Open Access Open Access  Restricted Access Subscription or Fee Access

Fusion of Derived Heading for Bearings Only Tracking

B. Sindhu(1), J. Valarmathi(2*), S. Christopher(3)

(1) VIT University, India
(2) VIT University, India
(3) , India
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v12i2.16711

Abstract


Data fusion technique combines data from two similar sensors placed in different location in order to reduce the error in filtered state estimate. In this paper, state vector fusion (SVF) and measurement fusion (MF) are used to fuse the bearing measurement and also to fuse the derived heading measurement. The derived heading from bearing measurement increases the accuracy of target state estimate. Here, the Lagrange three point difference (LTPD) method has been proposed to derive heading from the set of bearing measurements. Two sensors with single target scenario are considered and the heading parameters are derived for each sensor. The bearing and derived heading measurements from two different sensors are fused using SVF or MF and then nonlinear Extended Kalman filter (EKF) is used to obtain the optimized state estimate. Simulations have been carried out in order to compare the SVF and MF fusion techniques for the bearing measurements as well as the derived heading parameters using existing centered difference (CD) and proposed LTPD.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords


State Vector Fusion; Measurement Fusion; Derived Heading; Bearing Measurement; Lagrange Three Point Difference Method

Full Text:

PDF


References


V. P. Panakkal, R.Velmurugan, Bearings-Only Tracking using Derived Heading, IEEE Aerospace Conference, pp. 1-11, USA, March, 2010.
https://doi.org/10.1109/aero.2010.5446691

VPS Naidu, Fusion architectures for 3D target tracking using IRST and radar measurements, Journal of Aerospace sciences and technologies, Vol. 62(Issue 3): 1-13, 2010.

J. A. Roecker, C.D. McGillem, Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion, IEEE Transactions on Aerospace and Electronic Systems, Vol.24(Issue 4): 447-449, 1988.
https://doi.org/10.1109/7.7186

Y. Bar-Shalom, X. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation (Wiley, 2001).

Y. Bar-Shalom, P. Willett, and X. Tian, Tracking and Data Fusion: A Handbook of Algorithms (YBS Publishing, 2011).

S. Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems (Artech House, 1999).

A. H. Jazwinski, Stochastic Processes and Filtering theory (Courier Corporation, 2007).

R. Anitha, S. Renuka and A. Abudhahir, Multi Sensor Data Fusion Algorithms for Target Tracking using Multiple Measurements, IEEE International Conference on Computational Intelligence and Computing Research, pp. 1-4, Enathi, INDIA, December 2013.
https://doi.org/10.1109/iccic.2013.6724283

Q. Gan, C.J. Harris, Comparison of Two Measurement Fusion Methods for Kalman-Filter-Based Multi-sensor Data Fusion, IEEE Transactions on Aerospace and Electronic Systems, Vol. 37(Issue 1):273-279, January 2001.
https://doi.org/10.1109/7.913685

R. Yang, G. Wah Ng, N. Ma and C. Swee chia, A Bearing and Heading Tracker from a Stationary Sonar Sensor, Asia Pacific OCEANS, pp. 1-6, Singapore, May 2007.
https://doi.org/10.1109/oceansap.2006.4393965

J.R Raol, Multi-sensor data fusion with MATLAB (CRC press, 2009).

A. Gelb, Applied Optimal Estimation (MIT Press, 1974).

B. Ristic, S. Arulampalam, N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House, 2004).

W. Grossman, Bearings-Only Tracking: A Hybrid Coordinate System Approach, IEEE conference on Decision and Control, pp. 2032 – 2037, Brighton, UK, December 1991.
https://doi.org/10.1109/cdc.1991.261776

M. Mallick, S. Arulampalam, Comparison of Nonlinear Filtering Algorithms in Ground Moving Target Indicator Target Tracking, Proceedings of SPIE, Vol. 5204, pp. 1-7, San Diego, California, January 2004.
https://doi.org/10.1117/12.506803

D. V. Griffiths, I.M. Smith, Numerical methods for Engineers: A Programming approach (CRC Press, 1991).

M. Mallick, V. Krishnamurthy, B. Vo, Integrated Tracking, Classification, and Sensor Management: Theory and Applications (Wiley/IEEE, 2012, pp. 3-42).

M. Mallick, Y. Bar-Shalom, T. Kirubarajan and M. Moreland, An Improved Single-Point Track Initiation Using GMTI Measurements, IEEE Transactions on Aerospace and Electronic Systems, Vol. 51(Issue 4): 2697-2713, October 2015.
https://doi.org/10.1109/taes.2015.140599

J. H. Mathews, K. K. Fink, Numerical Methods Using Matlab (Prentice-Hall Inc, 2004).

Y. Zhou, J. Xu, Y. Jing, Comparison of Centralized Multi-Sensor Measurement and State Fusion Methods with Ensemble Kalman Filter for Process Fault diagnosis, Chinese control and Decision conference, pp. 3302—3307, Xuzhou, China, May 2010.
https://doi.org/10.1109/ccdc.2010.5498594

S. Bhattacharya, R. Appavu Raj, Performance evaluation of multi-sensor data fusion technique for test range application, Sadhana Academy Proceedings in Engineering Sciences, Vol. 29(Issue 2): 237–247, April 2004.
https://doi.org/10.1007/bf02703734

G. Wah Ng, C. Huat Tan, T. Poh Ng, Tracking Ground Targets Using State Vector Fusion, 7th International Conference on Information Fusion (FUSION), pp. 297-302, Philadelphia, PA, USA, July 2005.
https://doi.org/10.1109/icif.2005.1591868

B. Sindhu, J. Valarmathi, S. Christopher, Target heading estimation using Lagrange difference method through bearing only measurement, International Journal of Engineering and Technology, Vol.7(Issue 4): 3995-3998, December 2018.

Mohammed, D., Abdelkrim, M., Mokhtar, K., Benoudnine, H., Enhanced Modified Polar Coordinates Filter Applied to Bearing Only Tracking, (2013) International Review of Automatic Control (IREACO), 6 (3), pp. 365-372.

Narmadha, C., Marichamy, P., Narayanan, A., An Energy Efficient Clustering Algorithm Using Harmony Memory Search for Wireless Sensor Network, (2018) International Review on Modelling and Simulations (IREMOS), 11 (5), pp. 325-332.
https://doi.org/10.15866/iremos.v11i5.13584

El Abbassi, M., Jilbab, A., Bourouhou, A., A Robust Model of Multi-Sensor Data Fusion Applied in Wireless Sensor Networks for Fire Detection, (2016) International Review on Modelling and Simulations (IREMOS), 9 (3), pp. 173-180.
https://doi.org/10.15866/iremos.v9i3.8558

Habib, T., In-Orbit Spacecraft Inertia, Attitude, and Orbit Estimation Based on Measurements of Magnetometer, Gyro, Star Sensor and GPS Through Extended Kalman Filter, (2018) International Review of Aerospace Engineering (IREASE), 11 (6), pp. 247-251.
https://doi.org/10.15866/irease.v11i6.14839

Grillo, C., Montano, F., Automatic EKF Tuning for UAS Path Following in Turbulent Air, (2018) International Review of Aerospace Engineering (IREASE), 11 (6), pp. 241-246.
https://doi.org/10.15866/irease.v11i6.15122

Grillo, C., Montano, F., An EKF Based Method for Path Following in Turbulent Air, (2017) International Review of Aerospace Engineering (IREASE), 10 (1), pp. 1-6.
https://doi.org/10.15866/irease.v10i1.10501


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2020 Praise Worthy Prize