A Comparison of Methods to Detect and Eliminate Secondary Target Returns from Adaptive Radar Training Data

Award Information
Agency:
Department of Defense
Branch
Defense Advanced Research Projects Agency
Amount:
$99,000.00
Award Year:
2001
Program:
SBIR
Phase:
Phase I
Contract:
DAAH0101CR153
Award Id:
53321
Agency Tracking Number:
01SB1-0133
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
50 Mall Road, Burlington, MA, 01803
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
094841665
Principal Investigator:
WilliamSnyder
Senior Research Engineer
(781) 273-3388
wcsnyder@alphatech.com
Business Contact:
AndrewMullin
Gen Counsel & Dir of Cont
(781) 273-3388
andy.mullin@alphatech.com
Research Institute:
n/a
Abstract
For SAR, spatial and temporal adaptive processing (STAP) uses samples local to the cell being processed to estimate local clutter and noise statistics. If these samples are corrupted by returns from other unknown targets, the statistics are invalid and theperformance of the algorithms can be degraded. We propose to conduct a range of experiments to evaluate signal detection, statistical, multi-look, and tracking approaches for detecting unknown target returns in the training data. These include the CLEANalgorithm for detecting and eliminating returns by iteration, a statistical jackknifing approach to detect anomalous training data cells, combining scans from different angles to optimize detection, and near-clutter tracking methods such as

* information listed above is at the time of submission.

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