In the past few decades, there were many imaging algorithms designed in the case of the absence of multiple scattering. Recently, we discussed an algorithm for removing high order scattering components from collected data. This paper is a continuation of our previous work. First, we investigate the current state of multiple scattering in SAR. Then, we revise our method and test it. Given an estimate of our target reflectivity, we compute the multi scattering effects in the target region for various frequencies. Furthermore, we propagate this energy through free space towards our antenna, and remove it from the collected data.
Many imaging algorithms have been designed assuming the absence of multiple scattering. In the 2013 SPIE proceeding, we discussed an algorithm for removing high order scattering components from collected data. In this paper, our goal is to continue this work. First, we survey the current state of multiple scattering in SAR. Then, we revise our method and test it. Given an estimate of our target reflectivity, we compute the multi scattering effects in our target region for various frequencies. Furthermore, we propagate this energy through free space towards our antenna, and remove it from the collected data.
A general synthetic aperture radar (SAR) signal model is derived based on the Maxwells equation, and three
numerical simulations are analyzed and discussed. With this signal model, compressive sensing is applied to get
a better image.
The Born approximation is a common approach taken in modeling the physics of SAR imaging. In essence it says
that radiation only scatters once when in space. This is a reasonable assumption for targets that lie far apart or
that are far from the transmit and receive antennas, but it introduces error into the imaging process. The goal
of this paper is to iteratively compensate for this error by using estimates of the target distribution to estimate
multiple scattering phenomena. We will use a noise reduction technique at each iteration on the corrected data as
well as the estimated image to control any excess error caused by the estimated multiple scattering phenomena.
The physical model for our work will be based on the wave equation. We will briefly derive the important features
of the model as well as account for the error brought by common approximations that are made. Typically one
does not get an image that is approximately the target distribution, but rather an image that is approximately
proportional to the target distribution. This means that there is a scaling parameter that must be chosen when
using target distribution estimates to correct data. We will discuss methods for choosing this parameter. We
will provide a few basic SAR imaging methods and perform simulation using the Gotcha Data set in combination
with the iterative technique. At the end of the paper we will outline future work involving this method.
Multiple Input Multiple Output- MIMO Radar is a fast growing research area. This paper will give a brief
introduction to the subject as well as derive an image formation scheme. The general problem of radar imaging
is to use some physical model for a transmitted signal, and measurements of the signal that is scattered back to
a receiver by a scene to attempt to derive information about the scene. The concept of communication involves
a message sender, a message receiver, and a channel. The sender sends a message through the channel to the
receiver. The receiver attempts to recover the original message. MIMO communication is just communication
that involves sending several messages to several recipients. The problem of Multiple Input Multiple Output
Radar Imaging is to use the corruption of transmitted messages to try and derive useful information about the
environment that the messages traveled through. The extra information gained with MIMO Radar can be used
to get rid of false targets, detect moving targets, and create a better resolution image. The plan for this research
is to culminate to an in-scene 3-d Image reconstruction algorithm. The model presented provides a context in
which to examine this problem.