Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch–Tung–Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.

[1]  Yaakov Bar-Shalom,et al.  Update with out-of-sequence measurements in tracking: exact solution , 2000, SPIE Defense + Commercial Sensing.

[2]  Anna Freud,et al.  Design And Analysis Of Modern Tracking Systems , 2016 .

[3]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[4]  Felix Govaers,et al.  A generalized solution to smoothing and Out-of-Sequence processing , 2012, 2012 15th International Conference on Information Fusion.

[5]  Joaquín Míguez,et al.  Analysis of selection methods for cost-reference particle filtering with applications to maneuvering target tracking and dynamic optimization , 2007, Digit. Signal Process..

[6]  Javier Bajo,et al.  Effectiveness of Bayesian filters: An information fusion perspective , 2016, Inf. Sci..

[7]  Luca Martino,et al.  Group Importance Sampling for Particle Filtering and MCMC , 2017, Digit. Signal Process..

[8]  Robin J. Evans,et al.  A fixed-lag smoothing solution to out-of-sequence information fusion problems , 2002, Commun. Inf. Syst..

[9]  Juan M. Corchado,et al.  Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond , 2017, Frontiers of Information Technology & Electronic Engineering.

[10]  Robert M. Rogers,et al.  Applied Mathematics in Integrated Navigation Systems , 2000 .

[11]  D. Simon.  Kalman filtering with state constraints: a survey of linear and nonlinear algorithms , 2010 .

[12]  Ihsan Ullah,et al.  A state estimation and fusion algorithm for high-speed low-altitude targets , 2016, 2016 19th International Multi-Topic Conference (INMIC).

[13]  Y. Bar-Shalom,et al.  Multi-sensor multi-target tracking using out-of-sequence measurements , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[14]  Yaakov Bar-Shalom,et al.  Decorrelated unbiased converted measurement Kalman filter , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Taek Lyul Song,et al.  Smoothing data association for target trajectory estimation in cluttered environments , 2016, EURASIP Journal on Advances in Signal Processing.

[16]  Y. Bar-Shalom,et al.  One-step solution for the multistep out-of-sequence-measurement problem in tracking , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Carlo Quaranta,et al.  Technique for radar and infrared search and track data fusion , 2013 .

[18]  Yunmin Zhu,et al.  Optimal update with out-of-sequence measurements , 2005, IEEE Transactions on Signal Processing.

[19]  David R. Iny.  Theory and practical application of out of sequence measurements with results for multi-static tracking , 2007, SPIE Optical Engineering + Applications.

[20]  A. Abudhahir,et al.  Multi sensor data fusion algorithms for target tracking using multiple measurements , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[21]  Yaakov Bar-Shalom,et al.  Nonlinear out-of-sequence measurement filtering with applications to GMTI tracking , 2002, SPIE Defense + Commercial Sensing.

[22]  Robin J. Evans,et al.  Fundamentals of Object Tracking , 2011 .

[23]  Y. Bar-Shalom,et al.  IMM estimator with out-of-sequence measurements , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[24]  Robin J. Evans,et al.  A Bayesian solution and its approximations to out-of-sequence measurement problems , 2003, Inf. Fusion.

[25]  Ihsan Ullah,et al.  Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors , 2015 .

[26]  Mahendra Mallick,et al.  Optimal multiple-lag out-of-sequence measurement algorithm based on generalized smoothing framework , 2005, SPIE Optics + Photonics.

[27]  A. Marrs,et al.  Particle filters for tracking with out-of-sequence measurements , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[28]  Bhekisipho Twala.  Modelling Out-Of-Sequence Measurements Using a Grey Relational Anaysis and Copulas Hybrid , 2012 .

[29]  Abder Rezak Benaskeur,et al.  Forward prediction-based approach to target-tracking with Out-of-Sequence Measurements , 2008, 2008 47th IEEE Conference on Decision and Control.

[30]  Y. Bar-Shalom,et al.  Unbiased converted measurements for tracking , 1998 .

[31]  A. Marrs,et al.  Comparison of the KF and particle filter based out-of-sequence measurement filtering algorithms , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[32]  Lang Hong,et al.  Multirate filtering with out-of-sequence data , 2002, SPIE Defense + Commercial Sensing.

[33]  Juan M. Corchado,et al.  Fitting for smoothing: A methodology for continuous-time target track estimation , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[34]  Yuming Bo,et al.  Tracking algorithm with radar and infrared sensors using a novel adaptive grid interacting multiple model , 2014 .

[35]  Taek Lyul Song,et al.  Out-of-sequence measurements update using the information filter with reduced data storage , 2014, 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF).