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为了使直扩系统在未知非高斯噪声模型下准确地捕获微弱直扩信号,提出一种新的基于整数最大熵概率密度函数(probability density function,PDF)估计和局部最佳检测(locally optimal detection,LOD)相结合的相关前一维幅域处理捕获方式。通过蒙特卡洛数据仿真可以发现:在非高斯噪声的情况下,基于整数最大熵PDF估计的LOD检测器的检测性能要明显优于常规平方和(Square-Sum,SS)检测器,在信噪比为-25~0 dB时分别有2.0%~99.0%的改善。从根本上改变了常规SS检测器在非高斯噪声模型下性能急剧下降甚至无法工作的现象。
In order to make the DSSS system accurately capture the weak DS signals under the unknown non-Gaussian noise model, a new maximum probability density function (PDF) estimation based on integer maximum entropy and locally optimal detection LOD) combined with the previous one-dimensional domain processing capture. The Monte Carlo simulation results show that the LOD detector based on integer maximum entropy PDF is superior to the Square-Sum (SS) detector under non-Gaussian noise, Compared to -25 ~ 0 dB, respectively, from 2.0% to 99.0% improvement. Fundamentally changed the conventional SS detector performance under non-Gaussian noise model dropped dramatically or even not work phenomenon.