为了利用雷达高分辨距离像(HRRP)中的相位信息改善识别系统性能，提出了一类针对复距离像的目标识别新方法。分析了复距离像的统计特性，将识别领域常用的三种高斯模型——自适应高斯分类器(AGC)模型、联合高斯(JG)模型和概率主分量分析(PPCA)模型推广至复数域对复距离像统计建模。研究表明，这三种模型及其参数估计结果均不受距离像初相的影响。此外，为了解决噪声环境中的稳健识别问题，进一步提出了噪声稳健的模型修正方法。实验结果显示：在识别过程中加入距离像相位信息能够获得更高的正确识别率；且经过噪声稳健修正后的模型大幅改善了低信噪比下的识别性能。

This paper develops a new recognition method utilizing the phase information to improve the performance of radar high resolution range profile (HRRP) target recognition system. The statistical properties of complex HRRPs are analyzed, and three common Gaussian models, which are adaptive Gaussian classifier (AGC) model, jointly Gaussian (JG) model and probabilistic principal component analysis (PPCA) model, are generalized to the complex domain to model complex HRRPs. It is demonstrated that the structures and parameter estimators of the three complex models are invariant to the initial phase. Furthermore, to enhance the recognition performance under low signal-to-noise ratio (SNR) conditions, a noise robust modification algorithm is introduced. Experimental results show that the proposed method can obtain higher average correct recognition rates. Also, the modified models can deal well with the noisy test samples.