基于循环自相关/平均幅度差函数的弹道目标微动周期估计

A Micro-Doppler Period Estimation of Ballistic Targets Based on Circular Autocorrelation and Average Magnitude Difference Function

雷达微多普勒(m-D)是弹道目标的突出特征,为弹头识别提供了重要手段。然而,当弹道目标的微动伴随平动时,时频分布不再表现为正弦调制曲线,此时基于时频分布正弦假设的微多普勒特征提取方法可能失效。针对这一问题,提出了一种循环自相关函数(CACF)和循环平均幅度差函数(CAMDF)相结合的估计算法,来获取时频分布的循环系数矩阵和该矩阵的平均循环系数,从而估计出弹道目标的微动周期。该算法以时频分布的循环周期性代替正弦调制的周期性,不需要假设目标平动已被准确补偿,有效克服了传统微动周期估计方法的不足。理论推导论证了该算法的可行性,仿真实验验证了该算法的有效性和抗噪性。

Micro-Doppler(m-D)in ballistic targets is characterized by applying an important means to warhead recognition.However,when the micro-motion of the ballistic targets is accompanied by macro-motion,the time-frequency representation is no longer a sinusoidal modulation curve.Aimed at the problem that the micro-Doppler feature extraction method based on the sinusoidal hypothesis of time-frequency representation may become ineffective,an estimation algorithm based on circular autocorrelation function(CACF)in combination with the circular average amplitude difference function(CAMDF)is proposed to obtain the circular coefficient matrix of time-frequency representation and average circular coefficients of the matrix,estimating the m-D period of ballistic targets.The algorithm does not need to assume with the target macro-motion having been accurately compensated and the shortcomings of traditional m-D period estimation methods having been overcome effectively.The feasibility of the algorithm is demonstrated by theoretical derivation,and the effectiveness and anti-noise of the algorithm is verified by simulation experiments.