Multiple Model Kalman and Particle Filters and Applications: A Survey

Abstract Kalman Filters (KF) is a recursive estimation algorithm, a special case of Bayesian estimators under Gaussian, linear and quadratic conditions. For non-linear systems, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) provide first and higher order linearization approximations. Particle Filters (PF), on the other hand, are sequential Monte Carlo methods to provide estimations for non-linear non-Gaussian problems. For complex systems, Kalman or Particle Filter based single model filters may not be sufficient to model the system behaviour. Multiple Model (MM) Filters achieve more reliable estimates by using more than one filter with different models in parallel and the outputs of each filter are fused by assigning a probability to each filter. The most common methods used in the literature for multiple model estimation are Multiple Model Adaptive Estimation (MMAE) and Interacting Multiple Model (IMM). This paper presents an overview of the recent research on multiple model filters.

[1]  C. L. Wei,et al.  Robust multiple model adaptive estimation for spacecraft autonomous navigation , 2015 .

[2]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[3]  Shuang Li,et al.  Innovative Mars entry integrated navigation using modified multiple model adaptive estimation , 2014 .

[4]  Nasser Kehtarnavaz,et al.  An Object-Tracking Algorithm Based on Multiple-Model Particle Filtering With State Partitioning , 2009, IEEE Transactions on Instrumentation and Measurement.

[5]  Huibin Wang,et al.  Combination of Interacting Multiple Models with the Particle Filter for Three-Dimensional Target Tracking in Underwater Wireless Sensor Networks , 2012 .

[6]  Zhenliang Ma,et al.  Improved IMM Algorithm for Nonlinear Maneuvering Target Tracking , 2012 .

[7]  Inseok Hwang,et al.  State Estimation for Stochastic Linear Hybrid Systems with Continuous-State-Dependent Transitions: An IMM Approach , 2009 .

[8]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[9]  Junliang Chen,et al.  Interacting Multiple Model Particle-type Filtering Approaches to Ground Target Tracking , 2008, J. Comput..

[10]  Junping Du,et al.  New interacting multiple model algorithms for the tracking of the manoeuvring target [Brief Paper] , 2010 .

[11]  R. Vinodha,et al.  A Comparitive Study Of Kalman Filter, Extended Kalman Filter And Unscented Kalman Filter For Harmonic Analysis Of The Non-Stationary Signals , 2012 .

[12]  Wei Zhu,et al.  An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking , 2016, Sensors.

[13]  Lihua Xie,et al.  Robust Kalman filtering for uncertain discrete-time systems , 1994, IEEE Trans. Autom. Control..

[14]  Peter S. Maybeck,et al.  Multiple-model adaptive estimation using a residual correlation Kalman filter bank , 2000, IEEE Trans. Aerosp. Electron. Syst..

[15]  L. D. Liu,et al.  Robust Kalman filtering for discrete-time nonlinear systems with parameter uncertainties , 2012 .

[16]  Gonzalo Seco-Granados,et al.  Survey on Robust Carrier Tracking Techniques , 2014, IEEE Communications Surveys & Tutorials.

[17]  Chen Xiao-hui,et al.  Particle Filter for Underwater Bearings-Only Passive Target Tracking , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[18]  Amitava Das,et al.  Window based Multiple Model Adaptive Estimation for Navigational Framework , 2016 .

[19]  Ming Xin,et al.  High-degree cubature Kalman filter , 2013, Autom..

[20]  Shashi Poddar,et al.  Improving multiple model adaptive estimation by filter stripping , 2015, 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS).

[21]  Liang Hongtao,et al.  Tracking UUV based on interacting multiple model unscented particle filter with multi-sensor information fusion , 2015 .

[22]  Shesheng Gao,et al.  Interacting multiple model estimation-based adaptive robust unscented Kalman filter , 2017 .

[23]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .