An Adaptive Filtering-disturbance Observer-based State Estimation Algorithm for Large Ships

doi:10.12382/bgxb.2023.0588

Abstract

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

CLC Number:

TN713

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.

Figures/Tables 15

Fig.1 Reference coordinate system for large ships

Fig.2 Single simulation model probability

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

Fig.4 Estimation of disturbance values

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

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

Fig.6 Target velocity estimation in X0-axis direction

Fig.7 Target velocity estimation in Y0-axis direction

Fig.8 Target acceleration estimation in X0-axis direction

Fig.9 Target acceleration estimation in Y0-axis direction

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

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

Fig.10 Root mean square error of position

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

Fig.11 Root mean square error of velocity

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