Heterogeneous performance assessment of new approach for partially-correlated χ2-targets adaptive detection

Authors

DOI:

https://doi.org/10.3103/S0735272721120025

Keywords:

adaptive detection, non-coherent integration, Swerling models, fluctuating target, partially-correlated χ2-target, multi-target environment

Abstract

Radars are the cornerstones of modern integrated air defense systems. The decision on the present or absence of object, ensuring non-fluctuating false alarms, represents one of its fundamental concepts. This involves the implementation of constant false alarm rate (CFAR) strategy that updates the detection threshold in accordance with the inhomogeneous observation scenario. The hardness of finding a single CFAR variant, to deal with diverse noise situations, necessitates the development of composite technique. In this regard, fusion of particular decisions of single CFAR schemes provides better final detection through appropriate fusion rules. This paper is intended with the analysis of linear fusion (LF) of CA, OS, and TM structures. The target of interest and the fallacious ones are supposed to follow χ2-model with two-degrees of freedom in their fluctuation. The closed-form expression is derived for the detection performance. Our simulation results demonstrate that the LF model exhibits robust behavior on the presence or absence of interferers. Additionally, the LF ideal performance surpasses the Neyman–Pearson (N-P) detector one, which is the yardstick of the CFAR world. Moreover, the LF strategy has the capability to hold the unchanged false alarm rate in face of the presence of interferers.

References

M. B. El Mashade, “Monopulse detection analysis of the trimmed mean CFAR processor in nonhomogeneous situations,” IEE Proc. - Radar, Sonar Navig., vol. 143, no. 2, p. 87, 1996, doi: https://doi.org/10.1049/ip-rsn:19960324.

J. Zhao, R. Jiang, X. Wang, H. Gao, “Robust CFAR detection for multiple targets in K-distributed sea clutter based on machine learning,” Symmetry, vol. 11, no. 12, p. 1482, 2019, doi: https://doi.org/10.3390/sym11121482.

J. R. Machado-Fernández, N. Mojena-Hernández, J. de la C. Bacallao-Vidal, “Evaluation of CFAR detectors performance,” Iteckne, vol. 14, no. 2, 2017, doi: https://doi.org/10.15332/iteckne.v14i2.1772.

M. B. El Mashade, “Analysis of adaptive detection of moderately fluctuating radar targets in target multiplicity environments,” J. Franklin Inst., vol. 348, no. 6, pp. 941–972, 2011, doi: https://doi.org/10.1016/j.jfranklin.2011.03.004.

L. Zhao, W. Liu, X. Wu, J. S. Fu, “A novel approach for CFAR processors design,” in Proceedings of the 2001 IEEE Radar Conference (Cat. No.01CH37200), 2001, pp. 284–288, doi: https://doi.org/10.1109/NRC.2001.922992.

M. Mashade, “Adaptive detection enhancement of partially-correlated χ2 targets in an environment of saturated interference,” Recent Adv. Electr. Electron. Eng. (Formerly Recent Patents Electr. Electron. Eng., vol. 9, no. 3, pp. 202–222, 2017, doi: https://doi.org/10.2174/2352096508666151030221552.

L. Wang, J. Tang, Q. Liao, “A study on radar target detection based on deep neural networks,” IEEE Sensors Lett., vol. 3, no. 3, pp. 1–4, 2019, doi: https://doi.org/10.1109/LSENS.2019.2896072.

M. B. El Mashade, “Performance analysis of OS structure of CFAR detectors in fluctuating target environments,” Prog. Electromagn. Res. C, vol. 2, pp. 127–158, 2008, doi: https://doi.org/10.2528/PIERC08022807.

M. Barkat, P. K. Varshney, “Decentralized CFAR signal detection,” IEEE Trans. Aerosp. Electron. Syst., vol. 25, no. 2, pp. 141–149, 1989, doi: https://doi.org/10.1109/7.18676.

A. R. Elias-Fusté, A. Broquetas-Ibars, J. P. Antequera, J. C. M. Yuste, “CFAR data fusion center with inhomogeneous receivers,” IEEE Trans. Aerosp. Electron. Syst., vol. 28, no. 1, pp. 276–285, 1992, doi: https://doi.org/10.1109/7.135453.

S. D. Himonas, M. Barkat, “A distributed CFAR processor with data fusion for correlated targets in homogeneous clutter,” in IEEE International Conference on Radar, 1990, pp. 501–506, doi: https://doi.org/10.1109/RADAR.1990.201096.

M. K. Uner, P. K. Varshney, “Distributed CFAR detection in homogeneous and nonhomogeneous backgrounds,” IEEE Trans. Aerosp. Electron. Syst., vol. 32, no. 1, pp. 84–97, 1996, doi: https://doi.org/10.1109/7.481251.

H. Amirmehrabi, R. Viswanathan, “A new distributed constant false alarm rate detector,” IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 1, pp. 85–97, 1997, doi: https://doi.org/10.1109/7.570711.

M. Maynul, M. Hossam-E-Haider, “Detection capability and CFAR loss under fluctuating targets of different Swerling model for various gamma parameters in radar,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 2, 2018, doi: https://doi.org/10.14569/IJACSA.2018.090214.

P. Liu, C. Han, M. Lei, Z. Sun, “Adaptive censored cell-averaging CFAR detection in distributed sensor networks,” in 2007 10th International Conference on Information Fusion, 2007, pp. 1–8, doi: https://doi.org/10.1109/ICIF.2007.4407990.

M. B. El Mashade, “Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ2-targets,” J. Syst. Eng. Electron., vol. 29, no. 1, pp. 1–17, 2018, doi: https://doi.org/10.21629/JSEE.2018.01.01.

M. B. El Mashade, “Performance predominance of a new strategy for CFAR processors over the N-P model in detecting four degrees of freedom χ2 fluctuating targets,” Radioelectron. Commun. Syst., vol. 61, no. 9, pp. 377–393, 2018, doi: https://doi.org/10.3103/S0735272718090017.

Z. Cao, J. Li, C. Song, Z. Xu, X. Wang, “A novel CFAR algorithm for multi-target detection with FMCW radar,” in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 1–6, doi: https://doi.org/10.1109/GLOBECOM42002.2020.9322140.

C.-H. Lin, Y.-C. Lin, Y. Bai, W.-H. Chung, T.-S. Lee, H. Huttunen, “DL-CFAR: a novel CFAR target detection method based on deep learning,” in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, pp. 1–6, doi: https://doi.org/10.1109/VTCFall.2019.8891420.

M. B. El Mashade, “Performance amelioration of standard variants of adaptive schemes operating in heterogeneous environment,” Radioelectron. Commun. Syst., vol. 63, no. 4, pp. 171–185, 2020, doi: https://doi.org/10.3103/S0735272720040019.

M. B. El_Mashade, “M-sweeps multi-target analysis of new category of adaptive schemes for detecting χ 2 -fluctuating targets,” J. Inf. Telecommun., vol. 4, no. 3, pp. 314–345, 2020, doi: https://doi.org/10.1080/24751839.2020.1783493.

Multi-pulse homogeneous detection performance of LF-CFAR scheme for [chi]^2-fluctuating targets with two-degrees of freedom

Published

2022-02-18

Issue

Section

Research Articles