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...

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...

One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter technique. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluat...

As an optimal estimation method, the Kalman filter is the most frequently-used data fusion strategy in the field of dynamic navigation and positioning. Nevertheless, the abnormal model errors seriously degrade performance of the conventional Kalman filter. The adaptive Kalman filter was put forward to control the influences of model errors. However, the adaptive Kalman filter based on the predi...

Filtering noise in image sequences is an important preprocessing task in many image processing applications, including but not limited to real-time x-ray image sequences obtained in angiography. The main objective in real-time noise filtering is to improve the quality of the resultant image sequences. Practically affordable approaches are generally suboptimal and deal with the spatial and tempo...

Adaptive quantizers/dequantizers are systems that are used to quantize efficiently signals with a large variation in short-term variance. They are typically found in telecommunication systems where highly non-stationary signals such as speech need to be represented digitally with the minimum number of bits. Channel errors that are introduced owing to non-ideal transmission significantly reduce ...

An adaptive tracking filter for maneuvering targets is developed. The approach is based on matched filtering of the innovations sequence. Analytical solutions based on the invariant subspace method for time-invariant kinematic models along with the corresponding steady-state Kalman filter kernels are derived. Asymptotic and finite-memory operating characteristics are deduced. The proposed adapt...

In this paper, Bayesian nonlinear filtering is considered from the viewpoint of information geometry and a novel filtering method is proposed based on information geometric optimization. Under the Bayesian filtering framework, we derive a relationship between the nonlinear characteristics of filtering and the metric tensor of the corresponding statistical manifold. Bayesian joint distributions ...

Adaptive filters are primary methods to remove the power line interference from the ECG signal. The frequency range of ECG signal is generally 0.05 Hz to 100 Hz, and that of the power line interference is 50 Hz which lies in the ECG signal band. So, it has become very crucial to remove the power line interference from the ECG signal. In this paper Kalman based least mean square (KLMS) filter ha...