نوع مقاله: کوتاه

نویسندگان

1 استادیار دانشکده جنگ الکترونیک و دفاع سایبری، دانشگاه جامع امام حسین (ع)

2 دانشجوی دکتری دانشکده جنگ الکترونیک و دفاع سایبری، دانشگاه جامع امام حسین (ع)

3 استادیار دانشکده مهندسی برق، دانشگاه علوم دریایی امام خمینی (ره)

چکیده

امروزه سلاح‌های هدایت‌شونده نسل جدید و سامانه‌های کنترل آن‌ها نقش بسیار مهمی در میدان نبرد بازی می‌کنند. برتری اطلاعاتی یکی از پارامترهای بسیار مهم در کنترل و هدایت سلاح‌های نسل جدید است. هسته اصلی دربرتری اطلاعاتی وابسته به توانمندی‌های حسگرها، فرماندهی، کنترل و سیستم سلاح‌ها است. دستیابی به این هدف یعنی تسلط همه‌جانبه در عملیات نظامی با ایجاد یک شبکه مرکزی بین پارامترهای بیان‌شده حاصل می‌شود. هدف از انجام این تحقیق حمله به ساختار این شبکه در نقاط مشخص‌شده به‌منظور ایجاد اختلال در سیستم کنترل سلاح آن با روش مبتنی بر سناریو‌نویسی است. برای این منظور ابتدا با استفاده از منابع مختلف و به‌روز توصیف گرها و متغیرهای مؤثر استخراج‌ گردید. در ادامه پانل خبرگان تشکیل و ماتریس متقاطع توصیف گرها امتیاز‌دهی شد. میانگین نمره‌ها به نرم‌افزار سناریو ویزارد اعمال شد و سبد سناریو‌ها استخراج گردید. درنهایت دو سناریوی خوش‌بینانه و دو سناریوی بدبینانه به‌عنوان محصول استخراج شد.

کلیدواژه‌ها

عنوان مقاله [English]

Jamming and deceptive scenarios for guidance and control systems for guided weapons of the new generation of the world on the horizon of 1410.

نویسندگان [English]

  • alireza sadeghi 1
  • fallah mohammadzadeh 2
  • Mohammad Khishe 3

1 عضو هیئت علمی

2 Imam Khomeini University of Maritime Sciences

3 Assistant Professor, Imam Khomeini University of Maritime Sciences

چکیده [English]

Nowadays, the new generation's guided weapons and their control systems play a very important role on the battlefield. Information excellence is one of the most important parameters in controlling and directing new-generation weapons. The core of the information system is based on the capabilities of sensors, command, control and weapons systems. Achieving this goal is an impressive dominance in military operations by creating a central network of state parameters. The purpose of this research is to attack the structure of the network at designated points in order to disrupt its weapon control system using a scenario-based approach. For this purpose, firstly, descriptors and effective variables were extracted using various and up-to-date sources. The panel of experts was formed and the cross-sectional matrix of descriptors was rated. The mean scores were applied to the scenario wizard software and a portfolio of scenarios was extracted. Finally, two optimistic scenarios and two pessimistic scenarios were extracted as a product.

کلیدواژه‌ها [English]

  • scenarios
  • Scenario Wizard
  • Fire control system
  • New Generation Guided Weapons

[1]                 Adamy, D. (2001). EW 101: A first course in electronic warfare (Vol. 101). Artech House.

[2]                 Adamy, D. (2004). EW 102: a second course in electronic warfare. Artech House.

[3]                 Adamy, D. (2008). EW 103: Tactical battlefield communications electronic warfare. Artech House.

[4]                 Adamy, D. L. (2015). EW 104: Electronic Warfare against a New Generation of Threats. Artech House.

[5]                 Schoemaker, P. J. (1995). Scenario planning: a tool for strategic thinking. Sloan management review, 36(2), 25.

[6]                 Chermack, T. J., & Lynham, S. A. (2002). Definitions and outcome variables of scenario planning. Human Resource Development Review, 1(3), 366-383.

[7]                 Khishe, M., Mosavi, M.R., & Moridi, A., (2018). Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation. Appl. Acoust. 137, 121-139.

[8]                 Chermack, T. J., & Lynham, S. A. (2002). Definitions and outcome variables of scenario planning. Human Resource Development Review, 1(3), 366-383.

[9]                 Mirjalili, S. A. (2016). Dragonfly Algorithm: a New Meta-heuristic Optimization Technique for Solving Single-objective, Discrete, and Multi-objective Problems. Neural Computing and Application, 27 (4), 1053-1073.

[10]             "Chronology of the USS Monitor: From Inception to Sinking", The Mariner's Museum, USS Monitor Center, Archived from the original on 2006-07-13, Retrieved 2006-08-26.

[11]             Mindell, David (2002), Between Human and Machine. Baltimore: Johns Hopkins, pp, 25–28, ISBN 0-8018-8057-2.

[12]             For a description of an Admiralty Fire Control Table in action: Cooper, Arthur, "A Glimpse at Naval Gunnery". Ahoy: Naval, Maritime, Australian History.

[13]             The degree of updating varied by country. For example, the US Navy used servomechanisms to automatically steer their guns in both azimuth and elevation. The Germans used servomechanisms to steer their guns only in elevation, and the British began to introduce Remote Power Control in elevation and deflection of 4-inch, 4.5-inch and 5.25-inch guns in 1942, according to Naval Weapons of WW2, by Campbell. For example HMS Anson's 5.25-inch guns had been upgraded to full RPC in time for her Pacific deployment.

[14]             B.R. 901/43, Handbook of the Admiralty Fire Control Clock Mark I and I*

[15]             The rangekeeper in this exercise maintained a firing solution that was accurate within a few hundred yards (or meters), which is within the range needed for an effective rocking salvo. The rocking salvo was used by the US Navy to get the final corrections needed to hit the target.

[16]             Han, H. G., W. Lu, Y. Hou, & J. F. Qiao. (2016). An Adaptive-PSO-based Self-organizing RBF Neural Network. IEEE Transaction on Neural Network Learning System, 99, 1-14.

[17]              Anthony P. Tully (2003), "Located/Surveyed Shipwrecks of the Imperial Japanese Navy". Mysteries/Untold Sagas of the Imperial Japanese Navy, CombinedFleet.com, Retrieved 2006-09-26.

[18]             Mindell, David (2002), Between Human and Machine. Baltimore: Johns Hopkins, pp. 262–263, ISBN 0-8018-8057-2.

[19]             Najafzadeh, M., Rezaie-Balf, & M., Tafarojnoruz, A., (2018). Prediction of riprap stone size under overtopping flow using data-driven models. Int. J. River Basin Manage. 16 (4), 505-512.

[20]             Najafzadeh, M., Saberi-Movahed, & F., Sarkamaryan, S., (2018). NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects. Mar. Georesour. Geotechnol. 36 (5), 589-602.

[21]             "FM 4-15 Coast Artillery Field Manual-Seacoast Artillery Fire Control and Position Finding," U.S. War Department, Government Printing Office, Washington, 1940.