Implementation of Kalman Filter Using Vhdl

Jolly Baliyan; Atiika Aggarwal; Ashwani Kumar

doi:https://doi.org/10.51976/ijari.221411

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Implementation of Kalman Filter Using Vhdl

Jolly Baliyan, Atiika Aggarwal, Ashwani Kumar

Vol 2 , Issue 2 , April - June 2014 | Pages: 69-77 | Research Paper

https://doi.org/10.51976/ijari.221411

Received: March 05, 2014 | Revised: April 20, 2014 | Accepted: May 28, 2014 | Published Online: June 15, 2014

Author Details ( * ) denotes Corresponding author

1. * Jolly Baliyan, Department of Electronics & Communication Engineering, Meerut Institute of Technology, Meerut, Uttar Pradesh, India (jollychaudhary1111@gmail.com)

2. Atiika Aggarwal, Department of Electronics & Communication Engineering, Meerut Institute of Technology, Meerut, Uttar Pradesh, India

3. Ashwani Kumar, Department of Electronics & Communication Engineering, Meerut Institute of Technology, Meerut, Uttar Pradesh, India

The main task in object tracking is to filter the movement information from undesired dynamic objects because this information is considered as noise. To cope with these difficulties the implementation of edge segment tracking (EST) algorithm based kalman filter is presented which is used to track the desired dynamic object and to filter the noise. The estimation of current state depends on the variables i.e. time, velocity, covariance and noise mainly. Segmenting objects is capable of identifying moving objects in image sequence. One object may consist of several parts with different motion as object motion and shape are less consistent within frames. The hardware implementation of kalman filter is done on FPGA (Virtex 5) using VHDL on Xilinx ISE simulator in the range of MHz clock frequency and tested with an ADC and DAC which were integrated into the design to support analog signals at the input and output of the system.

Keywords

Kalman Filter; FPGA; Prediction Model; Measurement Model; VHDL

F.A. Faruqi and R.C. Davis, Kalman Filter design for target tracking, IEEE Trans. Aerosp. Electron. Syst., AES-16, 500-508, 1980

Y. Bar-Shalom and X. R. Lin, Estimation and tracking: principles, techniques, and software, Artech House, 1993, 417-483

S. Y. Kung and J. N. Hwang, Systolic array designs for Kalman Filtering, IEE Trans., Signal Processing, 171-182, 1991

L. P. Maguire and G. W. Irwin, Transputer implementation of Kalman Filter, IEEE Proc., 138, 355-362, 1991.

M. Munu, I. Harrison, D. Wilkin, and M. S. Woolfson, Comparison of adaptive target-tracking algorithms for phased-array radar, IEE Proc. F. Commun, Radar and Signal, 139, 336-342.

D. P. Atherton and H. J. Lin, Parallel implementation of IMM tracking algorithm using transputers, IEE Proc.-Radar, Sonar Navig., 141, 325-332, 1994

J. M. Jover, T. Kailath, A parallel architecture for Kalman Filter measurement update and parameter estimation, Automatica, 22, 32-57, 1986

Song Ci. Sharif, H (2005) Performance Comparison of Kalman Filter based approaches for energy efficiency in wireless sensor networks, IEEE conf.: on Computer Systems and Applications.