Non linear Optimum Interacting Multiple Model Smoother for GPS Navigation System

This paper addresses the problem of designing a fault identification and detection algorithm for non-linear systems. Timely identification and detection of a fault in a system is crucial in condition monitoring systems. However, finding the source of the failure is not trivial in systems with large numbers of components and complex component relationships. In this paper, an efficient scheme to detect adverse changes in system reliability and find the failed component is proposed, based on the interacting multiple model (IMM) algorithm, with fault detection and diagnosis formulated as a hybrid multiple model estimation scheme. The proposed approach provides an integrated framework for fault detection, diagnosis and state estimation. Its performance is illustrated for fault detection of a non-linear two-tank system. The proposed method can be used with different kinds of filters, using the confusion matrix and classification accuracy as comparison metrics. A particle filter is used with the IMM algorithm and its performance is compared to the linear Kalman filter as a comparative case concerning the improvement that can be achieved when going beyond the consideration that the system is linear.

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Interacting Multiple Model Navigation System for Quadrotor Micro Aerial Vehicles Subject to Rotor Drag

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9340796 ◽

2020 ◽

Author(s):

Mahmoud A.K. Gomaa ◽

Oscar De Silva ◽

George K.I. Mann ◽

Raymond G. Gosine

Keyword(s):

Navigation System ◽

Interacting Multiple Model ◽

Micro Aerial Vehicles ◽

Multiple Model ◽

Aerial Vehicles

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Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System

Applied Sciences ◽

10.3390/app8091682 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1682 ◽

Cited By ~ 5

Author(s):

Chuang Zhang ◽

Chen Guo ◽

Daheng Zhang

Keyword(s):

Navigation System ◽

Integrated System ◽

Filter Method ◽

Integration Scheme ◽

Interacting Multiple Model ◽

Integrated Navigation ◽

Multiple Model ◽

Integrated Navigation System ◽

Comparison Results ◽

On Line

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

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Robust Interacting Multiple Model Unscented Particle Filter for Navigation

Mathematical Problems in Engineering ◽

10.1155/2020/8871358 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Li Xue ◽

Yulan Han ◽

Chunning Na

Keyword(s):

Particle Filter ◽

Navigation System ◽

Measurement Information ◽

Interacting Multiple Model ◽

Integrated Navigation ◽

Multiple Model ◽

Integrated Navigation System ◽

Filter Performance ◽

Unscented Particle Filter ◽

Particle Filter Algorithm

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

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