Performance analysis for airborne radar and clutter suppression

Abstract

In airborne radar systems the returning echoes are taken by the antenna and passed through the detector circuits to obtain the envelope of the signal. To conquer the undesired clutter and interference constant false alarm rate algorithms are used these are well known for target identification in the clutter area. The aim of this work is to investigate the performance of different types of CFAR algorithms, and the effectiveness of the clutter suppression and target detection in radar systems. The comparative performance results show that the neural net- CFAR (NN-CFAR) detector has superior target detection in comparison with other types of CFAR detectors in clutter environments. Insights can be learned on issues like trespasser’s motion, which are tougher to track due to complexity and hard terrains the adaptive learning helps it model the noise complexities thus decreasing false alarms and increasing probabilities of detection. In this work, the performance of NN-CFAR has been established and could prove to be a durable solution for improving radar target detection in scenarios where the environment is complex.