针对高分辨雷达低杂噪比的情况,在复合高斯杂波加热噪声的背景中研究了分布式目标的检测问题。首先假设内部热噪声和外部杂波统计独立,在给定杂波纹理分量的前提下,将白高斯热噪声加上复合高斯杂波之后的总干扰近似等效处理成一个新的复合高斯杂波,只是将其参数做了适当调整。然后将分布式目标建模为子空间模型,基于Rao检验构造了N-Rao检测器。通过对N-Rao检测器虚警概率的计算表明其具有恒虚警率特性。最后通过Monte Carlo仿真实验表明,杂波形状参数的减少与杂噪比的增加都会使N-RAO检测器的检测性能有所提高,且在低杂噪比的情况下,N-RAO检测器有很好的检测性能。
On the condition of the low clutter-to-noise ratio(CNR) of high-range resolution radar system,distributed targets detection embedded in compound-Gaussian clutter plus thermal noise is studied.Firstly,we assume that the thermal noise is statistically independent of compound-Gaussian clutter.Given a specific value of r which is usually named texture,the total interference which is composed by the superposition of compound-Gaussian clutter and white Gaussian thermal noise is approximatively equivalent to a new compound-Gaussian clutter,whose parameters are suitably adjusted for the actually condition.And then,based on the Rao test,the new N-Rao detection is derived to implement the distributed target.The distributed target is modeled as a subspace random signal.By calculating the probability of false alarm,it is shown that the N-RAO detection is a constant false alarm rate(CFAR) detection.In the end,performances of the proposed detector are assessed through Monte Carlo simulations.The experimental results show that a decrease of the shape parameter v makes the N-Rao detection performance improved.This case that makes the performance improved can be made by increasing the CNR.And in low CAT?,the N-Rao detection has better detection performance.