Abstract
Abstract The characteristics of non-Gaussian clutter in radar systems are different from standard waveforms. To fully filter to achieve the accuracy of radar detection, the paper developed a radar simulation system based on virtual reality technology. The article uses a non-Gaussian mathematical model to simulate and collect the clutter generated by the system and realise the generation of data sequence according to the power spectrum. The research results show that the radar cross-section modelling, target recognition, anti-recognition and data fusion technology of visible targets can all be well applied in this system.
Highlights
In radar imaging and evaluation research, the technology is developed based on virtual reality
The white noise generates a correlated Gaussian random process through the filter and modulates it with a random variable S with the required single-point probability density function [4]. This method is limited by the order of the sequence and the autocorrelation function
We propose a multi-functional simulation analysis system based on the memoryless nonlinear transformation (ZMNL) model of the generalised Wiener process
Summary
In radar imaging and evaluation research, the technology is developed based on virtual reality. The non-Gaussian distribution model can simulate the statistical characteristics of the actual radar echo more accurately. The white noise generates a correlated Gaussian random process through the filter and modulates it with a random variable S with the required single-point probability density function [4]. This method is limited by the order of the sequence and the autocorrelation function. The ZMNL of the generalised Wiener process makes up for the shortcomings of the SIRP method It can realise the signal simulation of various commonly used statistical models. We use the ZMNL model method to simulate the correlated non-Gaussian distribution clutter signal
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