Radm John Pearson, USN (Ret) 9 MAY 2012

1. Material Identification Reflectivity Kernel (MIRK) forMCM/Mining Single Pass Detect-to-Engage (DTE) Operations Radm John Pearson, USN (Ret) 9 MAY 2012

2. DTE Challenges/Unmet Needs Sonar detection/classification currently/usually only at “possible/Mine-like object” level Too many objects of interest for many scenarios Too many false targets in cluttered environments like the Persian Gulf Too many neutralization weapons required/wasted due to false targets Current systems post-mission analysis (PMA) time precludes DTE Successful discrimination and neutralization Successful discrimination currently questionable True autonomous DTE operations not available

3. Background: What is MIRK? Proven USAF Program of Record Three phases of SBIR development and transition Material Identification-Synthetic Aperture Radar (MISAR) Demonstrated sonar capability Phase I/II SBIR’s with Mk 48 submarine torpedo program Initial NUWC analysis after MIRK processing of torpedo sonar data showed highly enhanced performance Initial MIRK processing of real data shows promising results on two other sonars BQQ-10 submarine active sail array SQS-53C surface ship sonar

4. 4 Phase I SBIR Hypotheses/Approach Echo returns from active interrogation of an underwater object contain reflectivity kernel (RK) clues • Deconvolution - Not a new problem/Knowing transmitted signal and echo return, solve for RK • New, unique mathematical technique devised by Prometheus • Use Time vs. Frequency Domain approach • Highly stable, real-time processing • Approach: Classify active sonar contacts based on material identification technique • Formulate theoretical hypotheses showing the existence of an RK in active sonar echo data • Develop algorithm to extract the RK from an actual received echo, given parameters of transmitted sonar signal

5. The Inverse Problem Input Given the input signal (s) and the scattered or output signal (f), find the Reflectivity Kernel (K) Kernel Output

6. 6 Demonstrated and Potential Capabilities • Demonstrations using Mk 48 torpedo at sea data • Processing of torpedo data recorded during SOBA range exercises containing the WSTTT target showed significant detection/classification performance enhancement. • Demonstrated over 95% reductionin false alarm rate over the torpedo performance • NOT aspect or elevation angle dependent • Algorithm computational efficiency allows integration into torpedo software in the near term • Algorithm applied to 53C data has shown similar capabilities. Ongoing efforts will quantify performance enhancements. • Potential to detect/classify/identify (?) mines, bottomed targets and underwater vehicles/satisfy CNO Abbreviated Acquisition Program (AAP) requirement for improved Pdc, Pid and Pfa in highly complex environments (enable sonar target discrimination capability in DTE operations)

7. 7 MIRK Detection / Classification Target Detection / Classification Rocks & Rock Ridges

8. Road to Effective MIRK DTE Operations Short term Demonstrate MCM proof of concept using current MIRK target discrimination capability with recorded MCM sonar data Show significant reduction in false alarm rate in highly cluttered environment Software upgrade only Investigate MIRK type processing for laser mine detection sensors Long term MIRK processing in all MCM sonars to enable real time DTE MIRK processing for future mining target detection devices (TDD) using acoustic detection/homing

9. Advantages • Relatively insensitive to operating frequency • Current waveforms provide adequate bandwidths • Robust in low signal to noise ratio (SNR) echo returns • Operates in parallel with existing detect/classify functions • Can be inserted into many active sonar systems with only a software impact • Torpedoes: MK-54, CBASS, MK-48 (Current Phase II SBIR) • Submarine: AN/BQQ-10 (Acoustic Rapid Commercial-Off-The-Shelf Insertion (ARCI)) • Surface ship: AN/SQS-53C • Littoral and Mine Warfare: MCM-1 class ships’ SQQ-32 HFWB (ARCI) sonar, Littoral Combat Ship Mission package, UUVs MIRK is a single ping detection/classification algorithm that operates on active sonar returns over their operational listening range without need of imaging or multiple looks

10. Recommendations Short Term Labs/MCM contractors provide MCM sonar data for MIRK processing (e.g. SQQ-32 HFWB (ARCI), EOD UUV, AN/AQS-20/24, AMNS, etc. sonar) Prometheus apply MIRK processing, labs conduct analysis of processing results for validation Similar action for laser mine detectors Similar action for additional ASW sonar processing and validation Long Term Refine and optimize MIRK processing for MCM sonars Fully integrate MIRK into current and planned MCM sonars Evaluate MIRK DTE operations in future mining TDD’s