CO-PI | Year #1: Sept. 2002-Sept. 2003 | Year #2: Sept. 2003-Sept. 2004 | Year #3: Sept. 2004-Sept. 2005 | |
---|---|---|---|---|

OVERALLPROJECT GOALS | - Physics-based models for vehicles under foliage
- Physics-based models for GPR imaging of landmines
- Myopic statistical algorithms for sensor management
| - Statistical models for vehicles under foliage
- Physics-based models for EMI detection of landmines
- Non-myopic statistical sensor management algorithms
| - Initial multimodal algorithms for vehicles
- Statistical models for GPR and EMI for landmines
- Statistical algorithms for vehicle detection
| |

KAMALRadar for: Foliage; Trunks; Vehicles | ||||

HEROStats for: Sensor schedule; Detection algorithms | ||||

YAGLELand mines; Blind deconv. |

Performed phenomenological studies of physics-based clutter and target models.

Developed algorithm for modelling multiple scattering from pine needle clusters.

Developed time-reversal method for foliage-camouflaged target detection.

Developed non-parametric Markov-Random-Field (MRF) approach for statistically characterizing

the scattered fields from clutter and from a target in clutter.

Statistical models for entire scattered fields can be extrapolated from little data.

Developed

sensor management algorithms using Renyi information divergence measures.

when the numbers of targets, their locations, and available sensors are all large.

Using particle filtering and Renyi-divergence-based scheduling, complexity was reduced

and simultaneous tracking of dozens of actual target trajectories was demonstrated.

Developed a hypothesis test for whether a known local minimum is also a global minimum.

Can peformance after convergence be improved? This test provides a statistical answer.

Range-migration algorithm for landmine detection using ground-penetrating radar.

Michigan MURI is addressing. Since Sarabandi has extensive experience with foliage

and vehicle modelling, Yagle is covering mine detection (with less previous work).

2-D and 3-D blind deconvolution algorithms for even point-spread functions.

effects for mines, and perhaps foliage as well, depending on how well the frequency

decorrelation approach currently being developed by Sarabandi works out.

Developed a linear-algebra-based method for inverse scattering when the Green's function

and object are both modelled as a linear combination of known basis functions.

This effort was discontinued when Marcin Bownik departed Michigan.

Developed an iterative physical optics approach to account for the effects of shadowing

on a hard target under foliage.

account for part of a target or foliage blocking another part. The new approach

greatly reduces the computation required, making implementation more practical.

Developed a new approach to estimate signal attenuation through dense foliage.

Development of frequency correlation approach to radar channel identification.

some propagation effects are not amenable to modelling. This new approach seems

to be promising for deconvolution of propagation effects, though not physics-based.

Monte Carlo simulations of scattered fields from radar probing of targets in foliage.

models. These will be inserted into the statistical sensor management algorithms.

Development of reduced-complexity

and quantification of their performance-vs.-complexity tradeoff.

scheduling increases exponentially with numbers of target states and modalities.

Sub-optimal approximations will enable practical target detection and tracking.

Development of MATILDA, an algorithm for computing bounds on performance of

time-reversal imaging algorithms (in any physical setting) in noise.

although it is also important for evaluating performance of sensor configurations.

Development of model for decay time for metal-detector-induced electromagnetic field.

Application of prestack hyperbola-detection algorithm (reciprocal+Radon transform).

much less computation is required. If a likely mine is detected, the RMA is applied.

Statistical priors would be helpful here, but are not as vital as in vehicle detection.

Development of 2-D and 3-D blind deconvolution algorithms for even point-spread functions.

Development of statistical models for scattered fields for vehicles in clutter.

statistical models, due to our excellent foliage and vehicle models. These models

will be central to any statistics-based vehicle detection algorithm. However, each

Monte Carlo simulation requires two weeks of computation on 20 processors!