Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

2013 ◽  
Vol 753-755 ◽  
pp. 2117-2120 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Data Fusion ◽  
Nonlinear Filtering ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model

2017 ◽  
Vol 32 (04) ◽  
pp. 1850010
Author(s):  
Yi Pan ◽  
Hui Ye ◽  
Keke He
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
System Parameter ◽  
Unscented Kalman Filter ◽  
Continuous System ◽  
The State ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Real Time System ◽  
Spherical Simplex

A modified interacting multiple model (IMM) method called spherical simplex unscented Kalman filter-based jumping and static IMM (SSUKF-JSIMM) is proposed to solve the problem of nonlinear filtering with unknown continuous system parameter. SSUKF-JSIMM regards the continuous system parameter space as a union of disjoint regions, and each region is assigned to a model. For each model, under the assumption that the parameter belongs to the corresponding region, one sub-filter is used to estimate the parameter and the state when the parameter is presumed to be jumping, and another sub-filter is used to estimate the parameter and the state when the parameter is presumed to be static. Considering that spherical simplex unscented Kalman filter (SSUKF) is more suitable for a real-time system than the unscented Kalman filter (UKF), SSUKFs are adopted as the sub-filters of SSUKF-JSIMM. Results of the two SSUKFs are fused as the estimation output of the model. Experimental results show that SSUKF-JSIMM achieves higher performance than IMM, SIR, and UKF in bearings-only tracking problem.

scholarly journals An IMM-GLR Approach for Marine Gas Turbine Gas Path Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qingcai Yang ◽  
Shuying Li ◽  
Yunpeng Cao
Keyword(s):  
Gas Turbine ◽  
Estimation Algorithm ◽  
Health Condition ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Multiple Faults ◽  
Fault Severity ◽  
Model Set ◽  
Gas Path Fault ◽  
Update Strategy

An IMM-GLR approach based on interacting multiple model (IMM) and generalized likelihood ratio (GLR) estimation was developed to detect, isolate, and estimate gas turbine gas path fault (including abrupt fault and multiple faults) in the underdetermine estimation conditions. In this approach, a model set representing gas turbine health condition and different fault condition was established, and a corresponding bank of filters was designed. An IMM-based FDI algorithm based on these filters is applied to detect and isolate fault, and a GLR estimation algorithm is used to estimate the fault severity. Then a model set update strategy based on the diagnosed fault was proposed to enable the diagnosis of multiple faults. Several simulation case studies on a marine gas turbine were conducted, and the results show that the IMM-GLR approach not only accurately diagnoses the abrupt gas path fault and multiple gas path faults but also accurately estimates the severity of the detected fault in the underdetermine estimation conditions.

Maneuvering Car Tracking Using the Interacting Multiple Model and the Dynamic 2nd-Order SVSF Method

2015 ◽  
Author(s):  
Mina Attari ◽  
Hamed Hossein Afshari ◽  
Saeid Habibi
Keyword(s):  
State Estimation ◽  
Sliding Mode ◽  
Estimation Method ◽  
Variable Structure ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Structure Control ◽  
Position Data ◽  
Car Driving ◽  
Tracking Strategy

Car tracking algorithms have recently found a major role in intelligent automotive applications. They are mainly based on the state estimation techniques to solve the maneuvering car tracking problems. The dynamic 2nd-order SVSF method is a novel robust state estimation method that is based on the variable structure control theory. It benefits from the accuracy, robustness, and chattering suppression properties of second-order sliding mode systems for robust state estimation. The main contribution of this paper is to present and implement a new tracking strategy that is a combination of the dynamic 2nd-order SVSF with the IMM filter. It benefits from the robust performance of the dynamic 2nd-order SVSF and the switching property of the IMM filter. This strategy is simulated and examined under several car driving patterns and experimental position data that are captured by a GPS device. The robustness and efficiency of this strategy is then compared with the Kalman filter-based counterparts.

scholarly journals Roll Angle Estimation Algorithm of Geomagnetic/Gyro Combination Based on an Interacting Multiple-Model Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian-li Su ◽  
Hua Wang
Keyword(s):  
Kalman Filter ◽  
Measurement Accuracy ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Roll Angle ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Calculation Results ◽  
Gyroscope Sensor ◽  
And Control

The knowledge of the geomagnetic and gyro information that can be used for projectile roll angle is decisive to apply trajectory correction and control law. In order to improve the measurement accuracy of projectile roll angle, an interacting multiple-model Kalman filter (IMMKF) algorithm using gyro angular rate information to geomagnetic sensor information is proposed. Firstly, the data acquisition module of the geomagnetic sensor and the gyroscope sensor is designed, and the test data of the sensors are obtained through the semiphysical experiments. Furthermore, according to the measurement accuracy of each sensor, the algorithm performs the IMMKF process on the geomagnetic/gyro information to get the roll angle. It can be proven by experiments and calculation results that the error of the roll angle obtained after processing by the IMMKF algorithm is close to 2°, which is better than the 5° calculated by adopting the Kalman filter directly with geomagnetic information.

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