一种基于相对熵的雷达测距估计方法
A Radar Ranging Estimation Method Based on Relative Entropy
鞠美玉 1徐大专 1许欢1
作者信息
- 1. 南京航空航天大学电子信息工程学院,南京 211106
- 折叠
摘要
最大后验(Maximum a posteriori,MAP)是最常用的参数估计方法.然而,MAP方法主要关注后验分布最大峰值的位置,没有充分利用后验分布的完整信息.本文基于相对熵,提出了一种最小散度(Minimum divergence,MD)雷达测距估计方法.首先推导参数的后验分布,然后构造一个与其相似的分布,通过寻找二者散度的最小值得到估计值.仿真结果表明,在雷达测距场景下,MD算法的性能与MAP算法相比,获得了约1 dB的增益,具有较好的估计性能.
Abstract
The maximum a posteriori(MAP)algorithm is the most commonly used parameter estimation method.However,the MAP algorithm focuses on the position of the maximum peak of the posterior distribution and does not fully utilize the complete information of the posterior distribution.This article proposes a minimum divergence(MD)radar ranging estimation method based on relative entropy.Firstly,the posterior distribution of the parameters is derived.Secondly,a distribution similar to them is constructed.Therefore,the value is estimated by finding the minimum value of their divergence.Simulation results indicate that in radar ranging scenarios,the MD algorithm achieves approximately 1 dB gain in performance compared to the MAP algorithm,demonstrating its superior estimation performance.
关键词
参数估计/最小散度/最大后验/雷达测距Key words
parameter estimation/minimum divergence(MD)/maximum a posteriori(MAP)/radar ranging引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据