GTD模型下一种带感知字典的快速HRRP综合算法
A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model
摘要：
为了获得高分辨距离像（HRRP），几何绕射理论（GTD）参数模型被广泛应用于频率步进雷达系统。本文提出了一种快速合成距离像算法--带感知字典的正交匹配追踪算法（OMP-SD）。将传统的高分辨距离像合成问题归结为冗余字典上的稀疏逼近问题。该方法利用目标稀疏分布的先验信息，即使在数据丢失的情况下也能实现目标的距离像。此外，通过引入感知字典（SD），降低了计算复杂性，同时减轻了模型失配。计算复杂度从OMP的O（MNDK）次触发器降低到OMP-SD的O（M（N +D）K）次触发器。仿真实验分别在加性高斯白噪声（AWGN）和无噪声情况下验证了该方法的优越性。
英文摘要：
To achieve high range resolution profile (HRRP), the geometric theory of diffraction (GTD) parametric model is widely used in stepped-frequency radar system. In the paper, a fast synthetic range profile algorithm, called orthogonal matching pursuit with sensing dictionary (OMP-SD), is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori information that targets are sparsely distributed in the range space, the synthetic range profile (SRP) can be accomplished even in presence of data lost. Besides, the computational complexity is reduced by introducing sensing dictionary (SD) and it mitigates the model mismatch at the same time. The computation complexity decreases from O(MNDK) flops for OMP to O(M(N +D)K) flops for OMP-SD. Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.
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