Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion

Tseng, Chien-Hao; Chang, Chih‐Wen; Jwo, Dah‐Jing

doi:10.3390/s110202090

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…The overall estimates is obtained by a combination of the estimates from the filters running in parallel based on the individual models that match the system modes. In each cycle, four major steps are involved: interaction, filtering, mode probability calculation, and combination [15].…”

Section: The Interacting Multiple Model Unscented Kalman Filtermentioning

confidence: 99%

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Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

2019

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A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

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Section: The Interacting Multiple Model Unscented Kalman Filtermentioning

confidence: 99%

“…A fuzzy logic adaptive system (FLAS) [9,15] is added to the UKF innovation term to compensate for the non-Gaussianity of the whole system. The fuzzy logic reasoning system is based on the Mamdani type model.…”

Section: Introductionmentioning

confidence: 99%

Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

2019

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“…Attempts to improve the accuracy of the filtering have also been made using adaptive approaches. In some studies, values for the state and measurement covariance matrices were updated based on the innovation [2] and recently fuzzy logic was used for this task [18,33]. Another approach for fusing accelerometer and gyroscope for attitude estimation is also based on fuzzy rules [21] in order to decide which of the accelerometer or the gyroscope will be given weight for estimation based on observations from these sensors such as whether a mobile robot is rotating or not.…”

Section: Related Workmentioning

confidence: 99%

Sensor fusion of camera, GPS and IMU using fuzzy adaptive multiple motion models

et al. 2017

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A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay; occurring because of the delay in estimating the motion of the user and displaying images based on this estimate. This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS-IMU fusion approach due to additional estimates from a cam-

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“…IMM algorithms have already been applied to vehicle localization, and are usually used to represent the possible vehicle driving patterns with a set of models, which are generally established according to different maneuvering or driving conditions [21,22,23]. The IMM algorithm has shown better results than conventional switching schemes, because a smooth transition from one model to another is achieved [24]. Different from the common applications, we envisioned that the IMM algorithm can provide a soft switching among the filters designed for different noise levels and contribute to adapt to the uncertain noise of MEMS inertial sensors.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

Liu

Chan

2017

Sensors

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In this paper, we propose a cost-effective localization solution for land vehicles, which can simultaneously adapt to the uncertain noise of inertial sensors and bridge Global Positioning System (GPS) outages. First, three Unscented Kalman filters (UKFs) with different noise covariances are introduced into the framework of Interacting Multiple Model (IMM) algorithm to form the proposed IMM-based UKF, termed as IMM-UKF. The IMM algorithm can provide a soft switching among the three UKFs and therefore adapt to different noise characteristics. Further, two IMM-UKFs are executed in parallel when GPS is available. One fuses the information of low-cost GPS, in-vehicle sensors, and micro electromechanical system (MEMS)-based reduced inertial sensor systems (RISS), while the other fuses only in-vehicle sensors and MEMS-RISS. The differences between the state vectors of the two IMM-UKFs are considered as training data of a Grey Neural Network (GNN) module, which is known for its high prediction accuracy with a limited amount of samples. The GNN module can predict and compensate position errors when GPS signals are blocked. To verify the feasibility and effectiveness of the proposed solution, road-test experiments with various driving scenarios were performed. The experimental results indicate that the proposed solution outperforms all the compared methods.

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Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion

Cited by 20 publications

References 16 publications

Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

Sensor fusion of camera, GPS and IMU using fuzzy adaptive multiple motion models

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

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