Universal Signal Matching for RF Threat Classification

This effort investigates and assesses the feasibility of new robust dynamic methods to classify threats from received RF signals for application across a variety of sensors and platforms using new information that can now be obtained from modern digital EW receivers. Mathematical and statistically based techniques including covariance functions, autocorrelation and kurtosis to automatically characterize additional emitter characteristics proposed will be justified. Classification includes classic parameters (RF, PW, PRI) and new automatic statistical processes for scan, PRI and RF Agile typing and characterization. New descriptors for Waveform Function (e.g. Track, Search) and Type (e.g. Pulse Doppler, FMCW) will be developed to automatically assess waveform intent for improved situation awareness, support EA, and improve ID. Intentional Modulation on Pulse (IMOP) Type and Characteristics are incorporated into emitter track / correlation using FPGA based IMOP results from a prior Phase II/III SBIR. The processes will be integrated into existing multi-hypothesis Bayesian belief network enabled tracking, classification and identification MATLAB processes (C/C++ for real-time). Established metrics of effectiveness are used to characterize performance. Proof of concept MATLAB code will be demonstrated with signals from RAS’ suite of synthetic signals, real-world modern stressing radar digitized data (unclassified), and PRI and Scan Pattern generation tools.