PROJECT TITLE :

Scene size limits for polar format algorithm

ABSTRACT:

Synthetic aperture radar (SAR) is a type of remote sensing where coherent radar echoes transmitted from a moving platform are processed to create a picture of a scene, typically on the ground. There are several algorithms that have been developed with varying levels of complexity and accuracy. In applications with giant scene size requirements, the choice of image formation algorithm is important. Actual imaging algorithms like the back-projection algorithm (BPA) can kind giant images without errors, but they are computationally expensive. Another well-known algorithm is that the polar format algorithm (PFA), which is significantly faster than BPA, but it uses approximations that cause image errors in large scenes. In this paper, we have a tendency to evaluate the scene size limitations of the PFA in terms of image defocus. This is often caused by residual quadratic part errors that arise due to approximations within the algorithm. We derive this residual quadratic part error employing a Taylor series expansion in the slow time dimension. Then, we tend to derive simplified expressions for image defocus for two flight methods: circular and linear. We additionally use the Taylor series growth to derive correct corrections for image distortion caused by PFA. These distortion corrections are employed in conjunction with the residual quadratic phase errors to derive correct scene size limitations that are notably completely different from the circular regions of focus determined in earlier works.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition ABSTRACT: It has been demonstrated that using a convolutional neural network, also known as CNN, is an efficient method
PROJECT TITLE : Bioinspired Scene Classification by Deep Active Learning With Remote Sensing Applications ABSTRACT: Scene parsing, robot motion planning, and autonomous driving are all examples of applications that require
PROJECT TITLE : Locate, Size and Count Accurately Resolving People in Dense Crowds via Detection ABSTRACT: We present a detection method for dense crowd counting that replaces the widely used density regression paradigm. Rather
PROJECT TITLE : A Multiple-Instance Densely-Connected ConvNet for Aerial Scene Classification ABSTRACT: Aerial views, in contrast to natural scenes, generally consist of many items that are crowded on the surface from a bird's
PROJECT TITLE : Dynamic Scene Deblurring by Depth Guided Model ABSTRACT: Object movement, depth fluctuation, and camera shake are the most common causes of dynamic scene blur. For the most part, present approaches use picture

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry