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本文应用Kalman 滤波技术,研究了一套适用于航空火力控制系统的机动目标状态估值算法。文中将目标加速度模型假设为既有依赖状态的系数,又能自动适应目标机动的二阶高斯——马尔柯夫过程。采用非线性滤波理论,对非线性耦合的目标相对运动模型没有强行解耦和线性化,而是直接进行滤波。最后就估值精度滤波收敛速度和目标机动变化的适应性等方面进行了大量的数字仿真。仿真结果表明本文所设计的非线性滤波器具有良好的性能。
In this paper, Kalman filter technology is applied to study a maneuvering target state estimation algorithm suitable for aviation fire control system. In this paper, the target acceleration model is assumed to be the coefficient of both the dependent state and the second-order Gaussian-Markov process that can automatically adapt to the target maneuver. Adopting the nonlinear filter theory, the target relative motion model of nonlinear coupling is not forcibly decoupled and linearized, but filtered directly. Finally, a large number of digital simulations are carried out in terms of the convergence speed of the estimation accuracy filter and the adaptability of the target maneuver. Simulation results show that the nonlinear filter designed in this paper has good performance.