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Improved relocatable over‐the‐horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm
Author(s) -
Perlovsky Leonid I.,
Webb Virgil H.,
Bradley Scott R.,
Hansen Christopher A.
Publication year - 1998
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/98rs00445
Subject(s) - clutter , computer science , radar , stationary target indication , radar tracker , artificial intelligence , artificial neural network , moving target indication , algorithm , low probability of intercept radar , track before detect , radar engineering details , continuous wave radar , radar imaging , telecommunications
An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over‐the‐Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug‐smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model‐based neural network that combines advantages of neural network and model‐based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track‐before‐detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal‐to‐noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the “Einsteinian” likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.