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

2. 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. 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 4

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

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% reduction in 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) 6

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

8. 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. 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. 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