基于APSO-SSUKF的光学成像微纳卫星单角度目标跟踪方法

Bing Hua, Guang Yang, Yunhua Wu, Zhiming Chen
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引用次数: 1

摘要

为了保证空间站的安全,提高非合作目标估计轨迹跟踪的精度,提出了一种基于自适应粒子群优化-球面单纯形无迹卡尔曼滤波(APSO-SSUKF)的光学成像微纳卫星,对低轨道目标进行单角度跟踪。首先,该算法考虑了J2摄动的影响,采用纯角度数据作为观测向量,采用球面单纯形无气味卡尔曼滤波(SSUKF)减少了空间非合作目标跟踪中UKF的计算量;其次,提出利用创新序列的实际协方差和理论协方差对测量噪声进行实时估计,设计自适应粒子群优化(APSO)算法对SSUKF中的过程噪声进行实时跟踪,提高了滤波器在纯角度跟踪中的精度。最后,利用光学成像微/纳米卫星对低轨卫星进行跟踪仿真,结果表明:与UKF、SSUKF和PSO-SSUKF相比,APSO-SSUKF在空间目标跟踪中预测位置的均方根误差分别降低了45.44%、35.26%和20.94%,APSO-SSUKF在速度预测方面的均方根误差分别降低了45.58%、33.53%和16.33%;在角度跟踪目标中,APSO-SSUKF提高了算法的收敛性和估计精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Angle-Only Target Tracking Method for Optical Imaging Micro-/Nanosatellite Based on APSO-SSUKF
To ensure the safety of the space station and improve the accuracy of the estimated trajectory tracking of noncooperative target, an optical imaging micro-/nanosatellite based on APSO-SSUKF (adaptive particle swarm optimization-spherical simplex unscented Kalman filter) is proposed to track low-orbit target using angle-only measurement. First, the algorithm considers the effect of J2 perturbation, uses the angle-only data as the observation vector, and uses spherical simplex unscented Kalman filter (SSUKF) to reduce the cost of calculation of the UKF in space noncooperative target tracking. Secondly, it is proposed to use the actual and theoretical covariance of the innovation sequence for real-time estimation of measurement noise, designing the adaptive particle swarm optimization (APSO) algorithm for real-time tracking of the process noise in the SSUKF that improves the accuracy of the filter in angle-only tracking. Finally, the tracking simulation of low-orbit satellite is carried out by using optical imaging micro-/nanosatellite, and the result shows that, compared with UKF, SSUKF, and PSO-SSUKF, APSO-SSUKF reduces the root mean square of the error in predicting the position in space target tracking by 45.44%, 35.26%, and 20.94%, and APSO-SSUKF reduces the root mean square of the error in velocity by 45.58%, 33.53%, and 16.33%, respectively; in the angle-tracking target, APSO-SSUKF improves the convergence and estimated accuracy of the algorithm in tracking.
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