Mine Countermeasures Data Fusion

What Is It?

Mine Countermeasures (MCM) Data Fusion is the employment of multiple, complimentary sensing assets to improve the confidence and overall situational awareness in MCM operations. It involves both optimal planning for multiple sensors as well as jointly processing the data produced. This data includes both tactical and environmental plus historical and current.

 

How Does It Work?

By fusing data from multiple sources, risk is reduced as fewer targets are missed, and time is reduced as fewer non-targets possessing mine-like characteristics are presented as false alarms.

 

What Will It Accomplish?

By fusing data from multiple sources, risk is reduced as fewer targets are missed, and time is reduced as fewer non-targets possessing mine-like characteristics are presented as false alarms.

 

The Office of Naval Research’s MCM Data Fusion product line began in 2007 and continues through 2011. It is part of an Organic MCM Enabling Capability (EC) focusing on the gap of clearing large areas of mines without cued intelligence or surveillance. In addition to data fusion and advanced processing / sensors, this EC focuses on buried mines, advanced neutralization / sweeping, and automation for the Littoral Combat Ship.

Although this product line ends in 2011, multi-asset employment and multi-sensor data fusion will remain a core enabler for the future of network-centric warfare. MCM Data Fusion consists of parallel investments in multi-sensor data processing and planning technologies. The data processing technologies consist of joint detection and feature extraction (e.g., broadband plus high-frequency sonar), classification across domains (e.g., appropriate sharing of information from same sensor / different environment or similar sensor / same environment), change detection, and co-registration. Environmental information is also incorporated to adapt and / or adaptively select detectors, features, and classifiers. The multi-sensor planning technologies consist of Bayesian search effectiveness evaluation, multi-objective optimization, and a multi-sensor hierarchical decision framework. Additional effort is invested in probabilistic techniques that blend discrete multi-sensor data with prior knowledge or intelligence.

The primary warfighter payoff of MCM Data Fusion is the reduction in risk and the MCM timeline. Currently, this timeline is exceedingly long and requires ships, Sailors, and divers to be in the minefi elds. By fusing multi-sensor data, fewer mines are missed and fewer non-mines appear as false alarms. An additional payoff is the reduced manning requirement due to the automation of much of the planning and data processing.

 

Research Opportunities:

  • Environmentally adaptive object recognition
  • Learning across sensor modes
  • Multi-sensor in situ retraining
  • Multi-sensor planning consistent with mission-level goals

 

Mike Traweek
(703) 696-4112
mike.traweek@navy.mil

Jason Stack
(703) 696-2485
jason.stack@navy.mil

 

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