American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Coordinate descent optimization for l1 minimization with application to compressed sensing; a greedy algorithm
  • IPI Home
  • This Issue
  • Next Article
    A support theorem for the geodesic ray transform of symmetric tensor fields
August  2009, 3(3): 465-486. doi: 10.3934/ipi.2009.3.465

Perfect and almost perfect pulse compression codes for range spread radar targets

Markku Lehtinen 1, , Baylie Damtie 2, , Petteri Piiroinen 3, and  Mikko Orispää 4,

1. 

University of Oulu, Sodankylä Geophysical Observatory, Sodankylä, Finland
2. 

Washera Geospace and Radar Science Laboratory, Bahir Dar University, P.O.Box, 79, Bahir Dar, Gojjam, Ethiopia
3. 

Department of Mathematics and Statistics, University of Helsinki, FI-00014, Helsinki, Finland
4. 

Sodankylä Geophysical Observatory, University of Oulu, Tähteläntie 62, FIN-99600 Sodankylä, Finland

Received  October 2008 Revised  February 2009 Published  July 2009

It is well known that a matched filter gives the maximum possible output signal-to-noise ratio (SNR) when the input is a scattering signal from a point like radar target in the presence of white noise. However, a matched filter produces unwanted sidelobes that can mask vital information. Several researchers have presented various methods of dealing with this problem. They have employed different kinds of less optimal filters in terms of the output SNR from a point-like target than that of the matched filter. In this paper we present a method of designing codes, called perfect and almost perfect pulse compression codes, that do not create unwanted sidelobes when they are convolved with the corresponding matched filter. We present a method of deriving these types of codes from any binary phase radar codes that do not contain zeros in the frequency domain. Also, we introduce a heuristic algorithm that can be used to design almost perfect codes, which are more suitable for practical implementation in a radar system. The method is demonstrated by deriving some perfect and almost perfect pulse compression codes from some binary codes. A rigorous method of comparing the performance of almost perfect codes (truncated) with that of perfect codes is presented.
Keywords: comparison of experiments, Radar waveform, Ambiguity function..
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Markku Lehtinen, Baylie Damtie, Petteri Piiroinen, Mikko Orispää. Perfect and almost perfect pulse compression codes for range spread radar targets. Inverse Problems & Imaging, 2009, 3 (3) : 465-486. doi: 10.3934/ipi.2009.3.465
[1]

Eric Falcon. Laboratory experiments on wave turbulence. Discrete & Continuous Dynamical Systems - B, 2010, 13 (4) : 819-840. doi: 10.3934/dcdsb.2010.13.819
[2]

Feimin Zhong, Wei Zeng, Zhongbao Zhou. Mechanism design in a supply chain with ambiguity in private information. Journal of Industrial & Management Optimization, 2020, 16 (1) : 261-287. doi: 10.3934/jimo.2018151
[3]

Hanqing Jin, Xun Yu Zhou. Continuous-time portfolio selection under ambiguity. Mathematical Control & Related Fields, 2015, 5 (3) : 475-488. doi: 10.3934/mcrf.2015.5.475
[4]

Filipa Caetano, Martin J. Gander, Laurence Halpern, Jérémie Szeftel. Schwarz waveform relaxation algorithms for semilinear reaction-diffusion equations. Networks & Heterogeneous Media, 2010, 5 (3) : 487-505. doi: 10.3934/nhm.2010.5.487
[5]

T. Varslo, C E Yarman, M. Cheney, B Yazıcı. A variational approach to waveform design for synthetic-aperture imaging. Inverse Problems & Imaging, 2007, 1 (3) : 577-592. doi: 10.3934/ipi.2007.1.577
[6]

Raluca Felea, Romina Gaburro, Allan Greenleaf, Clifford Nolan. Microlocal analysis of Doppler synthetic aperture radar. Inverse Problems & Imaging, 2019, 13 (6) : 1283-1307. doi: 10.3934/ipi.2019056
[7]

Zhilin Kang, Xingyi Li, Zhongfei Li. Mean-CVaR portfolio selection model with ambiguity in distribution and attitude. Journal of Industrial & Management Optimization, 2019  doi: 10.3934/jimo.2019094
[8]

Joseph D. Fehribach. Using numerical experiments to discover theorems in differential equations. Discrete & Continuous Dynamical Systems - B, 2003, 3 (4) : 495-504. doi: 10.3934/dcdsb.2003.3.495
[9]

Mikhail Gilman, Semyon Tsynkov. Statistical characterization of scattering delay in synthetic aperture radar imaging. Inverse Problems & Imaging, 2020, 14 (3) : 511-533. doi: 10.3934/ipi.2020024
[10]

Venkateswaran P. Krishnan, Eric Todd Quinto. Microlocal aspects of common offset synthetic aperture radar imaging. Inverse Problems & Imaging, 2011, 5 (3) : 659-674. doi: 10.3934/ipi.2011.5.659
[11]

Lassi Roininen, Markku S. Lehtinen, Petteri Piiroinen, Ilkka I. Virtanen. Perfect radar pulse compression via unimodular fourier multipliers. Inverse Problems & Imaging, 2014, 8 (3) : 831-844. doi: 10.3934/ipi.2014.8.831
[12]

Gang Bao, Jun Lai. Radar cross section reduction of a cavity in the ground plane: TE polarization. Discrete & Continuous Dynamical Systems - S, 2015, 8 (3) : 419-434. doi: 10.3934/dcdss.2015.8.419
[13]

G. Bellettini, Giorgio Fusco, Nicola Guglielmi. A concept of solution and numerical experiments for forward-backward diffusion equations. Discrete & Continuous Dynamical Systems - A, 2006, 16 (4) : 783-842. doi: 10.3934/dcds.2006.16.783
[14]

Gabriella Bretti, Maya Briani, Emiliano Cristiani. An easy-to-use algorithm for simulating traffic flow on networks: Numerical experiments. Discrete & Continuous Dynamical Systems - S, 2014, 7 (3) : 379-394. doi: 10.3934/dcdss.2014.7.379
[15]

Maide Bucolo, Federica Di Grazia, Luigi Fortuna, Mattia Frasca, Francesca Sapuppo. An environment for complex behaviour detection in bio-potential experiments. Mathematical Biosciences & Engineering, 2008, 5 (2) : 261-276. doi: 10.3934/mbe.2008.5.261
[16]

Wen-Li Li, Jing-Jing Wang, Xiang-Kui Zhang, Peng Yi. A novel road dynamic simulation approach for vehicle driveline experiments. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1035-1052. doi: 10.3934/dcdss.2019071
[17]

Iain Duff, Jonathan Hogg, Florent Lopez. Experiments with sparse Cholesky using a sequential task-flow implementation. Numerical Algebra, Control & Optimization, 2018, 8 (2) : 237-260. doi: 10.3934/naco.2018014
[18]

Arrigo Cellina, Carlo Mariconda, Giulia Treu. Comparison results without strict convexity. Discrete & Continuous Dynamical Systems - B, 2009, 11 (1) : 57-65. doi: 10.3934/dcdsb.2009.11.57
[19]

Kaitlyn Muller. The relationship between backprojection and best linear unbiased estimation in synthetic-aperture radar imaging. Inverse Problems & Imaging, 2016, 10 (2) : 549-561. doi: 10.3934/ipi.2016011
[20]

Qiang Yin, Gongfa Li, Jianguo Zhu. Research on the method of step feature extraction for EOD robot based on 2D laser radar. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1415-1421. doi: 10.3934/dcdss.2015.8.1415

2019 Impact Factor: 1.373

Tools

Metrics

  • PDF downloads (45)
  • HTML views (0)
  • Cited by (5)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences