABKF). The image process is represented by an autoregressive (AR) model with nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which take into account not only the effect of speckles as a multiplicative noise but also those of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as that of the additive noise

Model reduction method for a class of 2-D systems, A

Includes bibliographical references.A decomposition-aggregation scheme for reduction of dimensionality for a class of 2-D systems is introduced. This method, which is based upon the extension of the singular perturbation method in two dimensions, is used to decompose the original 2-D system into two reduced-order 2-D subsystems. These reduced order subsystems are shown to effectively capture the dynamical behavior of the original full-order system. Two numerical examples are provided that indicate the effectiveness of this method when used in image modeling applications.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada, and by Fonds Pour la Formation de Chercheurs et L'aide la Recherche, Programme E'tablissment de Nouveaux Chercheurs

Full-plane block Kalman filter for image restoration, A

Includes bibliographical references.A new two-dimensional (2-D) block Kalman filtering method is presented which uses a full-plane image model to generate a more accurate filtered estimate of an image that has been corrupted by additive noise and full-plane blur. Causality is maintained within the filtering process by employing multiple concurrent block estimators. In addition, true state dynamics are preserved, resulting in an accurate Kalman gain matrix. Simulation results on a test image corrupted by additive white Gaussian noise are presented for various image models and compared to those of the previous block Kalman filtering methods

Parameter estimation for two-dimensional vector models using neural networks

Includes bibliographical references.This correspondence addresses the problem of two-dimensional (2-D) vector image model parameter estimation using a new recursive least squares (RLS)-based learning method. Vector autoregressive (AR) models with various 1-D and 2-D, causal and noncausal regions of support (ROS) are considered. Numerical results are presented which demonstrate the usefulness of the proposed scheme for on-line implementation

Interleaved pipeline structures for two-dimensional recursive filtering

Includes bibliographical references (page 91).This paper presents new parallel and pipeline structures for real-time 2-D recursive filtering. For general scalar 2-D recursive filters, a 2-D multiple-interleaved pipeline architecture is introduced that is compatible with the nature of the image-scanning scheme. Using this new structure, the sampling period can be a fraction of the time needed for one scalar addition operation and the delay is only a few samples. In addition, this structure does not need any I / 0 buffers for real-time implementation

Comparison of two different PNN training approaches for satellite cloud data classification

Includes bibliographical references.This paper presents a training algorithm for probabilistic neural networks (PNNs) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.This work was supported by the DoD Center for Geosciences/Atmospheric Research (CG/AR) under Contract DAAL01-98-2-0078

Reduced order Kalman filtering for the enhancement of respiratory sounds

Includes bibliographical references (page 424).Correction included.In the processing and analysis of respiratory sounds, heart sounds present the main source of interference. This paper is concerned with the problem of cancellation of the heart sounds using a reduced order Kalman filter (ROKF). To facilitate the estimation of the respiratory sounds, an autoregressive (AR) model is fitted to heart signal information present in the segments of the acquired signal which are free of respiratory sounds. The state-space equations necessary for the ROKF are then established considering the respiratory sound as a colored additive process in the observation equation. This scheme does not require a time alignment procedure as with the adaptive filtering-based schemes. The scheme is applied to several synthesized signals with different signal-to-interference ratios (SIR) and the results are presented

Neural network directed Bayes decision rule for moving target classification

Includes bibliographical references.In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model (HMM) scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods.This work was funded by the Optoelectronic Computing Systems (OCS) Center at Colorado State University, under NSF/REC Grant 9485502

Reduced order strip Kalman filtering using singular perturbation method

Includes bibliographical references.Strip Kalman filtering for restoration of images degraded by linear shift invariant (LSI) blur and additive white Gaussian (WG) noise is considered. The image process is modeled by a 1-D vector autoregressive (AR) model in each strip. It is shown that the composite dynamic model that is obtained by combining the image model and the blur model takes the form of a singularly perturbed system owing to the strong-weak correlation effects within a window. The time scale property of the singularly perturbed system is then utilized to decompose the original system into reduced order subsystems which closely capture the behavior of the full order system. For these subsystems the relevant Kalman filtering equations are given which provide the suboptimal filtered estimates of the image and the one-step prediction estimates of the blur needed for the next stage. Simulation results are also provided