基于包络拟合的Chirplet分解算法

Chirplet Signal Decomposition Based on Envelope Fitting

针对信号自适应Chirplet分解未知参数多、分解算法运算量大的问题,提出了一种基于包络拟合的Chirplet自适应分解算法。该方法利用二次相位函数在时间上的积分估计调频率,通过对包络主瓣峰值的幅度拟合估计标准差和时间中心,并利用包络主瓣峰值的相位信息对调频率与初始频率估计值进行修正,提高参数估计精度。给出了单分量和多分量情况下的Chirplet参数估计流程。推导了一般高斯环境下Chirplet信号参数估计的CRB界。仿真及实测数据处理结果验证了算法的有效性。

The adaptive Chirplet decomposition contains a lot of unknown parameters and the decomposition methods are timeconsuming. Motivated by this, a method for adaptive Chirplet signal decomposition based on envelope fitting is proposed. The chirp rate is estimated by integrating the quadratic phase function as time. The time center of the energy concentration and the spread of the pulse is estimated by curve fitting of the signal envelope. The estimating precision of estimated chirp rate and original frequency is improved by using the phase value around the main lobe of envelope to revise them. The procedures for single-component and multi-eomponents Chirplet decomposition are presented. Also, the CRB of parameter estimation for Chirplet under normal gauss circumstance is deduced. Simulation and experimental results demonstrate the validity of the proposed method.