Radar Image Enhancement Model Using Adaptive Kalman Filter
DOI:
https://doi.org/10.26438/ijcse/v7i2.750759Keywords:
Adaptive Kalman filter, echo cancellation, denoising, deblurring, radar imagesAbstract
Echo and noise is one of the critical disturbances that alter the quality of radar images. To reduce the echo and noise in radar images we used adaptive Kalman filter. For radar image enhancement, denoising and echo cancellation are need of the system. In this paper an adaptive Kalman filter based model is proposed to reduce the echo and noise in radar images. The Kalman filter is compared with different parameters. Form experimental results the new proposed adaptive Kalman filter based model gives promising results for echo cancellation and denoising of radar images.
References
[1] Mohamed Amine ATTALAH, Toufik LAROUSSI, Aladdine AOUANE, Ali MEHANAOUI“Adaptive Filters for Direct Path and Multipath Interference Cancellation: Application to FM- RTL-SDR Based Passive Bistatic Radar” 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) 2016, pp 461- 465.
[2] Paul Zarchan; Howard Musoff (2000). Fundamentals of Kalman Filtering: A Practical Approach. American Institute of Aeronautics and Astronautics, Incorporated. ISBN 978-1-56347- 455-2.
[3] Kalman, R. E. (1960). "A New Approach to Linear Filtering and Prediction Problems". Journal of Basic Engineering. 82: 35. Doi:10.1115/1.3662552.
[4] Kelly, Alonzo (1994). "A 3D state space formulation of a navigation Kalman filter for autonomous vehicles" (PDF). DTIC Document: 13. 2006 Corrected Version
[5] Reid, Ian; Term, Hilary. "Estimation II" (PDF). www.robots.ox.ac.uk. Oxford University. Retrieved 6 August 2014.
[6] Fakhrul Razi Ahmad, Zakuan; et al. (2018). "Performance Assessment of an Integrated Radar Architecture for Multi-Types Frontal Object Detection for Autonomous Vehicle". 2018 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). Retrieved 9 January 2019.
[7] Ahi, Kiarash (26 May 2016). "Modeling of terahertz images based on x-ray images: a novel approach for verification of terahertz images and identification of objects with fine details beyond terahertz resolution". Proc. SPIE 9856, Terahertz Physics, Devices, and Systems X: Advanced Applications in Industry and Defense, 985610. doi:10.1117/12.2228685. Retrieved 26 May2016.
[8] Chen, Yangkang; Fomel sergey (November-December 2015). “Random noise attenuation using local signal-and-noise orthogonalization”. Geophysics. 80 (6): WD1- WD9_doi:10.1190/GEO2014-0227.1.
[9] Federico Garc´ıa-Rial, Luis Ubeda-Medina, Jesus´ Graja “Real- time GPU-based image processing for 3-D THz radar” DOI 10.1109/TPDS.2017.2687927, IEEE Transactions on Parallel and Distributed Systems 2017, vol. 28, issue 10, pp 1-13, oct2017.
[10] Xianru Liu and Zixing “Advanced Obstacles Detection and Tracking by Fusing Millimeter Wave Radar and Image Sensor Data” International Conference on Control, Automation and Systems 2010 Oct. 27-30, 2010. [4] B. B. Mohapatra, Vishal Mahajan, Abid Hussain VALRDE, DRDO Bangalore, India, “Post Processing Techniques for Inverse. Synthetic Aperture Radar Imaging” IEEE 2016, Volume: 3. pp 1115-1120.
[11] B.B Mohapatra, vishal Mahajan, Abid Hussain VA “Post Processing Techniques for Inverse Synthetic Aperture Radar Imaging” 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).
[12] Qian Bao 1, Yun Lin 1, Wen Hong 1，Bingchen Zhang1 “Multi-circular synthetic aperture radar imaging processing procedure based on compressive sensing” 4th Int. Workshop on Compressed Sensing on Radar, Sonar, and Remote Sensing 2016, pp 47-50.
[13]Thomas Wagner “Radar Signal Processing for Jointly Estimating Tracks and Micro-Doppler Signatures” Graduate Student Member, IEEE, Reinhard Feger, Member, IEEE and Andreas Stelzer, Member, IEEE 2017, Volume: 5, pp-1220- 1238.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
