Assessment of RCS-specific SNR and Loglikelihood Function in Detecting Low-observable Targets and Drones Illuminated by a Low Probability of Intercept Radar Operating in Littoral Regions
Keywords:Low-observable/Stealth Target, Radar Cross-section (RCS), Low Probability of Interception (LPI) Radar, Ultrawideband (UWB) Radar, Loglikelihood Function, Maximum Likelihood (ML) Estimation
The objective of this study is to deduce signal-to-noise ratio (SNR) based loglikelihood function involved in detecting low-observable targets (LoTs) including drones Illuminated by a low probability of intercept (LPI) radar operating in littoral regions. Detecting obscure targets and drones and tracking them in near-shore ambient require ascertaining signal-related track-scores determined as a function of radar cross section (RCS) of the target. The stochastic aspects of the RCS depend on non-kinetic features of radar echoes due to target-specific (geometry and material) characteristics; as well as, the associated radar signals signify randomly-implied, kinetic signatures inasmuch as, the spatial aspects of the targets fluctuate significantly as a result of random aspect-angle variations caused by self-maneuvering and/or by remote manipulations (as in drones). Hence, the resulting mean RCS value would decide the SNR and loglikelihood ratio (LR) of radar signals gathered from the echoes and relevant track-scores decide the performance capabilities of the radar. A specific study proposed here thereof refers to developing computationally- tractable algorithm(s) towards detecting and tracking hostile LoTs and/or drones flying at low altitudes over the sea (at a given range, R) in littoral regions by an LPI radar. Estimation of relevant detection-theoretic parameters and decide track-scores in terms of maximum likelihood (ML) estimates are presented and discussed.
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Copyright (c) 2021 Perambur Neelakanta, Dolores De Groff
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