A filtering approach based on MMAE for a SINS CNS integrated navigation system

Zhao, Fangfang; Chen, Cuiqiao; He, Wei; Ge, Shuzhi Sam

doi:10.1109/jas.2017.7510445

Cited by 22 publications

(5 citation statements)

References 14 publications

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“…It approximates the probability distribution of states by n + 2 ( n is the state dimension) spherical sampling points, which are composed of n + 1 sampling points with equal weight and the mean of states. Compared with the UKF based on unscented transformation method with 2 n + 1 symmetric points [27], the SSUKF algorithm can enhance the performance of UKF algorithm and reduce the computational amount on the basis of guaranteeing the accuracy. Details of the SSUKF algorithm can be found in Ref.…”

Section: Proposed Methodologymentioning

confidence: 99%

Adaptive particle filter for state estimation with application to non‐linear system

Zhao¹,

Cai²

2022

IET Signal Processing

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Particle filtering (PF) has certain application value, but the disadvantage is that there is a phenomenon of particle degradation. In order to reduce the impact of this problem, this paper presents a new adaptive PF approach to improve the estimate accuracy. From the perspective of selecting an appropriate important density functions, in this filter, the particles are first updated using the Spherical Simplex Unscented Kalman Filter algorithm, and then the particles are updated using the Adaptive Extended Kalman filter algorithm. Simultaneously, from the perspective of improving the resampling method, a new resampling technique based on the random resampling method has been designed and fused to this filter. The comparison and analysis of two simulation schemes have been conducted to assess the performance of the designed filtering algorithm. The simulation results show the effectiveness of the proposed approach.

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Section: Proposed Methodologymentioning

confidence: 99%

Adaptive particle filter for state estimation with application to non‐linear system

Zhao¹,

Cai²

2022

IET Signal Processing

Self Cite

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“…However, a notable drawback of INS is the rapid accumulation of errors over time, making it challenging to rely on for prolonged and highly precise navigation [1][2][3][4][5]. Typically, supplementary navigation devices such as the celestial navigation system (CNS) [6][7][8][9][10] and scene-matching navigation (SMN) [11][12][13][14][15][16][17] are employed to assist INS in forming an integrated navigation system that aligns with the demands of long-term, high-precision navigation.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

Tian,

Cheng,

Yao

et al. 2024

Remote Sensing

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In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

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“…In terms of information fusion, the Kalman Filter (KF) provided good results for linear approximation. Also, the extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) provided good localization results for complex and non-linear features [ 11 , 12 ]. The multi-model adaptive estimation method (MMAE) has been known to play a very important role in multi-model adaptive control, mainly because it is highly suitable for predicting the values of uncertain parameters or variables [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation

Wong,

Feng,

Kuo

2023

Sensors

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In this study, we designed a multi-sensor fusion technique based on deep reinforcement learning (DRL) mechanisms and multi-model adaptive estimation (MMAE) for simultaneous localization and mapping (SLAM). The LiDAR-based point-to-line iterative closest point (PLICP) and RGB-D camera-based ORBSLAM2 methods were utilized to estimate the localization of mobile robots. The residual value anomaly detection was combined with the Proximal Policy Optimization (PPO)-based DRL model to accomplish the optimal adjustment of weights among different localization algorithms. Two kinds of indoor simulation environments were established by using the Gazebo simulator to validate the multi-model adaptive estimation localization performance, which is used in this paper. The experimental results of the proposed method in this study confirmed that it can effectively fuse the localization information from multiple sensors and enable mobile robots to obtain higher localization accuracy than the traditional PLICP and ORBSLAM2. It was also found that the proposed method increases the localization stability of mobile robots in complex environments.

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A filtering approach based on MMAE for a SINS CNS integrated navigation system

Cited by 22 publications

References 14 publications

Adaptive particle filter for state estimation with application to non‐linear system

Adaptive particle filter for state estimation with application to non‐linear system

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation

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