Jozef Perďoch, Stanislava Gažovová, Miroslav Pacek
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An improved radar clutter suppression by simple neural network
The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range-Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets.
期刊介绍:
IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications.
Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.