毫米波/红外多传感器融合跟踪算法研究

毫米波/红外(MMW/IR)传感器是各国发展多模复合制导技术的重点.针对平方根无迹卡尔曼滤波(SR-UKF)的估计算法存在线性化误差及粒子滤波中得到优化的重要性密度函数比较困难的问题，将平方根无迹卡尔曼滤波与粒子滤波相结合，提出一种序贯融合的平方根无迹卡尔曼粒子滤波(SRUKPF)算法.利用平方根无迹卡尔曼算法得到的状态更新矩阵和误差协方差矩阵，构造粒子滤波的重要性密度函数，这样重要性密度函数能够融入最新观测信息，进而更加符合真实状态的后验概率分布.为验证算法的有效性，以地空导弹中MMW/IR传感器复合制导为背景进行仿真研究与分析，结果表明，该算法克服了粒子滤波法难以得到优化重要性密度函数的缺陷，能有效提高多传感器系统状态估计的精度

The Research Maneuvering Tracking Algorithm for MMW/IR Multi-sensor Fusion

Millimeter wave(MMW)/infrared (IR) sensor is a key technology for composite guidance system of missiles. Aimming to solve the problems that there were linear errors in the algorithm of square root unscented Kalman filter (SR UKF) and it was difficult to obtain the importance density function for the algorithm of particle filter(PF), a square root unscented Kalman particle filter (SR UK PF) algorithm with the sequential fusion was presented by combining SR UKF with PF. The main idea of this algorithm was to calculate the state transition matrix and the error covariance matrix by SR UKF, and to construct the importance density function by the sequential fusion of particle filter. Thus, the importance density function could integrate the latest observation into system state transition density, and the proposal distribution could be more in line with the distribution of real states. To demonstrate the effectiveness of this model, simulations were carried out based on tracking algorithm for the surface to air missile with MMW/IR sensor.The results show that this technique can overcome the flaw that it is hard to get the optimization importance density function in the particle filter, and it can significantly improve the accuracy of state estimation for the system with multi sensors.