Radar Clutter Characteristic Analysis And Research Of Adaptive Clutter Suppression Technoledge

Radar is used objects on the reflection of electromagnetic waves, scattering characteristics to identify targets and to measure its position in space. Radar in various work environments, such as land, artificial structures, the flock, sexual intercourse, aircraft, missiles, satellite will have a radar echo. Some of the objects or background is interested and hoped to be observed, known as targets, and some of them need to be suppressed, known as clutter. As the clutter along with radar target signal entered the radar signal receiver, and its strength is usually stronger than the target signal scores of dB. It causes great difficulties in radar detection. Therefore, the clutter characteristic analysis, modeling, simulation, and research on advanced clutter suppression methods to suppress clutter significant are of great importance.This paper briefly discusses the basic concepts of radar systems, as well as radar clutter suppression technology with a simple presentation. Then it analyzes the range and distribution of power spectral characteristics of the radar clutter and the two most elaborate typical clutter simulation methods. It also contains the analysis of the radar clutter simulation using ZMNL. On this basis, this paper discusses the adaptive moving target indication technical in-depth and the study on the working principle and its implementation, which mainly discusses the adaptive filter. Based on the review and analysis of the existing the minimum mean square of the variable step size adaptive filtering algorithm, this paper presents an optimization algorithm. The optimization algorithm can take instantaneous error and the input signal as step adjustment factor. It can not only maintain a higher rate of convergence and tracking performance, but also guarantee the smaller steady-state error as the change of the input signal. According to the simulation experiments with the existing algorithm, the step size of the optimization algorithm convergence quickly and maintain the steady-state error is smaller when the input signal changes suddenly. Finally, this paper discusses the subject of future research directions according to the current situation.