This paper presents the probability hypothesis density (PHD) filter for sets of trajectories. The resulting filter, which is referred to as trajectory probability density filter (TPHD), is capable of estimating trajectories in a principled way without requiring to evaluate all measurement-to-target association hypotheses. As the PHD filter, the TPHD filter is based on recursively obtaining the ...

This paper compares the standard Bilateral Filter and its variants such as Modified Bilateral Filter(MBF), Joint Bilateral Filter(JBF), Fuzzy Bilateral Filter(FBF) and Switching Bilateral Filter(SBF). For comparison purpose various performance matrices such as: Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), Mean Absolute Error(MAE), Normalized Color Difference(NCD), Perceptual Qualit...

This paper presents convergence results for the Box Gaussian Mixture Filter (BGMF). BGMF is a Gaussian Mixture Filter (GMF) that is based on a bank of Extended Kalman Filters. The critical part of GMF is the approximation of probability density function (pdf) as pdf of Gaussian mixture such that its components have small enough covariance matrices. Because GMF approximates prior and posterior a...

In conventional radar signal and image processing, the background clutter and noise are assumed to follow the Gaussian model. Under this assumption, it has been shown that the conventional matched filter is optimal in target detection (Reference 1). However, recent research has found that many nonhomogeneous types of clutter and noise, such as sea clutter, do not fit the Gaussian model well bec...

The decorrelating detector is known to eliminate multiaccess interference when the signature sequences of the users are linearly independent, at the cost of enhancing the Gaussian receiver noise. In this paper, we present a blind adaptive decorrelating detector which is based on the observation of readily available statistics. The algorithm recursively updates the filter coefficients of a desir...

Numerical weather prediction is characterized by high-dimensional, nonlinear systems and poses difficult challenges for real-time data assimilation (updating) and forecasting. The goal of this work is to build on the ensemble Kalman filter (EnsKF) to produce ensemble filtering techniques applicable to non-Gaussian densities in high dimensions. Two filtering algorithms are presented which extend...

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weigh...

Image denoising is an important first step to provide cleaned images for follow-up tasks such as image segmentation and object recognition. Many image denoising filters have been proposed, with most of the filters focusing on one particular type of additive or multiplicative noise. In this article, we propose a novel neighborhood regression approach. Using the neighboring pixels as predictors, ...

The polyspectrum modeling problem using linear or quadratic filters is investigated. In the linear case, we show that, if the output pth-order cumulant function of a filter, driven by a white noise, is of finite extent, then the filter necessarily has a finite extent impulse response. Next, we establish expressions of the output polyspectrum of a quadratic filter, driven by a Gaussian white noi...

A discrete-time signal estimator for systems subject to both white noise and bounded-power disturbance signals is developed. Sufficient and necessary conditions for the robust optimal filter are proved and the resulting filter gain is characterized by a set of two cross-coupled Riccati equations.