Design of a Multi-target Interference Resistant Adaptive Detector under Homogeneous Reverberation Backgrounds

Abstract

In this study, a new adaptive detector that can resist multi-target interference was proposed to improve the resistance to multi-target interference when using the constant false alarm rate(CFAR) method. This detector uses reference units as the feature value of the background environment and the TreeBagger algorithm for the construction of the estimator. In the training process, the reference units and TreeBagger algorithm were first used to construct the estimator, which was used to estimate the number of interference targets. In the detection process, the reference units were then used as the inputs of the estimator and the number of interference targets in the current background as the output of the estimator. Furthermore, the estimation results were used as the sequence threshold for the detector. Consequently, the detector was able to eliminate the interference targets and complete detection. The performance of the detector under homogeneous reverberation and multi-target interference backgrounds was then analyzed using the Monte Carlo simulation method, and a comparison of the results with those of existing methods was conducted, which showed that the proposed detector had a better performance at resisting multi-target interference.

Keywords

• constant false alarm rate
• interference resistance
• multi-target
• adaptive detector