Dual-sided subspace mappings for main-beam multi-target super-resolution in clutter and interference

The need for localizing multiple main-beam targets in the presence of interference, clutter and jammers arises in scenarios such as: air defense, where a radar detection may correspond to one or multiple targets (e.g., a missile launched from an airborne platform); ballistic missile defense, where the incoming missile complex involves a large number of objects; cruise missile defense for low angle target tracking in multipath; etc. This paper presents a super-resolution approach for the sub-beamwidth localization of multiple main-beam targets in the presence of clutter and interference, doing so with a single snapshot of sensor array data while placing nulls at specified locations. Such process first maps array data into compact subspaces containing the information of interest (e.g., a cluster of receive beams with imbedded nulls). Super-resolution is then achieved via small-matrix operations on the subspace data. The ensuing result is a mapping of array data into a compressed “information domain,” yielding an effective, practical Sensor-to-Information process that is more accurate, robust and versatile than current super-resolution methods.