Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index

We present an automatic parameter setting method to achieve an accurate second-order Kalman filter tracker based on a steady-state performance index. First, we propose an efficient steady-state performance index that corresponds to the root-mean-square (rms) prediction error in tracking. We then derive an analytical relationship between the proposed performance index and the generalized error covariance matrix of the process noise, for which the automatic determination using the derived relationship is presented. The model calculated by the proposed method achieves better accuracy than the conventional empirical model of process noise. Numerical analysis and simulations demonstrate the effectiveness of the proposed method for targets with accelerating motion. The rms prediction error of the tracker designed by the proposed method is 63.8% of that with the conventional empirically selected model for a target accelerating at 10 m/s2.

[1]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[2]  Guanglong Du,et al.  A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots , 2015, IEEE Transactions on Industrial Electronics.

[3]  V. Jain,et al.  Filter Design for Steady-State Tracking of Maneuvering Targets with LFM Waveforms , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[4]  Hongwei Liu,et al.  Switched Kalman filter-interacting multiple model algorithm based on optimal autoregressive model for manoeuvring target tracking , 2015 .

[5]  T. Singh,et al.  Optimal design of /spl alpha/-/spl beta/-/spl gamma/(/spl gamma/) filters , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[6]  Trinh Chu Duc,et al.  Application of Street Tracking Algorithm in an INS/GPS Integrated Navigation System , 2015 .

[7]  Martina Daun,et al.  Tracking Algorithms for Multistatic Sonar Systems , 2010, EURASIP J. Adv. Signal Process..

[8]  T. Benedict,et al.  Synthesis of an optimal set of radar track-while-scan smoothing equations , 1962 .

[9]  Myo Taeg Lim,et al.  New preceding vehicle tracking algorithm based on optimal unbiased finite memory filter , 2015 .

[10]  Bertil Ekstrand,et al.  Some aspects on filter design for target tracking , 2012 .

[11]  Xiao Feng,et al.  Autonomous navigation based on unscented-FastSLAM using particle swarm optimization for autonomous underwater vehicles , 2015 .

[12]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[13]  C. F. Asquith.  Weight Selection in First-Order Linear Filters , 1969 .

[14]  P. Kalata,et al.  The tracking index: A generalized parameter for α-β and α-β-γ target trackers , 1983, The 22nd IEEE Conference on Decision and Control.

[15]  Emanuela Falletti,et al.  Theoretical analysis and tuning criteria of the Kalman filter-based tracking loop , 2014, GPS Solutions.

[16]  Paolo Braca,et al.  Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Masao Masugi,et al.  Performance analysis of α- β- γtracking filters using position and velocity measurements , 2015, EURASIP J. Adv. Signal Process..

[18]  Ravi Kumar Jatoth,et al.  Performance Analysis of Alpha Beta Filter, Kalman Filter and Meanshift for Object Tracking in Video Sequences , 2015 .

[19]  Akihiro Takeuchi,et al.  On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[20]  David Frederic Crouse.  A General Solution to Optimal Fixed-Gain (α-β-γ etc.) Filters , 2015, IEEE Signal Process. Lett..

[21]  Sun-Mog Hong,et al.  Multiple-target tracking and track management for an FMCW radar network , 2013, EURASIP J. Adv. Signal Process..

[22]  Pierre Baldi,et al.  Gradient descent learning algorithm overview: a general dynamical systems perspective , 1995, IEEE Trans. Neural Networks.

[23]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[24]  Huadong Meng,et al.  Steady-State analysis of target tracker with constant input/bias constraint , 2008 .

[25]  Kenshi Saho.  Fundamental Properties and Optimal Gains of a Steady-State Velocity Measured α-β Tracking Filter , 2014 .