scholarly journals Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM

2021 ◽  
Vol 11 (11) ◽  
pp. 4908
Author(s):  
Yanxu Liu ◽  
Zhongliang Deng ◽  
Enwen Hu
Keyword(s):  
Sensor Fusion ◽  
Estimation Error ◽  
Error Variance ◽  
Multiple Models ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Motion Model ◽  
Positioning Accuracy ◽  
Covariance Intersection ◽  
Positioning Method

For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.

scholarly journals Models and Algorithms for Tracking Target with Coordinated Turn Motion

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xianghui Yuan ◽  
Feng Lian ◽  
Chongzhao Han
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Multiple Models ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Motion Model ◽  
Single Model ◽  
Kinematic Constraint ◽  
The Em Algorithm ◽  
Turn Rate

Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

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

Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 2090-2111 ◽  
Author(s):  
Chien-Hao Tseng ◽  
Chih-Wen Chang ◽  
Dah-Jing Jwo
Keyword(s):  
Sensor Fusion ◽  
Nonlinear Filter ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Fuzzy Adaptive

scholarly journals Adaptive filtering algorithms in target tracking applications

2003 ◽  
Vol 16 (3) ◽  
pp. 317-326
Author(s):  
Igor Jovandic ◽  
Zeljko Djurovic ◽  
Branko Kovacevic
Keyword(s):  
Target Tracking ◽  
Adaptive Filtering ◽  
Noise Level ◽  
Estimation Error ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Error Criterion ◽  
Filtering Algorithms ◽  
Target Trajectory ◽  
Simulation Results

Comparison of several target tracking algorithms is presented. Namely discrete noise level adjustment (DNLA), variable state dimension (VSD) and interacting multiple model (IMM) algorithms are discussed. Target trajectory, target models, filtering algorithms and simulation results are given. The cumulative estimation error criterion is used in order to compare the algorithms.

scholarly journals Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

Sensors ◽  
2013 ◽  
Vol 13 (4) ◽  
pp. 4122-4137 ◽  
Author(s):  
Taehwan Cho ◽  
Changho Lee ◽  
Sangbang Choi
Keyword(s):  
Sensor Fusion ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Position Accuracy ◽  
Model Filter

Tracking Moving Target on Airport Surface Based on Variable-Structure IMM Algorithm

2012 ◽  
Vol 459 ◽  
pp. 603-608 ◽  
Author(s):  
Shu Li Gong ◽  
Xu Hui Wang ◽  
Sheng Guo Huang
Keyword(s):  
Variable Structure ◽  
Moving Target ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Motion Model ◽  
Constant Acceleration ◽  
Surface Movement ◽  
Actual Movement ◽  
Velocity Motion ◽  
Model Algorithm

In order to track the moving target precisely on the airport surface, the Variable Structure Interacting Multiple Model Algorithm (VS-IMM) is applied to realize the Surface Movement Radar (SMR) tracking of target. Firstly, according to the actual movement of aircraft, the paper establishes a constant acceleration motion model, constant turn motion model and constant velocity motion model. Then, the VS-IMM algorithm is applied to the airport surface movement target tracking. Finally, VS-IMM is compared with IMM. Simulation results show that VS-IMM algorithm is more reliable than IMM algorithm on tracking of moving target on airport surface

Robust interacting multiple model algorithms based on multi-sensor fusion criteria

2015 ◽  
Vol 47 (1) ◽  
pp. 92-106 ◽  
Author(s):  
Weidong Zhou ◽  
Mengmeng Liu
Keyword(s):  
Sensor Fusion ◽  
Interacting Multiple Model ◽  
Multiple Model

scholarly journals Fault Detection and Control of Process Systems

2007 ◽  
Vol 2007 ◽  
pp. 1-20 ◽  
Author(s):  
Vu Trieu Minh ◽  
Nitin Afzulpurkar ◽  
W. M. Wan Muhamad
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Predictive Control ◽  
Detection System ◽  
Control Process ◽  
Multiple Models ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Process Systems ◽  
And Control

This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

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