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Active Signal Processing Enhancements for Classification of Low Signal-to-Noise Ratio (SNR) Sonar Signals in Doppler Clutter

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00024-17-C-4004
Agency Tracking Number: N151-034-0040
Amount: $499,999.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N151-034
Solicitation Number: 2015.1
Timeline
Solicitation Year: 2015
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-01-10
Award End Date (Contract End Date): 2018-01-10
Small Business Information
5860 Trinity Parkway
Centreville, VA 20120
United States
DUNS: 135121148
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Travis Cuprak
 (703) 968-6128
 tcuprak@adaptivemethods.com
Business Contact
 Judy Barhorst
Phone: (703) 968-6110
Email: jbarhorst@adaptivemethods.com
Research Institution
N/A
Abstract

The acoustic environment encountered in pulsed active sonar provides some of the most formidable challenges seen in modern signal processing. The confluence of strong bottom clutter, ownship Doppler spreading, and discrete mutual interference results in clutter leakage and sidelobes across beam and Doppler spaces. Platform motion causes clutter returns to be shifted in Doppler, leading to elevated noise shoulder near the zero Doppler ridge, impacting the detection and classification of low Doppler contacts. Current adaptive signal processing algorithms suffer from sample support limitations due to the rapidly changing active environment. In order to prevent signal suppression and maintain robust performance, these algorithms must be run conservatively, limiting their ability to null clutter and interference. In this proposal, Adaptive Methods outlines a new and innovative approach to active sonar signal processing. We build upon our experience in rapid adaptation to provide the next generation of active signal processing. Our approach, termed the Robust Active Signal Processing Adaptive Processor (RASAP) enables aggressive robust adaptation in sample starved environments. This approach mitigates the corruptive effects of interference and leakage while minimizing impact on downstream processing, leading to improved detection, tracking, and classification performance.

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