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Conventional Hough Detector in Presence of Randomly Arriving Impulse Interference

This paper investigates the performance of a conventional Hough detector in the presence of randomly arriving impulse interference. It also explores threshold analysis for better detection results at low signal-to-noise ratios (SNR).

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Conventional Hough Detector in Presence of Randomly Arriving Impulse Interference

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  1. Conventional Hough Detector in Presence of Randomly Arriving Impulse Interference LyubkaDoukovska Institute of Information and Communication TechnologiesBulgarian Academy of Sciences AComIn: Advanced Computing for Innovation

  2. References Rohling H., “Radar CFAR Thresholding in Clutter and Multiple Target Situations”, IEEE Trans., vol. AES-19, № 4, 1983, pp. 608-621. Carlson B., E. Evans, S. Wilson, “Search Radar Detection and Track with the Hough Transform”, Parts I, II, III, IEEE Trans., vol. AES, 1994, pp. 102-124. Doukovska L., “Detection Censoring Techniques for Hough Radar Detector Analysis”, Comptes rendus de l’Academie bulgare des Sciences, vol. 63, №8, ISSN 0861-1459, 2010, pp. 1201-1210. Doukovska L., “Adaptive Approach to Hough Radar Detector Analysis”, Comptes rendus de l’Academie bulgare des Sciences, vol. 63, №11, ISSN 0861-1459, 2010, pp. 1643-1650. Lukin K., V. Kudriashov, P. Vyplavin, V. Palamarchuk, “Coherent Imaging in the Range-Azimuth Plane Using a Bistatic Radiometer Based on Antennas with Beam Synthesizing”, IEEE Aerospace and Electronic Systems Magazine, vol. 29, №7, 2014, pp. 16-22. AComIn: Advanced Computing for Innovation

  3. Introduction Signal model The probability density function (pdf) of the reference window outputs with Poisson distribution law: where λ0 is the average power of the receiver noise, rj/λ0 is the average interference-to-noise ratio (INR) and s is average signal-to-noise ratio (SNR).

  4. Introduction Signal model The probability density function (pdf) of the reference window outputs with binominal distribution of pulse interference: where is the probability for the appearance of pulse jamming with average length in the range cells,rj/λ0 is the average interference-to-noise ratio (INR) and s is average signal-to-noise ratio (SNR). AComIn: Advanced Computing for Innovation

  5. CFAR detector CFAR Processor Analysis AComIn: Advanced Computing for Innovation

  6. CFAR detector CFAR Processor Analysis AComIn: Advanced Computing for Innovation

  7. CFAR detector CA CFAR Processor Analysis AComIn: Advanced Computing for Innovation

  8. CFAR detector CFAR BI Processor Analysis AComIn: Advanced Computing for Innovation

  9. CFAR detector CFAR Processor Analysis The target is detected according to the following algorithm: where H1 is the hypothesis that the test resolution cell contains the echoes from the target and H0 is the hypothesis that the test resolution cell contains the randomly arriving impulse interference only. The parameter V is the noise level estimation. The constant Ta is a scale coefficient, which is determined in order to maintain a given constant false alarm rate (CFAR). AComIn: Advanced Computing for Innovation

  10. CFAR detector Statistical analysis of CFAR detector Probability of false alarm: Probability of detection: AComIn: Advanced Computing for Innovation

  11. Hough transform Hough transform for detection AComIn: Advanced Computing for Innovation

  12. Hough detector Hough detector structure AComIn: Advanced Computing for Innovation

  13. Hough detector Statistical analysis of Hough detector The probability of detection PD can by calculated by Brunner’s method: The cumulative probability of target detection in Hough parameter space: AComIn: Advanced Computing for Innovation

  14. Experimental and Numerical Results Antenna with beam synthesizing It is a part of the equipment for acoustic holography and beamforming techniques for noise source identification belonging to the Smart Lab at IICT. A complete PULSE-based system consists of the following main components: 20 kHz precision array microphone; Module frame; Multi-purpose 6-channel input module; High-density 12-channel input module; Piston phone; A complete 18-channel combination array with a 35 cm diameter; Dell Latitude E6430; PULSE Refined Beamforming Calculations; Time Data Recording; FFT Analysis and Battery module.

  15. Experimental and Numerical Results Comparison between non-coherent and binary integration AComIn: Advanced Computing for Innovation

  16. Experimental and Numerical Results Comparison between non-coherent and binary integration AComIn: Advanced Computing for Innovation

  17. Experimental and Numerical Results Simulation results AComIn: Advanced Computing for Innovation

  18. Experimental and Numerical Results Simulation results AComIn: Advanced Computing for Innovation

  19. Conclusion • The presented paper considers the results obtained by the proposed conventional Hough detector in conditions of intensive randomly arriving impulse interference. • The need of an adequate threshold analysis procedure allowing better detection results for low values of the SNR is considered. • Results show that applying binary integration in the Hough parameter space allows for equal detection quality of smaller and bigger targets. AComIn: Advanced Computing for Innovation

  20. Conclusion • For environment with presents of clatter and impulse interference with probability of appearance e0=0.1, the best performance has Hough detector with API CFAR processor, with values of binary rule in the Hough parameter space - TM/NS=7/20. • The obtained results can be successfully applied for radar target detection and in the existing communication • network receivers that use pulse signals. AComIn: Advanced Computing for Innovation

  21. Acknowledgment The research work reported in the paper is supported by the project AComIn - “Advanced Computing for Innovation”, grant 316087, funded by the FP7 Capacity Programme (Research Potential of Convergence Regions). AComIn: Advanced Computing for Innovation

  22. Thank youfor your attention! AComIn: Advanced Computing for Innovation

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