A new method for detecting jittered PRI in histogram-based methods

A new method for detecting jittered PRI in histogram-based methods

Histogram-based methods are among the best methods for deinterleaving, because of their easy implementation. However, they have a basic defect when they encounter a jittered pulse repetition interval (PRI). Jittered PRI is one of the most sophisticated patterns for electronic warfare (EW) receivers. In jittered PRI, the time among successive pulses varies in a totally random manner; thus its detection is very complicated. In this paper we present a new method for extracting jittered PRI from histogram-based methods. Simulation results demonstrate excellent performance of the proposed method in normal as well as hard circumstances where a higher missing pulse rate occurs or even when several targets with PRI of type jitter exist.

___

  • Gade S, Herlufsen H. Use of weighting functions in DFT/FFT analysis (part I). Br¨uel & Kjær Technical Review 1987; 3: 1-28.
  • Lin S, Thompson M, Davezac S, Sciortino JC. Comparison of time of arrival vs. multiple parameter based radar pulse train deinterleavers. J Signal Process, Sens Fusion, Target Recog 2006; 6235: 250-264.
  • Li H, Chen B, Han J, Dong W. A new method for sorting radiating-source. In: International Conference on Networks Security, Wireless Communications and Trusted Computing; 25–26 April 2009; Wuhan, China. New York, NY, USA:IEEE. pp. 817-819.
  • He A, Zeng D, Wang J, Tang B. Multi-parameter signal sorting algorithm based on dynamic distance clustering. J Elec Sci & Tech 2009; 7: 249-253.
  • Guo Q, Qu Z, Wang C. Pulse-to-pulse periodic signal sorting features and feature extraction in radar emitter pulse sequences. J Syst Eng Elec 2010; 21: 382-389.
  • Ray PS. A novel pulse TOA analysis technique for radar identification. IEEE T Aero Elec Syst 1998; 34: 716-721.
  • Fan F, Yin X. Improved method for deinterleaving radar pulse trains with stagger PRI from dense pulse series. In: 2nd International Conference on Signal Processing Systems; 5–7 July 2010; Dalian, China. New York, NY, USA: IEEE. pp. 250-253.
  • Logothetis A, Krishnamurthy V. An interval amplitude algorithm for deinterleaving stochastic pulse train sources. IEEE T Signal Process 1998; 46: 1344-1350.
  • Conroy TL, Moore JB. The limits of extended Kalman filtering for pulse train deinterleaving. IEEE T Signal Process 1998; 46: 3326-3332.
  • Noone G. Radar pulse train parameter and tracking using neural networks. In: International Conference on Artificial Neural Networks and Expert Systems; 20–23 November 1995; Dunedin, New Zealand. New York, NY, USA: IEEE. pp. 95-98.
  • Ching SS, Chin TL. A vector neural network for emitter identification. IEEE T Antenn Propag 2002; 50: 1120-1127.
  • Kuang Y, Shi Q, Chen Q, Yun L, Long K. A simple way to deinterleave repetitive pulse sequences. In: 7th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering; 27–29 October 2005; Sofia, Bulgaria: pp. 218-222.
  • Liu J, Meng H, Liu Y, Wang X. Deinterleaving pulse trains in unconventional circumstances using multiple hypothesis tracking algorithm. J Signal Process 2010; 90: 2581-2593.
  • Driscoll DE, Howard SD. The detection radar pulse sequences by means of a continuous wavelet transform. In: IEEE International Conference on Acoustics, Speech and Signal Processing; 15–19 March 1999; Adelaide, South Australia. New York, NY, USA: IEEE. pp. 1389-1392.
  • Rogers JAV. ESM processor system for high pulse density radar environments. P IEE Part F 1985; 132: 621-624.
  • Richard G. Wiley. ELINT, the Interception and Analysis of Radar Signals. Boston, MA, USA: Artech House, 2006.
  • Nishiguchi K, Kobayashi M. Improved algorithm for estimating pulse repetition intervals. IEEE T Aero Elec Syst 2000; 36: 407-421.
  • Milojevic DJ, Popovic BM. Improved algorithm for the deinterleaving of radar pulses. P IEEE Part F 1992; 139:98-104.
  • Mardia HK. New techniques for deinterleaving repetition sequences. P IEEE Part F 1989; 136: 149-154.
  • Nishiguchi K. Time-period analysis for pulse train deinterleaving. T SICE 2005; 4: 68-78.
  • Conroy TL, Moore JB. On the estimation of interleaved pulse train phases. IEEE T Signal Process 2000; 48:3420-3425.
  • Perkins J, Coat I. Pulse train deinterleaving via the Hough transform. In: IEEE International Conference on Acoustics, Speech and Signal Processing; 19–22 April 1994; Adelaide, South Australia. New York, NY, USA: IEEE.pp. 1-4.
  • Orsi RJ, Moore JB, Mahony RE. Spectrum estimation of interleaved pulse trains. IEEE T Signal Process 1999; 47:1646-1653.
  • Moore JB, Krishnamurthy V. Deinterleaving pulse trains using discrete-time stochastic dynamic-linear models.IEEE T Signal Process 1994; 42: 3092-3103.
  • Gen¸col K, At N, Kara A. A wavelet-based feature set for recognizing pulse repetition interval modulation patterns.Turk J Elec Eng & Comp Sci 2016; 24: 3078-3090.
  • Bagheri M, Sedaaghi MH. A new approach to pulse deinterleaving based on adaptive thresholding. Turk J Elec Eng & Comp Sci 2017; 25: 3827-3838.
  • Tavora R, Zubelli JP, Mattoso MA, Pinto EL. An algorithm for deinterleaving pulse trains using the fast wavelet packet transform. In: Brazilian Symposium of Telecommunications; 25–27 January 1997; Recife, Brazil, pp. 1-14.
  • Schleher D. Introduction to Electronic Warfare. Norwood, MA, USA: Artech House, 1986.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
Sayıdaki Diğer Makaleler

Probabilistic dynamic security assessment of large power systems using machine learning algorithms

Veysel Murat Istemihan GENC, Sevda JAFARZADEH

Adaptive collaborative speed control of PMDC motor using hyperbolic secant functions and particle swarm optimization

Khalid MAHMOOD-UL-HASAN, Omer SALEEM

A comparative study on different types of metamaterials for enhancement of microstrip patch antenna directivity at the Ku-band (12 GHz)

Bülent URUL, Bilal TÜTÜNCÜ, Hamid TORPİ

Improvement of air pollution prediction in a smart city and its correlation with weather conditions using metrological big data

Ali Reza HONARVAR, Talat ZAREE

Bagged tree classification of arrhythmia using wavelets for denoising, compression, and feature extraction

Özgür TOMAK, Temel KAYIKÇIOĞLU

Ozyeğin Biopsy Robot: System integration architecture and motion compensation of a moving target

Özkan BEBEK, Awais AHMAD

An integrated approach for the development of an electric vehicle powertrain: design, analysis, and implementation

Özgür ÜSTÜN, Ömer Cihan KIVANÇ, Murat ÇAKAN, Ramazan Nejat TUNCAY, Can GÖKÇE, Mert Safa MÖKÜKCÜ, Gürkan TOSUN

A new method for detecting jittered PRI in histogram-based methods

Mohammad Hossein SEDAAGHI, Mostafa BAGHERI

Forecasting the Baltic Dry Index by using an artificial neural network approach

Bedir ÜNVER, Samet GÜRGEN, Bekir ŞAHİN, İsmail ALTIN

Hardware Trojan detection and localization based on local detectors

Amin BAZZAZI, Mohammad Taghi MANZURI SHALMANI, Ali Mohammad Afshin HEMMATYAR