中文摘要：

针对Alpha稳定分布噪声环境下参数估计性能退化的问题,受类相关熵概念的启发,提出分数低阶类相关熵（FCAS）的概念,并采用分数低阶类相关熵准则对平行因子分析（PARAFAC）算法中基于三线性最小二乘（TALS）迭代准则的目标函数进行了修正,推导出适用于冲激噪声环境的韧性平行因子分析（FCAS-PARAFAC）算法,并将该方法应用于双基地MIMO雷达系统中目标参数估计中。FCAS-PARAFAC算法能够抑制脉冲噪声的影响,具有较好的估计性能,并且能够实现自动配对,仿真实验验证了算法的有效性。

英文摘要：

According to the performance degradation problem of parameter estimation algorithm in the Alpha stable distribution noise, inspired by the concept of correntropy, a new class of statistics, namely, the fractional lower-order correntropy-analogous statistics（FCAS） was proposed. By employing the fractional lower-order correntropy-analogous statistics based cost function in parallel factor（PARAFAC）, the FCAS-PARAFAC algorithm was deduced which can be utilized for the parallel factor under impulsive noise environments. The FCAS-PARAFAC algorithm was applied to parameter estimation in bistatic MIMO radar under impulsive noise environment. The proposed method can suppress the impulse noise interference and has better estimation performance. Furthermore, the estimated parameters are automatically paired without the additional pairing method. Simulation results are presented to verify the effectiveness of the proposed method.