IMM-UKF based land-vehicle navigation with low-cost GPS/INS

Qian, Huaming; An, Di; Xia, Quanxi

doi:10.1109/icinfa.2010.5512039

Cited by 19 publications

(11 citation statements)

References 5 publications

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“…The IMM algorithm was first widely applied in the target tracking area. To better predict the position of the target, the constant location (CL), constant velocity (CV), constant acceleration (CA) and constant turn rate (CT) models are fused by IMM to cover all of the possible motions [ 20 , 21 ]. In recent years, IMM has become popular in high-precision INS/GPS-integrated navigation systems.…”

Section: Introductionmentioning

confidence: 99%

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System

Yao

2017

Sensors

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Inertial navigation system (INS)/Doppler velocity log (DVL) integration is the most common navigation solution for underwater vehicles. Due to the complex underwater environment, the velocity information provided by DVL always contains some errors. To improve navigation accuracy, zero velocity update (ZUPT) technology is considered, which is an effective algorithm for land vehicles to mitigate the navigation error during the pure INS mode. However, in contrast to ground vehicles, the ZUPT solution cannot be used directly for underwater vehicles because of the existence of the water current. In order to leverage the strengths of the ZUPT method and the INS/DVL solution, an interactive multiple model (IMM)-aided ZUPT methodology for the INS/DVL-integrated underwater navigation system is proposed. Both the INS/DVL and INS/ZUPT models are constructed and operated in parallel, with weights calculated according to their innovations and innovation covariance matrices. Simulations are conducted to evaluate the proposed algorithm. The results indicate that the IMM-aided ZUPT solution outperforms both the INS/DVL solution and the INS/ZUPT solution in the underwater environment, which can properly distinguish between the ZUPT and non-ZUPT conditions. In addition, during DVL outage, the effectiveness of the proposed algorithm is also verified.

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Section: Introductionmentioning

confidence: 99%

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System

Yao

2017

Sensors

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“…Very often in applications, the continuous dynamics of each mode can be modelled as a linear system, leading to the stochastic linear hybrid system (SLHS). SLHSs and extensions have been used in diverse applications such as communication networks [6], and drug diffusion in biological systems [7], but its primary application is in the area of navigation [8] and tracking [9–11] in which the discrete states correspond to different modes of vehicle motion and the stochastic nature of the model is used to describe unknown vehicle behaviour.…”

Section: Introductionmentioning

confidence: 99%

State estimation and fault detection and identification for constrained stochastic linear hybrid systems

Mann¹,

Hwang²

2013

IET Control Theory & Applications

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“…The estimation accuracy of IMM is better than the single model filter (EKF/UKF). It was found that the estimate produced by IMM-UKF was better than the single model EKF, UKF (Qian et al, 2010) and IMM-EKF.…”

Section: Introductionmentioning

confidence: 99%

“…As the name implies, multiple models can be utilized simultaneously (Honghui, 2002;Rong and Bar-Shalom, 1993b;Rong and Vesselin, 2003). To incorporate soft switching between different motion models, the IMM-UKF is introduced (Qian et al, 2010). k−1 ) to the appropriate manoeuvring models.…”

Section: E S T I M Atmentioning

confidence: 99%

IMM-UKF-TFS Model-based Approach for Intelligent Navigation

et al. 2013

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This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

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IMM-UKF based land-vehicle navigation with low-cost GPS/INS

Cited by 19 publications

References 5 publications

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System

State estimation and fault detection and identification for constrained stochastic linear hybrid systems

IMM-UKF-TFS Model-based Approach for Intelligent Navigation

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