ESM-RADAR and IFF Fusion for Enhanced Airborne Surveillance Systems

This paper proposes a robust plot-level fusion of airborne radar, IFF interrogator and airborne ESM sensors, for robust situational awareness in airborne surveillance. In the proposed work, an Interactive Multiple Model (IMM) filter-based plot-level fusion algorithm is presented. An IMM filter is primarily a bank of Kalman filters running in parallel, and their estimates are mixed using a parameter called model probability. The model probability is updated recursively as the measurement data arrive. Here, primary radar (PR) and Identification of Friend or Foe (IFF) sensors are used along with ESM bearing data for fused track estimation. The IMM filter parameters have been optimized for improved performance. The proposed approach has been assessed, and its performance evaluated through simulated scenarios. Performance has been found to improve by nearly 30%.