RCS Reduction of Canonical Targets Using Genetic Algorithm Synthesized RAM Gagan H Y 1pi11lds04 2 nd sem (DE & CS) M.Tech Under guidance of Prof. V. Mahadevan Dept. T.E
RCS Reduction of Canonical Targets Using Genetic
Algorithm Synthesized RAM
Gagan H Y1pi11lds04
2nd sem (DE & CS)M.Tech
Under guidance ofProf. V. Mahadevan
Dept. T.E
Contents
• Abstract
• Introduction
• Genetic algorithm (GA)
• GA with Maxwell’s equation
• RAM optimization
• RCS reduction
• Pro s & cons
• Conclusion
Abstract
• Radar cross reduction of canonical structures is the focus
• A novel procedure to synthesize RAM which is then used in RCS reduction
• An optimization technique by name ‘Genetic Algorithm’ is used to optimize RAM.
• Modal solutions of Maxwell’s equations integrated with GA optimizers to obtain convincible reduction in RCS
Introduction
• RCS reduction an important constraint in radar systems
• This is for the reason that in complex structures like fighter plane’s location becomes visible to radars
• So the solution would be to use RAM that absorb wide band radar frequencies
Contd…
• The 1st step in the design is to optimize RAM
• Optimization is carried out by GA
• Optimized Ram is integrated with Vector wave solution of Maxwell’s equation
• Obtain reduction in RCS
Genetic algorithms
• Genetic algorithms (GA) are basically a search algorithms based on mechanics of natural selection and natural genetics.
• GA s are adaptive heuristic search algorithm based on evolutionary ideas of natural selection and genetics.
Background
• Developed by John Holland in 1975 at University of Michigan.
• Goals of research :
To abstract and rigorously explain adaptive process of natural systems.
To design artificial system software that retains important mechanism of natural systems.
• Central theme being ‘Robustness’.
Components of GA
1. Representation (defining individuals):
Initialize with all possible solution sets
Each solution represented by a chromosome; composed of string of genes
Usual representation {0’s & 1’s}
2. Fitness function/cost function
•The available solution of chromosome is checked for compatibility w.r.t to a function
•Chromosomes satisfying /accounting for nearest solution id chosen for further operation
3. Variation operators
•Accounts for creation of new individuals from old ones
•2 types:Mutation Crossover
Mutation operator:
This is a Genetic Operator, that alters one or more gene value in a chromosome from its initial state.
This results in essentially a new gene being added into the existing gene pool.
This operator prevents the population being static at same local optima.
Types :
Flip Bit Boundary Non-Uniform Uniform Gaussian
Cross over operator:
In this form two chromosome (=parents) are combined together to form new offspring.
Offspring produced could form better solution.
Types :
One Point. Two Point. Uniform.
3. Parent selection mechanism:
• The role of parent selection (mating selection) is to distinguish among individuals based on their quality
• This allows the better individuals to become parents of the next generation.
• Parent selection is probabilistic
4. Survivor selection mechanism:
• The role of survivor selection is to distinguish among individuals based on their quality
• This decision is based on their fitness values, favoring those with higher quality
7. Termination Condition :
Commonly used conditions for terminations are the following :
• The maximally allowed CPU time elapses• The total number of fitness evaluations reaches a given
limit• For a given period of time, the fitness improvement
remains under a threshold value• The population diversity drops under a given threshold.
Vector wave solution
1. Planar structure
• k = propagation constant for ith region
• A & B unknown coefficients found using boundary condition
2. Spherical structure
3. Spherical structure
RCS reduction using GA synthesized RAM
• The 1st step is to optimize a RAM
RAMIt is a class of material used to
disguise a structure from radar
Radar Cross Section
Types of RAM
1. Iron ball paint
It contains tiny spheres coated with carbonyl iron or ferrite
Radar waves induce molecular oscillations from the alternating magnetic field in this paint, which leads to conversion of the radar energy into heat
The heat is then transferred to the aircraft and dissipated
2. Foam absorber
This material typically consists of a fireproofed urethane foam loaded with carbon black, and cut into long pyramids
The length from base to tip of the pyramid structure is chosen based on the lowest expected frequency and the amount of absorption required
The pyramid shapes are cut at angles that maximize the number of bounces a wave makes within the structure
With each bounce, the wave loses energy to the foam material and thus exits with lower signal strength
3. Jaumann absorber
It is actually a device which control 2 equally spaced reflection surfaces and a ocnducting ground plane
RAM optimization
Optimization by GA
• Fitness function
GA parameters
• Fitness function
• Mutation rate = 0.01
• Crossover rate = 0.7
• Selection process = tournament type
• Initial population = 100
Optimized RAM in RCS reduction
Comparison between coated and uncoated
RCS reduction in monostatic and bistatic radars
• Monostatic
• Bistatic
Advantages
• RCS reduction plays an important role in disguising targets
• RAM = low design cost
• Best optimization obtained
• Computation cost is also less
Disadvantages
• Layers of RAM can cost for increased weight of target
• RCS reduction computation involves complex calculations
Applications
• Majorly in stealth technology
• GA tool finds application in almost all optimization processes
Future work
• Applied for monostatic and bistatic radars
• Optimization with reduced number of layers
• Wider frequency range
Conclusion
The modal solution/GA technique is applied to
determine the optimal composite coatings for RCS reduction of canonical targets in a GA with planar/curved surface implementation is also introduced to efficiently reduce the RCS of the curved structures.
References
• E. Michielssen, J. M. Sajer, S. Ranjithan, and R. Mittra, “Design of
lightweight, broad-band microwave absorbers using genetic algorithms,”
•
• IEEE Trans. Microwave Theory Tech., vol. 41, pp. 1024–1031,
June/July 1993.
• D. S. Weile, E. Michielssen, and D. E. Goldberg, “Genetic algorithm
design of pareto optimal broad-band microwave absorbers,” IEEE Trans.
• Electromagnetic Compatibility, vol. 38, pp. 518–524, Aug. 1996.
• B. Chambers and A. Tennant, “Optimized design of Gaumann radar
absorbing materials using a genetic algorithm,” Proc. Inst. Elect. Eng.
• Radar, Sonar, Navigat., vol. 143, no. 1, pp. 23–30, Feb. 1996.
• Y. Rahmat-Samii and E. Michielssen, Electromagnetic Optimization by
Genetic Algorithms. New York: Wiley, 1999.