基于粒子滤波的高阶运动目标激光探测微动参数估计

Laser Micro-Motion Parameter Estimation of High-Order Moving Target Based on Improved Particle Filter

为精确估计高阶运动状态下的目标微动参数,实现目标精细识别,提出一种基于相位信息的分离估计方法。通过对解卷绕得到的真实相位进行求导,消除信号中的多项式相位信号项,实现目标运动和微动分量的分离。针对包含多维微动参数的正弦调频项,提出改进的粒子滤波静态参数估计方法,通过设计自适应方差法和变化粒子数提升了算法效率,通过设计累积残差作为观测概率密度函数,实现了对非线性模型中多维参数的同时估计。仿真和实验分析验证了算法的有效性和必要性。算法通过对相位进行处理降低了原始信号的非线性程度,减少了计算量,具有较强的抗噪性能。基于粒子滤波的微动参数估计方法减少了估计流程,避免了误差传递效应,提高了估计精度。

To precisely estimate the micro-motion parameters and realize target identification under high order motion,we propose a separation and estimation method based on phase information.The main motion and micromotion are separated by removing the polynomial phase signal part through the derivation of unwrapped echo phase.For the sinusoidal frequency modulation items with multi-dimensional micro-motion parameters after separation,an improved particle filter（PF）static parameter estimation method is proposed,and the efficiency of the algorithm is improved by designing the adaptive variance method and changing the number of particles.By designing cumulative residuals as a function of observed probability density,the simultaneous estimation of multi-dimensional parameters is realized in the nonlinear model.Simulation and experimental analysis verify the effectiveness and necessity of the proposed algorithm.The nonlinearity and the amount of calculation are reduced by processing the phase,which improves the anti-noise performance.The PF based parameter estimation method avoids the error transfer and improves the estimation accuracy effectively.