Kalman filter technniques are widely used in the areas of attitude and orbit determination, prediction and calibration. These techniques work well if the system dynamics are well-defined. Problems arise, however, when the system parameters are unknown ahead of time or changing over time. This paper discusses an adaptive Kalman filter design that utilizes recursive maximum likelihood parameter i...

It is known that the Kalman filter estimation performance heavily depends on the statistical parameters to both the dynamic and observation models, especially the covariance matrix Q. This paper presents a fast genetic algorithm (GA) to adapt extended Kalman filter (EKF) for real time tracking. In the proposed method, the covariance matrix Q can be real-time adjusted by GA to meet some specific...

One of the most important problems when designing controller is how to deal with all kinds of uncertainties, which, along with the high nonlinearities of most real systems, makes it difficult to guarantee the desired closed loop performance. Recently, nonlinear Kalman-class filter has been extensively researched and several well-known algorithms, including Extended Kalman Filter (EKF), Unscente...

-Remote Sensing technologies provide the spatial data/maps and offer great advantages for a land consolidation project. But sometimes in some regions, optics and infrared remote sensing can not work well. SAR (Synthetic aperture radar), an active microwave remote sensing imaging radar, has the unique capabilities of obtaining abundant electromagnetic information from ground objects all day/all ...

Fault Detection and Isolation (FDI) using Linear Kalman Filter (LKF) is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF) for Fault Detection and Isolation (FDI) ...

This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the...

The goal of this paper is to perform a performance analysis for speech enhancement using classical adaptive filters with kalman filter. In many applications like speech recognition, hearing aids, forensic applications and telephone conversations etc... As the enhancement of speech signals is of very important. The performance of the speech recognition system is also reduces if the speech signal...

In this paper, an efficient scheme has been proposed to model, detect and classify the fault. The modeling of fault has been proposed with the fuzzy logic using membership function. Fault detection of the unprecedented changes in system reliability and find the failed component state by classifying the faults is proposed using kalman filter and hybrid neurofuzzy computing techniques respectivel...

This paper deals with the problem of Adaptive Noise Cancellation (ANC) for the speech signal corrupted with an additive white Gaussian noise. After explaining the least Mean Square (LMS)-based adaptive filter and Kalman filter, it examine the hybrid Kalman-based LMS (KNLMS) technique for adaptation of the ANC. The proposed technique suggests a way to normalize LMS algorithm using Kalman filter....

Synthetic aperture radar (SAR) remote sensing, with its advantages of all-weather coverage, all day/night acquisitions, cloud penetration, and signal independence of the solar illumination angle, can be applied to land cover classification and land consolidation, especially in some regions where optics and infrared remote sensing do not work well. A limitation of its using for land consolidatio...