Using an adaptive INS, monopulse MUSIC, and STAP (AIMS) for targeting

Abstract

Angle estimation from target signals suffers from mainbeam jamming. One way to counteract the problem is to integrate sensor data from multiple sensors. An adaptive monopulse multiple signal classification (MUSIC) algorithm discerns the azimuth and elevation angle estimation or true spectrum amongst jamming. Relying solely on the algorithm results in an undesirable probability of error in target identification, classification and recognition. By integrating the azimuth and elevation signals from an integrated navigational system (INS) and monopulse radar, the probability of accurate detection of target location increases. The AIMS algorithm is designed for targeting and integrates sensor signals from an adaptive INS system which has repeated measurement location updates from a ground-based target, a four-aperture monopulse radar, which adaptively reduces mainbeam jamming from the MUSIC algorithm for reliable angle estimation, and a space-time adaptive processor (STAP) which isolates targets in the presence of clutter. The results show that the sensor integration of the AIMS algorithm effectively and efficiently identifies the correct target information.