Enhanced De-Interleavers for Submarine Electronic Warfare Support (ES) Systems
This technology consists of innovative algorithms to perform clustering and de-interleaving in open architecture frameworks for submarine EW and ISR applications. It leverages RAS technology for unique emitter identification and specific emitter features, performing automatic clustering on pulse and “wideband segment” measurements, provided by multiple sensor types, for signals with multi-dimensional agilities, wide RF operating ranges, high time-bandwidth wideband/LPI signals, and other complexities. It then de-interleaves similar, same-type signals over frequency and time, classifies agilities, and provides an unambiguous emitter report.
Challenging radar waveforms are addressed for which current techniques are inadequate. Key benefits include greatly enhanced capabilities to cluster and de-interleave pulses, and separate and identify emitters employing multi-dimensional agility, complex intentional modulations, large time-bandwidth waveforms, and emitters operating over very wide RF bandwidths.
Development of Algorithms for Characterizing Interleaved Emitter Pulse Trains with Complex Modulations
RAS is developing techniques to process data from non-contiguous pulse clusters resulting from periodic sampling (spectral, spatial, temporal) of the signal environment to improve Emitter Identification (EID). The resulting system will have improved EID with reductions in the size of candidate emitter lists and ambiguities, thereby improving situational awareness.
The system utilizes classical EW parameters augmented with calculated Specific Emitter Features and measurement of IMOP characteristics to enhance real-time pulse processing and clustering of RF-agile and PW/PRF-agile emitters, Cluster-to-Track Correlation, Specific Emitter Tracking, and EID.
This low-cost technology identifies RF emitters using scanning receivers that obtain the same performance levels as wideband receivers while reducing operating and maintenance costs. It enhances performance in identifying complex emitters within dense electromagnetic environments, resulting in improved ship survivability.
Cognitive Software Algorithms Techniques for Electronic Warfare
The Cognitive Reasoner for Electronic Warfare Systems (CREWS) applies artificial intelligence signal processing to quickly classify complex, highly agile threat signals based on functional characteristics learned from the observed, possibly incomplete, waveform data. Research Associates of Syracuse (RAS) provides innovative signal processing solutions to challenging Electronic Warfare/Electronic Intelligence (EW/ELINT) problems. Initial targeted applications are Block 2 and 3 versions of AN/SLQ-32. CREWS technology is expected to reduce significantly (or even eliminate) reliance on threat libraries for emitter classification, which lowers maintenance cost and improves threat awareness in complex emitter environments. Phase I demonstrated accurate performance of selected machine learning classifiers trained using limited real radar data. Phase II is developing and demonstrating a full cognitive processing prototype using more extensive radar threat datasets. RAS seeks prime contractor support to integrate CREWS software into EW systems.