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.