A Super Resolution SAR Imaging Algorithm Based on Adaptive Kalman Filter for Land Consolidation
نویسندگان
چکیده
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 consolidation is the available spatial resolution. To increase the resolution, we propose a super-resolution imaging algorithm based on adaptive Kalman Filter procedure. The method strongly improves the resolution by using prior knowledge, which is a scientific breakthrough in the case that the traditional pulse compression constrains the improvement of SAR spatial resolution. It is an optimal method in the sense of mean square error and its computation cost is lower than the traditional Kalman Filter algorithm. Simulation results demonstrate the effectiveness of the proposed method. Key-Words: Synthetic Aperture Radar, Adaptive Kalman Filter, high resolution, Land Consolidation, SNR, Mean Square Error
منابع مشابه
Adaptive Kalman Procedure for SAR High Resolution Image Reconstruction in the Planning Phase of Land Consolidation
-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 ...
متن کاملTuning of Extended Kalman Filter using Self-adaptive Differential Evolution Algorithm for Sensorless Permanent Magnet Synchronous Motor Drive
In this paper, a novel method based on a combination of Extended Kalman Filter (EKF) with Self-adaptive Differential Evolution (SaDE) algorithm to estimate rotor position, speed and machine states for a Permanent Magnet Synchronous Motor (PMSM) is proposed. In the proposed method, as a first step SaDE algorithm is used to tune the noise covariance matrices of state noise and measurement noise i...
متن کاملDoppler and bearing tracking using fuzzy adaptive unscented Kalman filter
The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the...
متن کاملA New Adaptive Extended Kalman Filter for a Class of Nonlinear Systems
This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...
متن کاملSparse Reconstruction for Sar Imaging Based on Compressed Sensing
Abstract—Synthetic Aperture Radar (SAR) can obtain a twodimensional image of the observed scene. However, the resolution of conventional SAR imaging algorithm based on Matched Filter (MF) theory is limited by the transmitted signal bandwidth and the antenna length. Compressed sensing (CS) is a new approach of sparse signals recovered beyond the Nyquist sampling constraints. In this paper, a hig...
متن کامل