Recent developments in the realm of state estimation of stochastic dynamic systems in the presence of non-Gaussian noise have induced a new methodology called the maximum correntropy filtering. The filters designed under the maximum correntropy criterion (MCC) utilize a similarity measure (or correntropy) between two random variables as a cost function. They are shown to improve the estimators’...

Most ground target tracking problems involve nonlinear filtering due to nonlinearity in the measurement model. At present, a quantitative measure of nonlinearity and its relationship with the performance of the filtering algorithms are lacking. We quantify the degree of nonlinearity of a filtering problem using the differential geometry based measures of nonlinearity such as the parameter-effec...

This paper discusses algorithms for filtering and recovering underlying trends from noisy data. The key assumption in this paper is that the trends are monotonic, e.g., describe system deterioration that accumulates irreversibly. A maximum a posteriori probability (MAP) estimate of the trend can be obtained using an empirical signal model (MAP prior). The overall problem statement is related to...

In different applications (i.e., information retrieval, filtering or analysis), it is useful to detect similar terms and to provide the possibility to use them jointly. Clustering of terms is one of the methods which can be exploited for this. In our study, we propose to test three methods dedicated to the clustering of terms (hierarchical ascendant classification, Radius and maximum), to combi...

When classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear statespace models, a key challenge is that estimates of the likelihood function and its derivatives are inherently noisy. The key idea in this paper is to run a particle filter based on a current parameter estimate, but then use the output from this particle filter to re-evaluate the l...

We present a Matrix Factorization (MF) based approach for the Netflix Prize competition. Currently MF based algorithms are popular and have proved successful for collaborative filtering tasks. For the Netflix Prize competition, we adopt three different types of MF algorithms: regularized MF, maximum margin MF and non-negative MF. Furthermore, for each MF algorithm, instead of selecting the opti...

In this paper, we perform multi-sensor multi-resolution data fusion of Landsat-5 TM bands (at 30 m spatial resolution) and multispectral bands of World View-2 (WV-2 at 2 m spatial resolution) through linear spectral unmixing model. The advantages of fusing Landsat and WV-2 data are two fold: first, spatial resolution of the Landsat bands increases to WV-2 resolution. Second, integration of data...

Abstarct In this paper, combination of channel, receiver frequency-dependent IQ imbalance and carrier frequency offset estimation under short cyclic prefix (CP) length are considered in OFDM system. An adaptive algorithm based on the set-membership filtering (SMF) algorithm is used to compensate for these impairments. In short CP length, per-tone equalization (PTEQ) structure is used to avoid i...

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzz...

This paper presents algorithms for parallelization of inference in hidden Markov models (HMMs). In particular, we propose parallel backward-forward type filtering and smoothing algorithm as well Viterbi-type maximum-a-posteriori (MAP) algorithm. We define associative elements operators to pose these problems parallel-prefix-sum computations sum-product max-product parallelize them using paralle...