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An Automatic Digital Camouflage Pattern Generation Method based on Texture Structure

Lei Ding, Chengjun Xu, Fangzi Cheng, Mingkun Guo
Frontiers in Science and Engineering, (2023), Vol.3, No.6, pp.61-73
Published: June 20, 2023
DOI: 10.54691/fse.v3i6.5133
PDF: Download Full Text PDF
Abstract

This article investigates the problem of scattered burst signal detection based on multiple sensors to obtain overall decisions. In the explosion detection system studied in the article, sensors independently transmit their decisions on measuring explosion information to the data fusion processing terminal, which provides overall decisions based on fusion rules. The researchers focus on the data fusion theory of the distributed parallel detection burst point data fusion system based on the Bayesian rule. This paper has obtained the data fusion rule and sensor decision criteria that make the overall system optimal, and proposed a nonlinear Gauss Seidel mathematical variable algorithm that optimizes the data fusion rule and multi-sensor decision criteria The data fusion problem when detecting burst point signals with two different and three identical types of sensors. The data fusion algorithm proposed in this article is validated and simulated through computer experiments on the detection of three types of sensors. The relevant experimental data show that the performance of a data fusion system based on Bayesian detection is significantly improved compared with the sensor acquisition of burst point information. In the experiment, the risk of Bayesian missing detection of burst point signal coefficient of the data fusion system using three sensors with the same performance is reduced by 32.7%.

Keywords: Data Fusion; Bayesian Rule; Scattered Detection; Computer Simulation.
APA Citation: Lei Ding, Chengjun Xu, Fangzi Cheng, Mingkun Guo (2023). An Automatic Digital Camouflage Pattern Generation Method based on Texture Structure. Frontiers in Science and Engineering, 3(6), 61-73. https://doi.org/10.54691/fse.v3i6.5133

References

  1. J N Tsitsiklis, Decentralized detection. in Advances in StatisticalSignal Processing, VOL. 2 -Signal Detection, H. V. Poor and J. B. Thomas, Eds. Greenwich, CT:JAI Press, vol.19, pp. 456-123,March 2021.
  2. R R Tenney, N R Sandell. Detection With Distributed Sensors. IEEE Transactions on Aerospace and Electronic Systems, AES - vol.17, pp.501 -510,April 2022.
  3. V V S Sarma and Rao K A Gopala, Decentralized Detection and Estimationin Distributed Sensor Systems. In Proceedings of the IEEE1983 Cybernetics and Society Confenrence, vol. 16, pp.438 -441,July 2022.
  4. G S Lauer and S N R Jr andell. Di stributed Detection with WaveformObservations :Correlated Observation Processes. In Proceedings of the 1982 American Controls Conference, vol. 25 , pp.812-819,February 2022.
  5. D Teneketzis. The Decentralized Quickest Detection Problem. InProceedings of the 21st IEEE Conference of Decision andControls, FortLauderdale, vol. 52, pp.673-679,March 2022.
  6. F A Sadjadi. Hypotheses testing in a Distribut ed Envrinment s. IEEE Transactions on Aerospace and Electroni c Systems, March vol. 22, pp.134 -137,January 2021.
  7. Z Chai r and P K Varshney. Optimal Data Fusion in Multiple SensorDetection Systems. IEEE Trans. on Aerospace and Electronic Systems, January, vol. 22 pp.98-101,January 2022.
  8. L K Ekchian and R R Tenny. Detection Networks. In Proceedingsof the 21st IEEE Conference of Decision and Controls, Fort Lauderdale, FL. , vol.39, pp.683-691,June 2021.
  9. Z Chair and P k Varshney. Neyman -Pearson Hypothesis Testing inDi stributed Networks. Proc. 26th IEEE Trans. on Decision andControl, vol.27,pp.1842 -1843,March 2021.
  10. Li Hong. Theory, Algorithm and Application of Multi-sensor Data fusion. Xi 'an: Ph. D. Dissertation of Northwestern Polytechnical University, vol.11,pp.18-22, January 2020.
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