Resource Manager Enhancements for Automated Maritime Mission Prosecution

Maritime situational awareness in dense environments depends on effective resource management to combine information from radar, EO/IR (electro-optical/infra-red), and ESM (electronic support measures) technologies.  There are several situations in which information from radar systems becomes paramount for target identification; AIS and ESM data can be degraded, spoofed, or jammed and EO/IR has shorter range and more potential exposure for the sensor platform.  Inverse Synthetic Aperture Radar (ISAR) range-Doppler imagery has a long history of being used for maritime classification via airborne platforms at long ranges, but in dense environments typical ISAR sessions (15-30 seconds) may be too long to effectively construct a complete surface picture.  Conversely, High Resolution Radar sessions cannot provide enough detail to classify targets down to the Fine Naval Class (FNC) level.  For these reasons, ISAR Snapshot (ISAR-SS) modes with sessions of less than or equal to 1 second are being developed to enable FNC level classification in a fraction of the time.  In order to use these ISAR-SS sessions efficiently in dense environments, they must provide information to the resource manager regarding usefulness of the data obtained, confidence in the resulting ship classification, and the qualities required for additional data, such that additional ISAR-SS sessions on the same target can be scheduled only as needed. RDRTec’s Automated Combatant Vessel Classifier (ACVC) can classify a ship to its fine naval class level using ISAR data collected at long ranges in all environments. ACVC is actively being integrated into tactical programs for AST (Advanced Sensor Technology), Triton, and P-8A. ACVC combined with our third-party open architecture ISAR modes, puts us in a unique position to develop and test an ISAR-SS mode.  RDRTec proposes to build off of its existing high TRL multi-hypothesis, Track-Before-Detect acquisition algorithms, motion compensation, and focusing algorithms to generate well focused short CPI ISAR-SS imagery.  ACVC calculates classification confidence, or Probability of Correct Classification (PCC) at Fine, Gross, and Perceptual Naval class levels (FNC, GNC, PNC, respectively) and reports ISAR dwell status messages indicating specific image quality issues.  PCC and status messages will enable a resource manager to decide when and under what conditions an ISAR-SS should be repeated or if the resulting classification is of sufficient quality to add to the surface picture.  ACVC classification results are already being integrated into mission system track databases and real time data assessments during long dwell ISAR’s are available for use by resource schedulers.  This will be furthered with the addition of ISAR-SS to aid a resource managers ability to only spend resources as needed to maintain classification of targets within a dense operating environment.