IR target detection and clutter reduction using the interacting multiple-model estimator

The significant challenge in long range low SNR IR target acquisition is performing reliable target detection and classification while maintaining a reasonable false alert rate. Existing IR systems experience excessive amounts of clutter and false alerts. A target motion discriminator (TMD) technique based on the interacting multiple model using probabilistic data association with amplitude information estimation algorithm is presented to exploit the consistency of position and motion information observed consistency of position and motion information observed over multiple scans to form a robust classification decision. Several motion class models reside within the TMD, to provide accurate target state estimates and target likelihood functions for both maneuvering and non- maneuvering contacts. Classification performance is dramatically enhanced by using a 'no target model' to reject contacts which exhibit erratic motion, i.e., inconsistent with a target motion model while promoting contacts with consistent motion. The accurate estimation of target dynamics obtained by the IMM approach provides the capability to reject clutter and reliability detect a dim threat. A robust sequential likelihood ratio test which minimizes the decision time and improves target declaration performance is developed and demonstrated using real data collected under various environmental conditions.