一种自适应滤波与干扰观测器相结合的大型舰船状态估计算法

doi:10.12382/bgxb.2023.0588

摘要/Abstract

摘要：

为满足航母等大型舰船目标的状态估计要求,提出一种由非线性干扰观测器和强跟踪容积卡尔曼滤波算法融合形成的交互多模型强补偿容积卡尔曼滤波算法。引入非线性干扰观测器,完成由外界不确定因素引起的干扰总量的估计,并对观测器稳定性进行证明。使用估计的干扰值实时修正强跟踪容积卡尔曼滤波的过程参数,最终形成交互多模型强补偿容积卡尔曼滤波算法,完成对目标状态相对准确的估计。研究结果表明:新提出的滤波算法能够较为准确地完成对目标状态的估计,与变结构多模型粒子滤波算法、变结构多模型无迹卡尔曼滤波算法和交互多模型强跟踪容积卡尔曼滤波算法相比,在目标位置和速度估计上具有更高的估计精度。

关键词: 舰船, 目标状态估计, 交互多模型强补偿容积卡尔曼滤波, 自适应滤波算法, 干扰观测器

Abstract:

In order to meet the state estimation requirements of large ship targets such as aircraft carriers, an interactive multi-model strong compensating cubature Kalman filtering algorithm is proposed,which is formed by the fusion of nonlinear disturbance observer and strong tracking cubature Kalman filtering algorithm. The nonlinear disturbance observer is introduced to estimate the total amount of disturbance caused by external uncertainties and prove the stability of the observer, and then the estimated disturbance value is used to modify the process parameters of the strong tracking cubature Kalman filter in real time, which finally forms the interactive multi-model strong compensating cubature Kalman filtering algorithm and completes the relatively accurate estimation of target state. The results show that the proposed filtering algorithm can complete the more accurate estimation of target state, and has higher estimation accuracy in the estimation of target position and velocity compared with the variable-structure multi-model particle filtering algorithm, the variable-structure multi-model unscented Kalman filtering algorithm, and the interactive multi-model strong tracking cubature Kalman filtering algorithm.

Key words: ships, target state estimation, interactive multi-model strong compensating cubature Kalman filter, adaptive filtering algorithm, disturbance observer

中图分类号:

TN713

王泳安, 李东光, 吴浩, 刘洋. 一种自适应滤波与干扰观测器相结合的大型舰船状态估计算法[J]. 兵工学报, 2024, 45(7): 2318-2328.

WANG Yong’an, LI Dongguang, WU Hao, LIU Yang. An Adaptive Filtering-disturbance Observer-based State Estimation Algorithm for Large Ships[J]. Acta Armamentarii, 2024, 45(7): 2318-2328.

图/表 15

图1 大型舰船参考坐标系

Fig.1 Reference coordinate system for large ships

图2 单次仿真模型概率

Fig.2 Single simulation model probability

图3 50次蒙特卡洛仿真平均模型概率

Fig.3 Average of model probabilities after 50 Monte Carlo simulations

图4 干扰值估计

Fig.4 Estimation of disturbance values

表1 对非线性干扰项的估计误差

Table 1 Estimation error of nonlinear disturbance term

方向	最大估计误差/N	平均估计误差/N
X₀轴	8.25×10⁵	6.47×10⁴
Y₀轴	5.20×10⁵	8.80×10³

图5 目标运动轨迹及各算法滤波后轨迹

Fig.5 Target motion trajectory and the trajectory filtered by each algorithm

图6 X0轴方向目标速度估计

Fig.6 Target velocity estimation in X0-axis direction

图7 Y0轴方向目标速度估计

Fig.7 Target velocity estimation in Y0-axis direction

图8 X0轴方向目标加速度估计

Fig.8 Target acceleration estimation in X0-axis direction

图9 Y0轴方向目标加速度估计

Fig.9 Target acceleration estimation in Y0-axis direction

表2 各算法对目标位置估计情况

Table 2 Estimation of target position by algorithms

算法	最大误差值/m	平均误差值/m
VSMM-PF	121.67	40.29
VSMM-UKF	144.55	47.52
IMM-STCKF	108.26	25.55
IMM-SCCKF	41.17	12.08

表3 各算法加速度均方根误差

Table 3 Root mean square error of acceleration for each algorithm

算法	X₀轴加速度均方根误差均值/ (m·s^-2)	Y₀轴加速度均方根误差均值/ (m·s^-2)
VSMM-PF	4.40	0.62
VSMM-UKF	3.72	0.80
IMM-STCKF	1.04	0.68
IMM-SCCKF	1.17	0.70

图10 位置均方根误差

Fig.10 Root mean square error of position

表4 各算法均方根误差

Table 4 Root mean square error of each algorithm

算法	位置均方根误差均值/m	速度均方根误差均值/(m·s^-1)
VSMM-PF	40.29	2.85
VSMM-UKF	47.76	3.62
IMM-STCKF	25.69	2.24
IMM-SCCKF	13.35	1.52

图11 速度均方根误差

Fig.11 Root mean square error of velocity

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