bstract. We present a modification of the new edge-directed inerpolation method that eliminates the prediction error accumulation roblem by adopting a modified training window structure, and exending the covariance matching into multiple directions to suppress he covariance mismatch problem. Simulation results show that the roposed method achieves remarkable subjective performance in reserving ...

MIMO radar beamforming algorithms usually consist of a signal covariance matrix synthesis stage, followed by signal synthesis to fit the obtained covariance matrix. In this paper, we propose a radar beamforming algorithm (called BeamShape) that performs a single-stage radar transmit signal design; i.e. no prior covariance matrix synthesis is required. Beam-Shape’s theoretical as well as computa...

An offline approach is proposed for the estimation of model and data error covariance matrices whereby covariance matrices of model data residuals are ‘‘matched’’ to their theoretical expectations using familiar leastsquares methods. This covariance matching approach is both a powerful diagnostic tool for addressing theoretical questions and an efficient estimator for real data assimilation stu...

ABSTRACT Observational astrophysics consists of making inferences about the Universe by comparing data and models. The credible intervals placed on model parameters are often as important maximum a posteriori probability values, indicate concordance or discordance between models with measurements from other data. Intermediate statistics (e.g. power spectrum) usually measured made fitting to the...

The validation of matching hypotheses using Mahalanobis distance is extensively utilized in robotic applications, and in general data-association techniques. The Ma-halanobis distance, deened by t h e i n n o vation and its covariance, is compared with a threshold deened by the chi-square distribution to validate a matching hypothesiss the validation test is a time-consuming operation. This pap...

Scan-SLAM is a simultaneous localisation and mapping algorithm that combines scan-matching methods with recursive estimation of landmark locations (using an EKF or other Bayesian filter). The scan-matching capability allows landmarks with arbitrary shapes to be modelled directly by sensed data and tracked within a conventional filter framework. This paper presents the essential Scan-SLAM algori...

We consider the problem of nonlinear filtering under the circumstance of unknown covariance statistic of the measurement noise. A novel adaptive unscented Kalman filter (UKF) integrating variational Bayesian methods and fuzzy logic techniques is proposed in this paper. It is called fuzzy adaptive variational Bayesian UKF (FAVBUKF). Firstly, the sufficient statistics of the measurement noise var...

This paper introduces a new algorithm to estimate a robot’s planar displacement by weighted matching of dense twodimensional range scans. Based on models of expected sensor uncertainty, our algorithm weights the contribution of each scan point to the overall matching error according to its uncertainty. A general maximum likelihood formulation is used to optimally estimate the displacement betwe...

Object matching is the process of determining the presence and the location of a reference object inside a scene image. Matching accuracy requires robust image description and efficient similarity measures. In this paper, we present a tree based object matching approach using a descriptor proposed in a previous work [1]. Visual objects are described by a collection of multi-scale covariance mat...