Resolution-enhanced radar/SAR imaging: an experiment design framework combined with neural network-adapted variational analysis regularization

Stewart Santos,Yuriy Shkvarko,Jose Tuxpan

doi:10.1186/1687-6180-2011-85

Abstract

The convex optimization-based descriptive experiment design regularization (DEDR) method is aggregated with the neural network (NN)-adapted variational analysis (VA) approach for adaptive high-resolution sensing into a unified DEDR -VA-NN framework that puts in a single optimization frame high-resolution radar/SAR image formation in uncertain operational scenarios, adaptive despeckling and dynamic scene image enhancement for a variety of sensing modes. The DEDR -VA-NN method outperforms the existing adaptive radar imaging techniques both in resolution and convergence rate. The simulation examples are incorporated to illustrate the efficiency of the proposed DEDR-VA-related imaging techniques.

Highlights

In this article, we consider the problem of enhanced remote sensing (RS) imaging stated and treated as an ill-posed nonlinear inverse problem with model uncertainties
We extend the developments of the descriptive experiment design regularization (DEDR) and variational analysis (VA) techniques originated in [2,7,9] by performing the aggregation of the DEDR and VA paradigms and putting the RS image enhancement/ reconstruction tasks into the unified neural network (NN)-adapted computational frame addressed as a unified DEDR-VA-NN method
Concluding remarks The extended DEDR method combined with the dynamic VA regularization has been adapted to the NN computational framework for perceptually enhanced and considerably speeded up reconstruction of the RS imagery acquired with imaging array radar and/or fractional SAR imaging systems operating in an uncertain RS

Summary

Introduction

We consider the problem of enhanced remote sensing (RS) imaging stated and treated as an ill-posed nonlinear inverse problem with model uncertainties. The problem of highresolution RS imaging is to develop a framework and related method(s) that perform optimal estimation of the SSP (referred to as a scene image) from the available radar/SAR data measurements.

Results

Conclusion