Constant false alarm rate detection in Pareto Type II clutter

• Published in: Digital signal processing Vol. 68; pp. 192 - 198
• Main Authors: Weinberg, Graham V., Bateman, Lachlan, Hayden, Peta
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-09-2017
• Subjects: Constant false alarm rate; Ingara data; Pareto Type II clutter; Radar detection; Sliding window detector; Transformations; Constant false alarm rate; Pareto Type II clutter; Sliding window detector; Ingara data; Transformations; Radar detection
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2017.06.014

Recent investigations have shown that the Pareto class of models provide a valid approximation for the statistical structure of the backscattering from the sea, for high resolution X-band maritime surveillance radar. This has stimulated the research and development of non-coherent radar detection processes for operation in such a clutter model environment. Using data from Defence Science and Technology Group's X-band radar the application of a Pareto Type I clutter model has been justified, which has facilitated the development of sliding window decision processes. However it has been found that when these detectors are applied to synthetic target detection in real data there are some issues resulting in substantial detection losses. In order to rectify this it is necessary to investigate the development of radar detection schemes under a Pareto Type II clutter model assumption. Using a transformation approach for radar detector design it will be shown that it is possible to construct detection processes that achieve the constant false alarm rate property with respect to the Pareto shape parameter, as in the Pareto Type I case, while requiring a priori knowledge of the Pareto scale parameter. Performance analysis includes application to real X-band clutter returns with synthetic target models.
•Sliding window detectors developed for Pareto Type II clutter.•Designed to overcome saturation issues in real applications.•Geometric mean, order statistic and smallest-of geometric mean detectors examined.