This paper describes a method for accurately geo-locating moving targets using three-channel SAR-based GMTI
interferometry. The main goals in GMTI processing are moving target detection and geo-location. In a 2011 SPIE
paper we showed that reliable target detection is possible using two-channel interferometry, even in the presence of
main-beam clutter. Unfortunately, accurate geo-location is problematic when using two-channel interferometry,
since azimuth estimation is corrupted by interfering clutter. However, we show here that by performing three-channel
processing in an appropriate sequence, clutter effects can be diminished and significant improvement
can be obtained in geo-location accuracy. The method described here is similar to an existing technique known
as Clutter Suppression Interferometry (CSI), although there are new aspects of our implementation. The main
contribution of this paper is the mathematical discussion, which explains in a straightforward manner why
three-channel CSI outperforms standard two-channel interferometry when target signatures are embedded in
main-beam clutter. Also, to our knowledge this paper presents the first results of CSI applied to the Gotcha
Challange data set, collected using an X-band circular SAR system in an urban environment.
The use of multiple cooperative sensors for the detection of person borne IEDs is investigated. The purpose of the effort
is to evaluate the performance benefits of adding multiple sensor data streams into an aided threat detection algorithm,
and a quantitative analysis of which sensor data combinations improve overall detection performance. Testing includes
both mannequins and human subjects with simulated suicide bomb devices of various configurations, materials, sizes
and metal content. Aided threat recognition algorithms are being developed to test detection performance of individual
sensors against combined fused sensors inputs. Sensors investigated include active and passive millimeter wave imaging
systems, passive infrared, 3-D profiling sensors and acoustic imaging. The paper describes the experimental set-up and
outlines the methodology behind a decision fusion algorithm-based on the concept of a "body model".
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