Abstract from Univ. of Michigan DARPA-ARO Proposal
# ABSTRACT FROM OUR PROPOSAL

## Sequential Adaptive Multi-Modality Target Detection

## and Classification Using Physics-Based Models

The goal is to develop adaptive algorithms for detection and classification
of the following: (1) vehicles under forest canopies; and (2) land mines.
The data will be obtained from a variety of
sensing modalities, including radar, optical and thermal emissions.
The approach will be to start with detailed physics-based radar propagation
and scattering models for canopies, vehicles, and land mines already developed
here in the Radiation Laboratory of the University of Michigan. These coherent
models are capable of preserving
detailed structure of tree canopies (species, density, vegetation moisture
content, etc.) and also account for near- and far-field interactions among
vegetation particles, as well as their interactions with embedded targets.

We will derive simpler models for these physics-based models which capture
the relevant physics of each modality while preserving features essential
to adaptive target detection algorithms. Hierarchical representations
of the forward model will be the first avenue taken to accomplish model
reduction. For this we will start by applying a recently developed theory of
multi-dimensional anisotropic wavelets whose footprints can be matched to
spatio-temporal resolution ellipsoids specified by the modality. Other
low-dimensional models, obtained using functional approximation theory,
will also be investigated.

The resulting lower-dimensional model will be used in a sequential adaptive
detection procedure in which information from one sensor modality is used as
a first stage to adaptively alter source waveforms and arrays for another
modality. How to select and sequence modalities is a research question to be
addressed. Modalities and sensors to be considered include: imaging radar at
various frequencies, polarizations, and resolutions; thermal imagers at
various frequency bands; and lidar.

The expected result of this research is a new approach to solving the
above inverse problems by combining different sensor modalities. Specific
algorithms for each of the above problems will be developed, along with
a set of performance measurements obtained from both Monte Carlo
simulations on existing detailed forward problem models, and from real data.