USA flag logo/image

An Official Website of the United States Government

Study of a class of wavelets transforms for clutter characterization for…

Award Information

Agency:
Department of Defense
Branch:
Army
Award ID:
26287
Program Year/Program:
1994 / SBIR
Agency Tracking Number:
26287
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Information Research
415 Bradford Place North Dartmouth, MA 02747
View profile »
Woman-Owned: Yes
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 1994
Title: Study of a class of wavelets transforms for clutter characterization for enhanced target detection and identification
Agency / Branch: DOD / ARMY
Contract: N/A
Award Amount: $30,000.00
 

Abstract:

Low flying target often experience severe clutter of interference from various sources, which is difficult to characterize. Wavelets and wavelet transforms present a new and promising mathematical approach for clutter characterization for enhanced detection and identification of low flying targets. A class of wavelet transforms is proposed in this study to determine the feasibility of the wavelet based approach. They include the Daubechies/Mallat wavelet method, the wavelet decomposition method, and the Gabor transform method. All three methods provide effective time-frequency analysis of the signal. The RF waveform can be converted to its lowpass equivalent for analysis. The relative merits of the three methods are examine, especially their performance in detection and identification. The implementation and computation issues are also considered, in view of the real-time processing requirements. Simulated data sets are used to evaluate the detection and identification performances. Comparison with the traditional approach is also considered.

Principal Investigator:

Dr. C. H. Chen
5089937024

Business Contact:

Small Business Information at Submission:

Information Research
415 Bradford Place North Dartmouth, MA 02747

EIN/Tax ID:
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No