INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.6, No.12, Nov. 2014

Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

Full Text (PDF, 1589KB), PP.70-90


Views:16   Downloads:1

Author(s)

Mohamed B. El Mashade

Index Terms

Adaptive radar detectors;post-detection integration;Swerling fluctuation models;partially-correlated χ2 fluctuating targets, target multiplicity environments

Abstract

This paper addresses the problem of detecting the partially-correlated χ2 fluctuating targets with two and four degrees of freedom. It presents the performance analysis, in its exact form, of GTM-CFAR processor when the operating environment is contaminated with extraneous targets and the radar receiver post-detection integrates M pulses of exponentially correlated targets. Mathematical formulas for the detection and false alarm probabilities are derived, in the absence as well as in the presence of spurious targets which are fluctuating in accordance with the so-called moderately fluctuating χ2 targets. A thorough performance assessment by several numerical examples, which has considered the role that each parameter can play in the processor performance, is also given. The results show that the processor performance improves, for weak SNR of the primary target, as the correlation coefficient ρs increases and this occurs either in the absence or in the presence of outlying targets. As the strength of the target return increases, the processor tends to invert this behavior. The SWI & SWII and SWIII & SWIV models enclose the correlated target cases when the target correlation follows χ2 fluctuation models with two and four degrees of freedom, respectively, and this behavior is common for all GTM based detectors.

Cite This Paper

Mohamed B. El Mashade,"Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers", IJIGSP, vol.6, no.12, pp. 70-90, 2014.DOI: 10.5815/ijigsp.2014.12.10

Reference

[1]Aloisio, V. di Vito, A., & Galati, G. (1994), “Optimum detection of moderately fluctuating radar targets”, IEE Proc.-Radar, Sonar Navig., Vol.141, No.3, (June 1994), pp. 164-170.

[2]El Mashade, M. B. (1995), “Analysis of the censored mean level CFAR processor in multiple target and nonuniform clutter,” IEE Radar, Sonar Navig., Vol.142, No.5, (Oct. 1995), pp. 259-266.

[3]J. Malik, D. Girdhar, R. Dahiya & G. Sainarayanan (2014), "Reference Threshold Calculation for Biometric Authentication," I.J. Image, Graphics and Signal Processing, 2014, 2, 46-53.

[4]El_Mashade, M. B. (1995), “Detection performance of the trimmed-mean CFAR processor with noncoherent integration.” IEE Radar, Sonar Navig., Vol.142, No.1, (Feb. 1995), pp. 18-24.

[5]Swerling, P. (1997), “Radar probabilitity of detection for some additional fluctuating target cases,” IEEE Transactions Aerospace and Electronic Systems, AES-33, No. 2, (April 1997), pp. 698-709.

[6]Ouadfel, S. & Meshoul, S. (2013), "A Fully Adaptive and Hybrid Method for Image Segmentation Using Multilevel Thresholding," I.J. Image, Graphics and Signal Processing, 2013, 1, 46-57.

[7]El Mashade, M. B. (1998), “Multipulse analysis of the generalized trimmed mean CFAR detector in nonhomogeneous background environments,” Int. J. Electron. Commun. (AEū), Vol.52, No. 4, (1998), pp. 249-260.

[8]El Mashade, M. B. (2002), “Target multiplicity performance analysis of radar CFAR detection techniques for partially correlated chi-square targets,” Int. J. Electron. Commun. (AEü), Vol.56, No.2, (April 2002), pp.84-98.

[9]El Mashade, M. B. (2008), “Performance Analysis of OS Structure of CFAR Detectors in Fluctuating Target Environments,” Progress In Electromagnetics Research C, Vol. 2, pp. 127-158, 2008.

[10]El Mashade, M. B. (2011), “Analysis of adaptive detection of moderately fluctuating radar targets in target multiplicity environments,” Journal of the Franklin Institute 348 (2011), pp. 941–972.

[11]El Mashade, M. B. (2012), "Target-Multiplicity Analysis of CML Processor for Partially-Correlated χ2 Targets," International Journal of Aerospace Sciences 2012, Vol.1, No.5, pp. 92-106.

[12]El Mashade, M. B. (2013), "Multiple-Target Analysis of Adaptive Detection of Partially Correlated χ2 Targets," Int. J. Space Science and Engineering, Vol. 1, No. 2, 2013, pp. 142-176.

[13]El Mashade, M. B. (2005), “M-Sweeps exact performance analysis of OS modified versions in nonhomogeneous environments," IEICE Trans. Commun., Vol.E88-B, No.7, (July 2005), pp. 2918-2927.

[14]El Mashade, M. B. (2011), "Analytical Performance Evaluation of Optimum Detection of χ2 Fluctuating Targets with M-Integrated Pulses," Electrical and Electronic Engineering 2011; Vol.1, No.2, pp. 93-111.

[15]El Mashade, M. B. (1998), “Detection analysis of linearly combined order statistic CFAR algorithm in nonhomogeneous background environments,” Signal Processing “ELSEVIER”, Vol.68, (Aug. 1998), pp. 59-71.

[16]El Mashade, M. B. (2006), "Performance Comparison of a Linearly Combined Ordered-Statistic Detectors under Postdetection Integration and Nonhomogeneous Situations," Journal of Electronics (China), Vol.23, No.5, (September 2006), pp. 698-707. 

[17]El Mashade, M. B. (2013), "Post detection Integration Analysis of Adaptive Detection of Partially-Correlated χ2 Targets in The Presence of Interferers," Majlesi Journal of Electrical Engineering, Vol. 7, No. 3, pp. 43-58, September 2013.