论文部分内容阅读
首先建立运动单站被动测距的数学模型,然后分析被动测距的可观测性,并针对实际应用提出了相应的滤波估计方法。可观测性问题分析了具有不同运动特征的目标在仅有方位角和俯仰角测量时能计算位置坐标的充分条件,将被动测距归结为典型的间接测量问题。由于直接测量的方位角和俯仰角与位置坐标的关系构成非线性测量方程,利用离散状态空间模型的分析方法,将被动测距描述为非线性状态估计问题。推广卡尔曼滤波、粒子滤波是求解被动测距非线性状态估计的常用方法。模拟了实际的被动测距模型,并用推广卡尔曼滤波和粒子滤波方法估计目标的位置坐标序列。模拟实验表明:这两种方法在运动单站被动测距中是有效的。
Firstly, the mathematical model of passive ranging of single-station sports is established. Then the observability of passive ranging is analyzed, and the corresponding filtering estimation method is put forward for practical application. Observability Problem The sufficient conditions for calculating the position coordinates of a target with different motion characteristics in azimuth and elevation measurement are analyzed. Passive ranging is analyzed as a typical indirect measurement problem. Because of the direct measurement of azimuth and elevation angle and the relationship between the position coordinates constitute a nonlinear measurement equation, the use of discrete state space model analysis method, the passive ranging described as a nonlinear state estimation problem. Popularizing Kalman filter, particle filter is a common method to solve the nonlinear state estimation of passive ranging. The actual passive ranging model is simulated and the location coordinate sequence of the target is estimated by using the extended Kalman filter and particle filter. Simulation results show that these two methods are effective in passive single-station ranging.