Advanced Radar Detection Schemes Under Mismatched Signal Models

摘要: Adaptive detection of signals embedded in correlated Gaussian noise has been an active field research the last decades. This topic is important many areas signal processing such as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most existing adaptive algorithms have designed following lead derivation Kelly's detector which assumes perfect knowledge target steering vector. However, realistic scenarios, mismatches are likely occur due both environmental instrumental factors. When a mismatched present data under test, conventional may suffer severe performance degradation. The presence strong interferers cell test makes task even more challenging. An effective way cope with this scenario relies on use "tunable" detectors, i.e., detectors capable changing their directivity through tuning proper parameters. aim book recent advances design tunable focus so-called two-stage obtained cascading two opposite behaviors. We derive exact closed-form expressions for resulting probability false alarm matched homogeneous noise. It turns out that solutions guarantee wide operational range terms tunability while retaining, at same time, overall commensurate detector. Table Contents: Introduction / Radar Detection Targets Schemes Mismatched Signals Enhanced Sidelobe Blanking Algorithms Conclusions