Nonlinear estimation framework in target tracking

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete-time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic model description. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements the particle filter with advanced features. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper describes the individual components of the framework, their key features and use. The paper demonstrates easy and natural application of the framework in target tracking which is illustrated in two examples - tracking a ship with unknown control and tracking three targets based on raw data.

[1]  Ondřej Straka,et al.  A Software Framework and Tool for Nonlinear State Estimation , 2009 .

[2]  James V. Candy,et al.  Bayesian Signal Processing , 2009 .

[3]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[4]  Ondrej Straka,et al.  FRAMEWORK FOR IMPLEMENTING AND TESTING NONLINEAR FILTERS , 2006 .

[5]  E. Blasch,et al.  Multiresolution EO/IR target tracking and identification , 2005, 2005 7th International Conference on Information Fusion.

[6]  Miroslav Šimandl.  State Estimation for Non-Gaussian Models , 1996 .

[7]  H. M. Shertukde,et al.  Tracking of crossing targets with imaging sensors , 1991 .

[8]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[9]  Ondrej Straka,et al.  Functional sampling density design for particle filters , 2008, Signal Process..

[10]  Niels Kjølstad Poulsen,et al.  KALMTOOL for Use with MATLAB , 2003 .

[11]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[12]  Miroslav Flídr,et al.  Nonlinear Nonnormal Dynamic Models: State Estimation and Software , 1997 .

[13]  H. Sorenson,et al.  Recursive bayesian estimation using gaussian sums , 1971 .

[14]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[15]  Erik Blasch,et al.  Nonlinear tracking evaluation using absolute and relative metrics , 2006, SPIE Defense + Commercial Sensing.

[16]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[17]  LI X.RONG,et al.  Evaluation of estimation algorithms part I: incomprehensive measures of performance , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[18]  James V. Candy,et al.  Model-Based Signal Processing , 1985 .

[19]  Niels Kjølstad Poulsen,et al.  New developments in state estimation for nonlinear systems , 2000, Autom..

[20]  Wolfgang Koch,et al.  Fixed-interval retrodiction approach to Bayesian IMM-MHT for maneuvering multiple targets , 2000, IEEE Trans. Aerosp. Electron. Syst..

[21]  Ondrej Straka,et al.  NONLINEAR FILTERING TOOLBOX FOR CONTINUOUS STOCHASTIC SYSTEMS WITH DISCRETE MEASUREMENTS , 2006 .