A novel method based on comparison using threshold scale for CFAR detectors under environments with conditions of electromagnetic interference
A novel method based on comparison using threshold scale for CFAR detectors under environments with conditions of electromagnetic interference
Detection of a noisy signal is a complex process. Many radar systems are working in an environment wherethe signal processing parts cannot overcome the effects of interference sources due to their high power. These sources ofconflict may completely erode the signal or may make a mistake in deciding. It may make the return of the echoes of thegoals difficult. To solve this problem, the detector processor can use a new algorithm to estimate noise power and thencan set the threshold in different positions of the cell under test. The proposed algorithm, by differentiating betweenhomogeneous and interference environments in a multitarget structure, selects a set of reference cells that surroundthe cell under test to estimate the unknown noise/clutter and determine the effective threshold. Then, to evaluate theperformance of cell averaging of constant false alarm rate (CA-CFAR), censored mean level detector CFAR (CMLDCFAR), and excision CFAR (EX-CFAR) detectors, we compared threshold, false alarm, and detection probability in termsof different correlation coefficients. The values were obtained using simulation by MATLAB software. The simulationresults show that the excision parameter, by adding to the window of the reference cells that surround the cell undertest, reduces the effects of background noise on the received signal. We conclude from the proposed method that thehybrid detector not only has higher quality detection interactions in heterogeneous environments but also has relativelyless computational complexity than CA-CFAR, CMLD-CFAR, and EX-CFAR detectors.
___
- [1] Abbadi A, Abbane A, Bencheikh M, Soltani F. A new adaptive CFAR processor in multiple target situations. In:
Seminar on Detection Systems Architectures and Technologies; Algiers, Algeria; 2017. pp. 1-5.
- [2] Farrouki A, Barkat M. Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous
environments. IEEE Proceedings on Radar, Sonar and Navigation 2005; 152: 43–51.
- [3] Verma A. Variability index constant false alarm rate (VI-CFAR) for sonar target detection. In: International
Conference on Signal Processing, Communications and Networking; Chennai, India; 2008. pp. 138–141.
- [4] Zaimbashi A. An adaptive cell averaging-based CFAR detector for interfering targets and clutter-edge situations.
Digital Signal Processing 2014; 31: 59–68.
- [5] Zaimbashi A, Norouzi Y. Automatic dual censoring cell-averaging CFAR detector in non-homogenous environments.
Signal Processing 2008; 88: 2611–2621.
- [6] Wu F, Wu N, Wu M. A fast and slow time combined CFAR detection algorithm used in through-the-wall radar.
In: IEEE Electrical Design of Advanced Packaging and Systems Symposium; Haining, China; 2014. pp. 1-3.
- [7] Trunk GV. Range resolution of targets using automatic detectors. IEEE Transactions on Aerospace and Electronic
Systems 1978; 14 (5): 750–755.
- [8] Goldman H, Bar-David I. Analysis and application of the excision CFAR detector. IEEE Proceedings on Communications, Radar and Signal Processing 1988; 135: 563–575.
- [9] Rohling H. Radar CFAR thresholding in clutter and multiple target situations. IEEE Transactions on Aerospace
and Electronic Systems 1983; 19 (4): 608–621.
- [10] Song H, Lu S, Yi W, Kong L. CFAR detector based on clutter partition in heterogeneous background. In: IEEE
China Summit and International Conference on Signal and Information Processing; Chengdu, China; 2015. pp.
288-291.
- [11] Ritcey JA. Performance analysis of the censored mean-level detector. IEEE Transactions on Aerospace and Electronic Systems 1986; 22 (4): 443–454.
- [12] Lehtomaki JJ, Juntti M. Analysis of energy based signal detection. University of Oulu 2005; 68 (1): 1-102.
- [13] Jiang W, Huang Y, Yang J. Automatic censoring CFAR detector based on ordered data difference for low-flying
helicopter safety. Sensors 2016; 16: 1-21.
- [14] Yong HK, Sungshin K, Hye-Young H, Bok-Haeng H, Cheol-Hwan Y. Real-time detection and filtering of chaff clutter
from single-polarization Doppler Radar Data. Journal of Atmosphere Ocean Technology 2013; 30 (5): 873–895.
- [15] Luo J, Han Y, Fan L. Underwater acoustic target tracking: a review. Sensors 2018; 18 (1): 1-37.
- [16] Lops M. Hybrid clutter-map/L-CFAR procedure for clutter rejection in nonhomogeneous environment. IEEE
Proceedings on Radar, Sonar and Navigation 1996; 143: 239–245.
- [17] Xiangwei M, Jian G, You H. A generalized smallest of selection CFAR algorithm [radar signal processing]. In:
Proceedings of the International Conference on Radar; Adelaide, Australia; 2003. pp. 130-132.
- [18] Xiangwei M, You H. Quasi best weighted order statistics CFAR detector. In: International Conference on Signal
Processing; Beijing, China; 1998. pp. 145-147.
- [19] Messali Z, Soltani F, Sahmoudi M. Robust radar detection of CA, GO and SO CFAR in Pearson measurements
based on a non linear compression procedure for clutter reduction. Signal, Image and Video Processing 2008; 2:
169-176.
- [20] Nouar N, Farrouki A. CFAR detection of spatially distributed targets in k-distributed clutter with unknown
parameters. In: 22nd European Signal Processing Conference; Lisbon, Portugal; 2014. pp. 1731–1735.
- [21] Pace E, Taylor L. False alarm analysis of the envelope detection GO-CFAR processor. IEEE Transactions on
Aerospace and Electronic Systems 1994; 30 (3): 848–864.
- [22] Srinivasan R. Robust radar detection using ensemble CFAR processing. IEEE Proceedings on Radar, Sonar and Navigation 2000; 147 (6): 291–297.
- [23] Srinivasan R. Fast simulation of smallest-of and geometric-mean CFAR detectors. IEEE Proceedings on Radar,
Sonar and Navigation 2001; 148 (3): 186–191.
- [24] Zhang R, Sheng W, Xiaofeng M. Improved switching CFAR detector for non-homogeneous environments. Signal
Processing 2013; 93 (1): 35–48.
- [25] Richards MA, Scheer JA, Holm WA. Principles of Modern Radar: Basic Principles. Raleigh, NC, USA: SciTech
Publishing, 2010.
- [26] Himonas DS. Adaptive censored greatest-of CFAR detection. IEEE Proceedings on Radar and Signal Processing
1992; 139 (3): 247–255.
- [27] Himonas DS, Barkat M. Automatic censored CFAR detection for nonhomogeneous environments. IEEE Transactions
on Aerospace and Electronic Systems 1992; 28 (1): 286–304.
- [28] Yan S, Hao C, Hou C. Performance analysis of two modified censored mean-level CFAR detectors in Pearson
distributed reverberation. In: 4th International Conference on Systems and Informatics; Hangzhou, China; 2017.
pp. 1114–1119.
- [29] Miftahushudur T, Kurniawan D, Putra AR. Summed area table for optimizing of processing time on CA CFAR
algorithm. In: International Conference on Radar, Antenna, Microwave, Electronics and Telecommunications;
Bandung, Indonesia; 2015. pp. 109–113.
- [30] Aalo V, Peppas K, Efthymoglou G. Performance of CA-CFAR detectors in nonhomogeneous positive alpha-stable
clutter. IEEE Transactions on Aerospace and Electronic Systems 2015; 51 (3): 2027–2038.
- [31] Han YI, Kim T. Performance of excision GO-CFAR detectors in nonhomogeneous environments. IEE Proceedings
on Radar, Sonar and Navigation 1996; 143: 105–112.
- [32] Li Y, Wu L, Zhang N. A CFAR detector based on a robust combined method with spatial information and sparsity
regularization in non-homogeneous Weibull clutter. IEEE Access 2018; 6: 16279–16293.
- [33] Zhang Y, Gao M, Li Y. Performance analysis of typical mean-level CFAR detectors in the interfering target
background. In: 9th IEEE Conference on Industrial Electronics and Applications; Hangzhou, China; 2014. pp.
1045–1048.
- [34] Ling-Zhi Y, Wei-Ping L, Zhi-Rong L, Gen-Ping W. Study on consistent CFAR detection of a linear signal with
unknown parameters. In: Fifth World Congress on Intelligent Control and Automation; Hangzhou, China; 2004.
pp. 3886–3890.
- [35] Likai Z, Sen L, Guangzhao H. An improved VI-CFAR detector based on GOS. In: Seventh International Conference
on Mechatronics and Manufacturing; Singapore; 2016. pp. 1-5.