摘要
针对卡尔曼滤波压缩感知在信道估计时伪测量过程计算效率较低的问题进行了研究,提出了一种高性能的卡尔曼滤波压缩感知信道估计算法。对伪测量过程的近似l0范数约束框架进行了进一步研究,引入高斯核函数对雅克比赋权矩阵的列向量进行优化,使算法对稀疏信号支撑集的重构速度有较大程度提升;同时,引入微分熵确立了收敛指标,降低了算法的运行时间。仿真表明,在同等条件下,该算法相对于原有算法,估计精度和收敛速度均有较大程度提高,在低信噪比和不同稀疏度下都具有较好的鲁棒性和实用性。
In order to improve the speed and accuracy of pseudo measurement process, this paper proposed an improved signal channel estimation algorithm based on Kalman filtered compressed sensing. Based on the research outcome so far, this paper introduced the Gaussian kernel function to optimize the useful row vectors of support set under the 10 norm constraint, which made the algorithm have a better performance in finding support set. At the same time, this paper defined the evaluation index based on differential entropy to reduce the reconstruction time. The simulation results show that the accuracy and convergence speed of the proposed algorithm are improved greatly compared to the traditional algorithm, good robustness and practicability can be achieved under low signal-to-ratio or sparse degree.
出处
《计算机应用研究》
CSCD
北大核心
2017年第4期1217-1220,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61201209)
陕西省电子信息系统集成重点实验室基金资助项目(2011ZD09)
关键词
压缩感知
稀疏多径信道估计
卡尔曼滤波
伪测量过程
compressed sensing
sparse muhipath channel estimation
Kalman filter
pesudo measurement
