基于EMD-PSO-UKF的飞机升降速度算法研究

Research on Algorithm of Aircrafts Vertical Velocity Based on EMD-PSO-UKF

中文关键词:  升降速度  无迹卡尔曼滤波  经验模态分解  粒子群优化算法

英文关键词:vertical velocity  UKF  EMD  PSO

中文摘要:

      某型飞机升降速度计算方法存在信号不稳定、噪声过大、滤波系数控制难等问题。为解决此问题，基于无迹卡尔曼滤波（Unscented Kalman Filter,UKF）、经验模态分解（Empirical Mode Decomposition,EMD）和粒子群优化（Particle Swarm Optimization,PSO）算法，提出了一种新型的飞机升降速度算法模型。该算法模型以飞机的气压高度和升降速度作为状态向量，以加速度作为随机噪声，建立UKF的状态方程和观测方程，通过EMD确定观测方程中的观测噪声协方差R，使用PSO算法计算状态方程中的系统噪声协方差Q。对不同气压高度的飞机升降速度进行仿真，并与某型飞机测试系统记录的气压高度数据所计算的升降速度进行比较。结果表明，经EMD-PSO-UKF算法模型计算的升降速度均在系统的最大允许误差范围内，验证了该算法模型计算的飞机升降速度具有较高的精度、稳定性和可靠性。

英文摘要:

      The calculation method of vertical velocity of a certain type of aircraft has some problems,including signal instability,excessive noise,and challenging filter coefficient management.As a result,an aircraft vertical velocity algorithm model is proposed based on unscented Kalman filter (UKF),empirical mode decomposition (EMD),and particle swarm optimization (PSO).The algorithm model establishes the state equation and observation equation of UKF using the air pressure height and vertical velocity of the aircraft as the state vectors,and the acceleration as the randow noise.EMD is used to compute the observation noise covariance R in the observation equation,while PSO is used to derive the system noise covariance Q in the state equation.The vertical velocity of aircraft with different pressure height is simulated and compared with the vertical velocity calculated by pressure height data that is recorded by a certain type of aircraft test system.The results show that vertical velocity calculated by the EMD-PSO-UKF algorithm model is within the maximum permitted error range,which confirms that the algorithm model for determining the vertical velocity with high accuracy,stability and reliability.