Kalman filter and ARMA filter as approach to multiple sensor data fusion problem

The Air Traffic Control (ATC) systems are critical due the amount of information that they have to process in real time. These systems combined different kind of information, like primary and secondary radar information and flight planning information. Depending the extent of a country different RADAR are installed trying to cover most of the country area. This could become a problem, because different RADAR can detect and locate the same object at different position on the map, this is a multiple sensor data fusion problem. In this paper we present a technique that combines conventional Kalman filter techniques with an ARMA filter to provide a solution to the problem. Multiple objects tracking examples are presented.

[1]  R. H. Khan.  Target detection and tracking with HF radar using ISAR techniques , 1996, Proceedings of the 1996 IEEE National Radar Conference.

[2]  Abir El Abed,et al.  Spatio-temporal target-measure association using an adaptive geometrical approach , 2012, Pattern Recognit. Lett..

[3]  Felix Mora-Camino,et al.  Path Stretching and Tracking for Time-Based Aircraft Spacing at Meter Fix , 2006 .

[4]  Miki Haseyama,et al.  An ARMA order selection method with fuzzy reasoning , 2001, Signal Process..

[5]  T. Evans.  Airborne Situation Awareness , 1997 .

[6]  Michael O. Kolawole.  9 – Decision theory , 2002 .

[7]  Jinfang Liu,et al.  Information Fusion Kalman Predictor for Two-Sensor Multichannel ARMA Signal System with Time-Delayed Measurements , 2012 .

[8]  K. Matsunaga,et al.  Validation test of Downlink Aircraft Parameters via SSR mode S experimental system , 2012, 2012 13th International Radar Symposium.

[9]  E. Gagnon,et al.  Extended Kalman Filter for State Estimation and Trajectory Prediction of a Moving Object Detected by an Unmanned Aerial Vehicle , 2007, 2007 American Control Conference.

[10]  George Apostolakis,et al.  Decision theory , 1986 .

[11]  Edmund Førland Brekke,et al.  Tracking of targets with state dependent measurement errors using recursive BLUE filters , 2009, 2009 12th International Conference on Information Fusion.

[12]  Zi-Li Deng,et al.  Self-tuning weighted measurement fusion Kalman filtering algorithm , 2012, Comput. Stat. Data Anal..

[13]  Yuan Gao,et al.  New approach to information fusion steady-state Kalman filtering , 2005, Autom..

[14]  Chongzhao Han,et al.  Sequential unscented Kalman filter for radar target tracking with range rate measurements , 2005, 2005 7th International Conference on Information Fusion.

[15]  V. B. Frencl,et al.  Tracking with range rate measurements: Turn rate estimation and particle filtering , 2012, 2012 IEEE Radar Conference.

[16]  Michael S. Nolan,et al.  Fundamentals of Air Traffic Control , 1990 .

[17]  D. J. White,et al.  Decision Theory , 2018, Behavioral Finance for Private Banking.

[18]  Aníbal Ollero,et al.  Path planning based on Genetic Algorithms and the Monte-Carlo method to avoid aerial vehicle collisions under uncertainties , 2011, 2011 IEEE International Conference on Robotics and Automation.

[19]  Shih-Chieh Huang,et al.  Recursive estimation of vehicle position by using navigation sensor fusion , 2012, 2012 12th International Conference on ITS Telecommunications.

[20]  Luis A. Aguirre,et al.  Flight path reconstruction – A comparison of nonlinear Kalman filter and smoother algorithms , 2011 .

[21]  Yongzhi Liu,et al.  Neuro-fuzzy techniques for airborne target tracking , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[22]  S. State,et al.  A METHODOLOGY FOR AUTOMATED TRAJECTORY PREDICTION ANALYSIS , 2004 .

[23]  Zi-Li Deng,et al.  Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal , 2011, Signal Process..

[24]  Xin Wang,et al.  Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises , 2012, J. Appl. Math..

[25]  S. Max,et al.  A Wireless Location System for Sensing the Relative Position between Mining Vehicles , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[26]  Carlo Kopp,et al.  RANGE-LIMITED UAV TRAJECTORY USING TERRAIN MASKING UNDER RADAR DETECTION RISK , 2012, Appl. Artif. Intell..

[27]  Mark A. Richards,et al.  Fundamentals of Radar Signal Processing , 2005 .