Hybrid Detection Approaches using the Single Data Set Algorithm

• Published in: Proceedings of the IEEE National Radar Conference (1996) pp. 1 - 6
• Main Authors: Aboutanios, Elias, Rosenberg, Luke
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.09.2020
• Subjects: Clutter; coherent; Covariance matrices; detection; Detectors; Logic gates; Radar; Radar clutter; Sea clutter; single snapshot; Training data
• ISSN: 2375-5318
• DOI: 10.1109/RadarConf2043947.2020.9266535

The dynamic nature of sea clutter can lead to significant degradation of the performance of traditional coherent target detectors such as the Generalised Likelihood Ratio Test (GLRT) and Adaptive Matched Filter (AMF). These detectors are designed for complex Gaussian distributed clutter and rely on the availability of homogeneous training data. However, depending on the radar waveform and collection geometry, these conditions can be violated in maritime environments as the statistics of the clutter are slowly varying over time and range and can be non-Gaussian. The Single Data Set (SDS) algorithm was proposed to mitigate this problem by foregoing the need for training data. In this work we propose to combine the AMF and SDS approaches by devising a novel strategy for capitalising on both test and training data to improve the detection performance. Unlike heterogeneity screening strategies such as the Generalised Inner Product (GIP), the new technique is informed by the clutter statistics to employ all of the available range gates. The new algorithm performance is compared with the AMF, SDS and GIP-based hybrid detectors and shown to offer good performance in homogeneous environments and superior performance in heterogeneous environments.