利用相位差变化率的机载无源定位跟踪改进算法

为解决基于空频域信息的机载无源定位中高精度角速度参数难以获取的问题,论文利用相位差变化率对角速度的放大作用,通过2维相位干涉仪布局建立了一种新的无源定位观测模型,实现了对空中运动目标的定位。针对UKF(Unscented Kalman Filter)滤波中误差协方差矩阵负定导致滤波不稳定的问题,引入平方根UKF滤波算法并进行改进,采用简化球形分布的SSUT(Simplex Spherical distribution Unscented Transform)变换进行Sigma点采样,通过减少采样点个数减小了滤波的计算量。仿真表明:在较低的参数测量精度条件下,新观测模型位置和速度跟踪误差降低,采用改进的平方根UKF算法能够在保证稳定跟踪的同时,提高算法效率。

An Improved Airborne Passive Location&Tracking Algorithm Using the Rate-of-change of Phase Difference

In order to solve the problem that it is difficult to acquire high precision angular velocity parameter in airborne passive location based on spatial-frequency domain information, this paper utilizes phase difference rate-of-change’s augmentation to angular velocity, and constructs a new passive location measure model by two dimensions interferometer layout. The passive location of air target is realized. For the negative error covariance matrix possibly induces instability in the Unscented Kalman Filter (UKF), the square root UKF is introduced and improved. The Simplex Spherical distribution Unscented Transform (SSUT) is used to sigma sampling. The calculated amount of filtering is reduced by cutting down the number of sampling point. The simulation results show that the position and velocity tracking error of the new measure model decrease under the lower condition of parameters measurement. When the stable tracking effect can be reached, the algorithm efficiency can be enhanced by adopting improved square root UKF.