基于似然函数的双曲调频信号参数估计快速算法

相较于线性调频(LFM)信号，双曲调频(HFM)信号因具有良好的脉冲压缩性能和多普勒不变性，被广泛用于雷达侦查、水声探测等多普勒影响严重的场景中，其中HFM信号的参数估计问题尤为重要。有鉴于此，该文提出一种基于似然函数的HFM信号参数估计快速算法。文中首先推导出HFM信号的Cramer-Rao下界作为参数估计的性能评估标准；然后基于高斯随机噪声，构建了HFM信号的似然函数，并结合数据向量化的特点提出一种改进的适应度函数，最后利用全局最优引导人工蜂群(GABC)算法对该适应度函数进行极值寻优，从而实现HFM信号的参数估计；通过蒙特卡洛仿真证明了该方法在信噪比为3 dB以上时，HFM信号的参数估计结果的均方误差更逼近Cramer-Rao下界，且运算量约是原来的1/3，在保证估计精度的同时提高算法收敛速度。

Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function

Compared with Linear Frequency Modulation (LFM) signals, Hyperbolic Frequency Modulation (HFM) signals, which have good performance of the pulse compression and the Doppler invariance, are widely used in scenes with severe Doppler effects such as radar detection and underwater acoustic detection, and among them, the parameter estimation problem of HFM signals is particularly important. In view of this, this paper proposes a Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function. Firstly, the Cramer-Rao lower bound of the HFM signal is derived as the performance evaluation standard for parameter estimation; Then based on the Gaussian random noise, the likelihood function of the HFM signal is constructed, and an improved fitness function is proposed in combination with the characteristics of data vectorization, then the Global best guided Artificial Bee Colony (GABC) algorithm is used to optimize the fitness function to realize the parameter estimation of the HFM signal. Finally, Monte Carlo simulation results show that, compared to the method before the improvement, the mean square error of the parameter estimation result of the HFM signal is closer to the Cramer-Rao lower bound when the signal-to-noise ratio is more the 3 dB, and the amount of calculation is about one-third of the method before improvement, which improves the algorithm convergence speed while ensuring the estimation accuracy.