PL EN


Preferences help
enabled [disable] Abstract
Number of results
2019 | 121 | 53-63
Article title

Implementation of Signal Processing Unit for Laser Range Finder

Content
Title variants
Languages of publication
EN
Abstracts
EN
Detection of the range of a target using laser range finders requires filtering of incoming data in real-time which can be achieved by a dedicated hardware to meet demanding time requirements. In this paper efficient filter structure which is the combination of constant false alarm rate (CFAR) and pipelined least mean square (LMS) adaptive filtering technique used to create solution for various adaptive filtering problems. Cell averaging (CA) CFAR and automatic censored cell averaging (ACCA) CFAR algorithm are fused separately with the adaptive filter in order to filter the incoming signal so that the detection of the of a target can be achieved through peak detection unit and accordingly range can be calculated. The design with CA-CFAR fused with PLMS filter is synthesized and implemented on a Xilinx Virtex4 FPGA with 32 MHz clock using Xilinx ISE software. Other design with ACCA-CFAR combined with PLMS filter is modeled and simulated in MATLAB simulink.
Year
Volume
121
Pages
53-63
Physical description
Contributors
  • Department of Electronics, Indian School of Mines, 826004, Dhanbad, India
  • Department of Electronics, Indian School of Mines, 826004, Dhanbad, India
author
  • Instruments Research & Establishment Organization, D.R.D.O. 248008, Dehradun, India
References
  • [1] Douglas, S.C. Introduction to Adaptive Filters, Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999.
  • [2] Farrouki A and Barkat M., Automatic censoring CFAR detector based on ordered data variability for non-homogeneous environments, IEE Proc. Radar Sonar Navig. (2005) 152(1), 43–51
  • [3] Finn H. M. & Johnson R.S., Adaptive detection mode with threshold control as a function of spatially sampled clutter estimates, RCA Rev. 29(3), (1968) 414-464.
  • [4] Ghandhi P. P. and Kassam S. A., Analysis of CFAR processors in nonhomogeneous background, IEEE Trans. Aerospace. Electron Syst. 24(4) (1988) 427–445.
  • [5] Rohling H., Radar CFAR thresholding in clutter and multiple target situations, IEEE Trans. Aerosp. Electron. Syst. 19(4) (1983) 608–621.
  • [6] Schmid K., Waters K., et al, LIDAR 101: an introduction to LIDAR technology, data and applications, NOAA Coastal Services Center, Charleston, SC (2008).
  • [7] M. Kronauge and H. Rohling,Fast two-dimensional CFAR procedure. IEEE Transactions on Aerospace and Electronic Systems, vol. 6, no. 9, (2013) pp. 1817-1823.
  • [8] JunweiYan; Xiang Li, Zhenhai Shao, Intelligent and fast two-dimensional CFAR procedure. IEEE International Conference on Communication Problem-Solving (ICCP), (2015) 461-463.
  • [9] Tsakalides P. &Nikias C. L., Performance Assessment of CFAR Processors in Pearson Distributed Clutter, IEEE Transactions on Aerospace and Electronics Systems, 36(4) (2000) 1377-1386.
  • [10] Uwe Meyer-Baese, Digital Signal Processing with Field Programmable Gate Arrays, Third Edition, Springer Series on Signals and Communication Technology. Springer-Verlag Berlin Heidelberg 2007. ISBN 978-3-540-72612-8
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-14a40856-36df-498a-a2d9-3376064720d8
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.